1
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Drakoulas G, Gortsas T, Polyzos E, Tsinopoulos S, Pyl L, Polyzos D. An explainable machine learning-based probabilistic framework for the design of scaffolds in bone tissue engineering. Biomech Model Mechanobiol 2024; 23:987-1012. [PMID: 38416219 DOI: 10.1007/s10237-024-01817-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 01/01/2024] [Indexed: 02/29/2024]
Abstract
Recently, 3D-printed biodegradable scaffolds have shown great potential for bone repair in critical-size fractures. The differentiation of the cells on a scaffold is impacted among other factors by the surface deformation of the scaffold due to mechanical loading and the wall shear stresses imposed by the interstitial fluid flow. These factors are in turn significantly affected by the material properties, the geometry of the scaffold, as well as the loading and flow conditions. In this work, a numerical framework is proposed to study the influence of these factors on the expected osteochondral cell differentiation. The considered scaffold is rectangular with a 0/90 lay-down pattern and a four-layered strut made of polylactic acid with a 5% steel particle content. The distribution of the different types of cells on the scaffold surface is estimated through a scalar stimulus, calculated by using a mechanobioregulatory model. To reduce the simulation time for the computation of the stimulus, a probabilistic machine learning (ML)-based reduced-order model (ROM) is proposed. Then, a sensitivity analysis is performed using the Shapley additive explanations to examine the contribution of the various parameters to the framework stimulus predictions. In a final step, a multiobjective optimization procedure is implemented using genetic algorithms and the ROM, aiming to identify the material parameters and loading conditions that maximize the percentage of surface area populated by bone cells while minimizing the area corresponding to the other types of cells and the resorption condition. The results of the performed analysis highlight the potential of using ROMs for the scaffold design, by dramatically reducing the simulation time while enabling the efficient implementation of sensitivity analysis and optimization procedures.
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Affiliation(s)
- George Drakoulas
- Department of Mechanical Engineering and Aeronautics, University of Patras, 26504, Rio, Greece.
| | - Theodore Gortsas
- Department of Mechanical Engineering and Aeronautics, University of Patras, 26504, Rio, Greece.
- Department of Mechanical Engineering, University of Peloponnese, 26334, Patras, Greece.
| | - Efstratios Polyzos
- Department of Mechanics of Materials and Constructions, Vrije Universiteit Brussel (VUB), 1050, Brussels, Belgium
| | - Stephanos Tsinopoulos
- Department of Mechanical Engineering, University of Peloponnese, 26334, Patras, Greece
| | - Lincy Pyl
- Department of Mechanics of Materials and Constructions, Vrije Universiteit Brussel (VUB), 1050, Brussels, Belgium
| | - Demosthenes Polyzos
- Department of Mechanical Engineering and Aeronautics, University of Patras, 26504, Rio, Greece
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2
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Shakiba M, Akimov AV. Machine-Learned Kohn-Sham Hamiltonian Mapping for Nonadiabatic Molecular Dynamics. J Chem Theory Comput 2024; 20:2992-3007. [PMID: 38581699 DOI: 10.1021/acs.jctc.4c00008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2024]
Abstract
In this work, we report a simple, efficient, and scalable machine-learning (ML) approach for mapping non-self-consistent Kohn-Sham Hamiltonians constructed with one kind of density functional to the nearly self-consistent Hamiltonians constructed with another kind of density functional. This approach is designed as a fast surrogate Hamiltonian calculator for use in long nonadiabatic dynamics simulations of large atomistic systems. In this approach, the input and output features are Hamiltonian matrices computed from different levels of theory. We demonstrate that the developed ML-based Hamiltonian mapping method (1) speeds up the calculations by several orders of magnitude, (2) is conceptually simpler than alternative ML approaches, (3) is applicable to different systems and sizes and can be used for mapping Hamiltonians constructed with arbitrary density functionals, (4) requires a modest training data, learns fast, and generates molecular orbitals and their energies with the accuracy nearly matching that of conventional calculations, and (5) when applied to nonadiabatic dynamics simulation of excitation energy relaxation in large systems yields the corresponding time scales within the margin of error of the conventional calculations. Using this approach, we explore the excitation energy relaxation in C60 fullerene and Si75H64 quantum dot structures and derive qualitative and quantitative insights into dynamics in these systems.
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Affiliation(s)
- Mohammad Shakiba
- Department of Chemistry, University at Buffalo, The State University of New York, Buffalo, New York 14260, United States
| | - Alexey V Akimov
- Department of Chemistry, University at Buffalo, The State University of New York, Buffalo, New York 14260, United States
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3
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Célerse F, Wodrich MD, Vela S, Gallarati S, Fabregat R, Juraskova V, Corminboeuf C. From Organic Fragments to Photoswitchable Catalysts: The OFF-ON Structural Repository for Transferable Kernel-Based Potentials. J Chem Inf Model 2024; 64:1201-1212. [PMID: 38319296 PMCID: PMC10900300 DOI: 10.1021/acs.jcim.3c01953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/18/2024] [Accepted: 01/22/2024] [Indexed: 02/07/2024]
Abstract
Structurally and conformationally diverse databases are needed to train accurate neural networks or kernel-based potentials capable of exploring the complex free energy landscape of flexible functional organic molecules. Curating such databases for species beyond "simple" drug-like compounds or molecules composed of well-defined building blocks (e.g., peptides) is challenging as it requires thorough chemical space mapping and evaluation of both chemical and conformational diversities. Here, we introduce the OFF-ON (organic fragments from organocatalysts that are non-modular) database, a repository of 7869 equilibrium and 67,457 nonequilibrium geometries of organic compounds and dimers aimed at describing conformationally flexible functional organic molecules, with an emphasis on photoswitchable organocatalysts. The relevance of this database is then demonstrated by training a local kernel regression model on a low-cost semiempirical baseline and comparing it with a PBE0-D3 reference for several known catalysts, notably the free energy surfaces of exemplary photoswitchable organocatalysts. Our results demonstrate that the OFF-ON data set offers reliable predictions for simulating the conformational behavior of virtually any (photoswitchable) organocatalyst or organic compound composed of H, C, N, O, F, and S atoms, thereby opening a computationally feasible route to explore complex free energy surfaces in order to rationalize and predict catalytic behavior.
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Affiliation(s)
- Frédéric Célerse
- Laboratory
for Computational Molecular Design (LCMD), Institute of Chemical Sciences
and Engineering, Ecole Polytechnique Fédérale
de Lausanne (EPFL), Lausanne 1015, Switzerland
| | - Matthew D. Wodrich
- Laboratory
for Computational Molecular Design (LCMD), Institute of Chemical Sciences
and Engineering, Ecole Polytechnique Fédérale
de Lausanne (EPFL), Lausanne 1015, Switzerland
- National
Center for Competence in Research-Catalysis (NCCR-Catalysis), Ecole Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
| | - Sergi Vela
- Laboratory
for Computational Molecular Design (LCMD), Institute of Chemical Sciences
and Engineering, Ecole Polytechnique Fédérale
de Lausanne (EPFL), Lausanne 1015, Switzerland
| | - Simone Gallarati
- Laboratory
for Computational Molecular Design (LCMD), Institute of Chemical Sciences
and Engineering, Ecole Polytechnique Fédérale
de Lausanne (EPFL), Lausanne 1015, Switzerland
| | - Raimon Fabregat
- Laboratory
for Computational Molecular Design (LCMD), Institute of Chemical Sciences
and Engineering, Ecole Polytechnique Fédérale
de Lausanne (EPFL), Lausanne 1015, Switzerland
| | - Veronika Juraskova
- Laboratory
for Computational Molecular Design (LCMD), Institute of Chemical Sciences
and Engineering, Ecole Polytechnique Fédérale
de Lausanne (EPFL), Lausanne 1015, Switzerland
| | - Clémence Corminboeuf
- Laboratory
for Computational Molecular Design (LCMD), Institute of Chemical Sciences
and Engineering, Ecole Polytechnique Fédérale
de Lausanne (EPFL), Lausanne 1015, Switzerland
- National
Center for Competence in Research-Catalysis (NCCR-Catalysis), Ecole Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
- National
Centre for Computational Design and Discovery of Novel Materials (MARVEL), Ecole Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
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4
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Hauge E, Kristiansen HE, Konecny L, Kadek M, Repisky M, Pedersen TB. Cost-Efficient High-Resolution Linear Absorption Spectra through Extrapolating the Dipole Moment from Real-Time Time-Dependent Electronic-Structure Theory. J Chem Theory Comput 2023; 19:7764-7775. [PMID: 37874968 PMCID: PMC10653104 DOI: 10.1021/acs.jctc.3c00727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 10/02/2023] [Accepted: 10/03/2023] [Indexed: 10/26/2023]
Abstract
We present a novel function fitting method for approximating the propagation of the time-dependent electric dipole moment from real-time electronic structure calculations. Real-time calculations of the electronic absorption spectrum require discrete Fourier transforms of the electric dipole moment. The spectral resolution is determined by the total propagation time, i.e., the trajectory length of the dipole moment, causing a high computational cost. Our developed method uses function fitting on shorter trajectories of the dipole moment, achieving arbitrary spectral resolution through extrapolation. Numerical testing shows that the fitting method can reproduce high-resolution spectra by using short dipole trajectories. The method converges with as little as 100 a.u. dipole trajectories for some systems, though the difficulty converging increases with the spectral density. We also introduce an error estimate of the fit, reliably assessing its convergence and hence the quality of the approximated spectrum.
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Affiliation(s)
- Eirill Hauge
- Hylleraas
Centre for Quantum Molecular Sciences, Department of Chemistry, University of Oslo, P.O. Box 1033, Blindern, 0315 Oslo, Norway
- Department
of Numerical Analysis and Scientific Computing, Simula Research Laboratory, Kristian Augusts Gate 23, 0164 Oslo, Norway
| | - Håkon Emil Kristiansen
- Hylleraas
Centre for Quantum Molecular Sciences, Department of Chemistry, University of Oslo, P.O. Box 1033, Blindern, 0315 Oslo, Norway
| | - Lukas Konecny
- Hylleraas
Centre for Quantum Molecular Sciences, Department of Chemistry, University of Tromsø—The Arctic University
of Norway, N-9037 Tromsø, Norway
- Center
for Free Electron Laser, Max Planck Institute
for the Structure and Dynamics of Matter Science, Luruper Chaussee 149, 22761 Hamburg, Germany
| | - Marius Kadek
- Hylleraas
Centre for Quantum Molecular Sciences, Department of Chemistry, University of Tromsø—The Arctic University
of Norway, N-9037 Tromsø, Norway
- Department
of Physics, Northeastern University, Boston, Massachusetts 02115, United States
| | - Michal Repisky
- Hylleraas
Centre for Quantum Molecular Sciences, Department of Chemistry, University of Tromsø—The Arctic University
of Norway, N-9037 Tromsø, Norway
- Department
of Physical and Theoretical Chemistry, Faculty of Natural Sciences, Comenius University, Ilkovicova 6, SK-84215 Bratislava, Slovakia
| | - Thomas Bondo Pedersen
- Hylleraas
Centre for Quantum Molecular Sciences, Department of Chemistry, University of Oslo, P.O. Box 1033, Blindern, 0315 Oslo, Norway
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5
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Ge F, Zhang L, Hou YF, Chen Y, Ullah A, Dral PO. Four-Dimensional-Spacetime Atomistic Artificial Intelligence Models. J Phys Chem Lett 2023; 14:7732-7743. [PMID: 37606602 DOI: 10.1021/acs.jpclett.3c01592] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/23/2023]
Abstract
We demonstrate that AI can learn atomistic systems in the four-dimensional (4D) spacetime. For this, we introduce the 4D-spacetime GICnet model, which for the given initial conditions (nuclear positions and velocities at time zero) can predict nuclear positions and velocities as a continuous function of time up to the distant future. Such models of molecules can be unrolled in the time dimension to yield long-time high-resolution molecular dynamics trajectories with high efficiency and accuracy. 4D-spacetime models can make predictions for different times in any order and do not need a stepwise evaluation of forces and integration of the equations of motions at discretized time steps, which is a major advance over traditional, cost-inefficient molecular dynamics. These models can be used to speed up dynamics, simulate vibrational spectra, and obtain deeper insight into nuclear motions, as we demonstrate for a series of organic molecules.
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Affiliation(s)
- Fuchun Ge
- State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
| | - Lina Zhang
- State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
| | - Yi-Fan Hou
- State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
| | - Yuxinxin Chen
- State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
| | - Arif Ullah
- School of Physics and Optoelectronic Engineering, Anhui University, Hefei 230601, China
| | - Pavlo O Dral
- State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
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6
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Ruth M, Gerbig D, Schreiner PR. Machine Learning for Bridging the Gap between Density Functional Theory and Coupled Cluster Energies. J Chem Theory Comput 2023. [PMID: 37418619 DOI: 10.1021/acs.jctc.3c00274] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/09/2023]
Abstract
Accurate electronic energies and properties are crucial for successful reaction design and mechanistic investigations. Computing energies and properties of molecular structures has proven extremely useful, and, with increasing computational power, the limits of high-level approaches (such as coupled cluster theory) are expanding to ever larger systems. However, because scaling is highly unfavorable, these methods are still not universally applicable to larger systems. To address the need for fast and accurate electronic energies of larger systems, we created a database of around 8000 small organic monomers (2000 dimers) optimized at the B3LYP-D3(BJ)/cc-pVTZ level of theory. This database also includes single-point energies computed at various levels of theory, including PBE1PBE, ωΒ97Χ, M06-2X, revTPSS, B3LYP, and BP86, for density functional theory as well as DLPNO-CCSD(T) and CCSD(T) for coupled cluster theory, all in conjunction with a cc-pVTZ basis. We used this database to train machine learning models based on graph neural networks using two different graph representations. Our models are able to make energy predictions from B3LYP-D3(BJ)/cc-pVTZ inputs to CCSD(T)/cc-pVTZ outputs with a mean absolute error of 0.78 and to DLPNO-CCSD(T)/cc-pVTZ with an mean absolute error of 0.50 and 0.18 kcal mol-1 for monomers and dimers, respectively. The model for dimers was further validated on the S22 database, and the monomer model was tested on challenging systems, including those with highly conjugated or functionally complex molecules.
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Affiliation(s)
- Marcel Ruth
- Institute of Organic Chemistry, Justus Liebig University, Heinrich-Buff-Ring 17, 35392 Giessen, Germany
| | - Dennis Gerbig
- Institute of Organic Chemistry, Justus Liebig University, Heinrich-Buff-Ring 17, 35392 Giessen, Germany
| | - Peter R Schreiner
- Institute of Organic Chemistry, Justus Liebig University, Heinrich-Buff-Ring 17, 35392 Giessen, Germany
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7
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Sun F, Kadupitiya J, Jadhao V. Probing Accuracy-Speedup Tradeoff in Machine Learning Surrogates for Molecular Dynamics Simulations. J Chem Theory Comput 2023. [PMID: 37094180 DOI: 10.1021/acs.jctc.2c01282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2023]
Abstract
The performance promise of machine learning surrogates of molecular dynamics simulations of soft materials is significant but generally comes at the cost of acquiring large training datasets to learn the complex relationships between input soft material attributes and output properties. Under the constraint of limited high-performance computing resources, optimizing the size of the training datasets becomes paramount. Using an artificial neural network based surrogate for molecular dynamics simulations of confined electrolytes, we explore the tradeoff between surrogate accuracy and computational gains. Accuracy is assessed by computing the root-mean-square errors between the surrogate predictions and the ground truth results obtained via molecular dynamics simulations. The computational performance is judged by evaluating the speedup which incorporates the training dataset creation time. Improvement in accuracy occurs with a loss of speedup, which scales as the inverse of the training dataset size. The link between surrogate generalizability and the accuracy-speedup tradeoff is assessed by examining the errors incurred in surrogate predictions on unseen, interpolated input variables and developing a net speedup metric to capture the associated gains.
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Affiliation(s)
- Fanbo Sun
- Intelligent Systems Engineering, Indiana University, 700 N. Woodlawn Avenue, Bloomington, Indiana 47408, United States
| | - Jcs Kadupitiya
- Intelligent Systems Engineering, Indiana University, 700 N. Woodlawn Avenue, Bloomington, Indiana 47408, United States
| | - Vikram Jadhao
- Intelligent Systems Engineering, Indiana University, 700 N. Woodlawn Avenue, Bloomington, Indiana 47408, United States
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8
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Perrella F, Coppola F, Rega N, Petrone A. An Expedited Route to Optical and Electronic Properties at Finite Temperature via Unsupervised Learning. Molecules 2023; 28:molecules28083411. [PMID: 37110644 PMCID: PMC10144358 DOI: 10.3390/molecules28083411] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 04/06/2023] [Accepted: 04/07/2023] [Indexed: 04/29/2023] Open
Abstract
Electronic properties and absorption spectra are the grounds to investigate molecular electronic states and their interactions with the environment. Modeling and computations are required for the molecular understanding and design strategies of photo-active materials and sensors. However, the interpretation of such properties demands expensive computations and dealing with the interplay of electronic excited states with the conformational freedom of the chromophores in complex matrices (i.e., solvents, biomolecules, crystals) at finite temperature. Computational protocols combining time dependent density functional theory and ab initio molecular dynamics (MD) have become very powerful in this field, although they require still a large number of computations for a detailed reproduction of electronic properties, such as band shapes. Besides the ongoing research in more traditional computational chemistry fields, data analysis and machine learning methods have been increasingly employed as complementary approaches for efficient data exploration, prediction and model development, starting from the data resulting from MD simulations and electronic structure calculations. In this work, dataset reduction capabilities by unsupervised clustering techniques applied to MD trajectories are proposed and tested for the ab initio modeling of electronic absorption spectra of two challenging case studies: a non-covalent charge-transfer dimer and a ruthenium complex in solution at room temperature. The K-medoids clustering technique is applied and is proven to be able to reduce by ∼100 times the total cost of excited state calculations on an MD sampling with no loss in the accuracy and it also provides an easier understanding of the representative structures (medoids) to be analyzed on the molecular scale.
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Affiliation(s)
- Fulvio Perrella
- Scuola Superiore Meridionale, Largo San Marcellino 10, I-80138 Napoli, Italy
| | - Federico Coppola
- Scuola Superiore Meridionale, Largo San Marcellino 10, I-80138 Napoli, Italy
| | - Nadia Rega
- Scuola Superiore Meridionale, Largo San Marcellino 10, I-80138 Napoli, Italy
- Department of Chemical Sciences, University of Napoli Federico II, Complesso Universitario di M.S. Angelo, via Cintia 21, I-80126 Napoli, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Napoli, Complesso Universitario di M.S. Angelo ed. 6, via Cintia 21, I-80126 Napoli, Italy
| | - Alessio Petrone
- Scuola Superiore Meridionale, Largo San Marcellino 10, I-80138 Napoli, Italy
- Department of Chemical Sciences, University of Napoli Federico II, Complesso Universitario di M.S. Angelo, via Cintia 21, I-80126 Napoli, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Napoli, Complesso Universitario di M.S. Angelo ed. 6, via Cintia 21, I-80126 Napoli, Italy
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9
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Ricci E, Vergadou N. Integrating Machine Learning in the Coarse-Grained Molecular Simulation of Polymers. J Phys Chem B 2023; 127:2302-2322. [PMID: 36888553 DOI: 10.1021/acs.jpcb.2c06354] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
Abstract
Machine learning (ML) is having an increasing impact on the physical sciences, engineering, and technology and its integration into molecular simulation frameworks holds great potential to expand their scope of applicability to complex materials and facilitate fundamental knowledge and reliable property predictions, contributing to the development of efficient materials design routes. The application of ML in materials informatics in general, and polymer informatics in particular, has led to interesting results, however great untapped potential lies in the integration of ML techniques into the multiscale molecular simulation methods for the study of macromolecular systems, specifically in the context of Coarse Grained (CG) simulations. In this Perspective, we aim at presenting the pioneering recent research efforts in this direction and discussing how these new ML-based techniques can contribute to critical aspects of the development of multiscale molecular simulation methods for bulk complex chemical systems, especially polymers. Prerequisites for the implementation of such ML-integrated methods and open challenges that need to be met toward the development of general systematic ML-based coarse graining schemes for polymers are discussed.
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Affiliation(s)
- Eleonora Ricci
- Institute of Nanoscience and Nanotechnology, National Center for Scientific Research "Demokritos", GR-15341 Agia Paraskevi, Athens, Greece
- Institute of Informatics and Telecommunications, National Center for Scientific Research "Demokritos", GR-15341 Agia Paraskevi, Athens, Greece
| | - Niki Vergadou
- Institute of Nanoscience and Nanotechnology, National Center for Scientific Research "Demokritos", GR-15341 Agia Paraskevi, Athens, Greece
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10
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Ferté A, Houssin A, Albouy N, Merritt ICD, Vacher M. ESIPT in the pyrrol pyridine molecule: mechanism, timescale and yield revealed using dynamics simulations. Phys Chem Chem Phys 2023; 25:9761-9765. [PMID: 36857691 DOI: 10.1039/d3cp00026e] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
Excited State Intramolecular Proton Transfer in pyrrol pyridine is theoretically investigated using non-adiabatic dynamics simulations. The photochemical process is completely characterised: the reaction time, the total yield and the accessibility of the conical intersection are evaluated. Finally, new mechanistic interpretation are extracted: the proton transfer reaction in this molecule is shown to be driven by two complementary mechanisms.
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Affiliation(s)
- Anthony Ferté
- Nantes Université, CNRS, CEISAM, UMR 6230, Nantes F-44000, France.
| | - Axel Houssin
- Nantes Université, CNRS, CEISAM, UMR 6230, Nantes F-44000, France.
| | - Nina Albouy
- Nantes Université, CNRS, CEISAM, UMR 6230, Nantes F-44000, France. .,Département de Chimie, École Normale Supérieure, PSL University, Paris 75005, France
| | | | - Morgane Vacher
- Nantes Université, CNRS, CEISAM, UMR 6230, Nantes F-44000, France.
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11
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Bougueroua S, Aboulfath Y, Barth D, Gaigeot MP. Algorithmic graph theory for post-processing molecular dynamics trajectories. Mol Phys 2023. [DOI: 10.1080/00268976.2022.2162456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Affiliation(s)
- Sana Bougueroua
- Université Paris-Saclay, Univ Evry, CNRS, LAMBE UMR8587, Evry-Courcouronnes, France
| | - Ylène Aboulfath
- Université Paris-Saclay, Univ Versailles SQ, DAVID, Versailles, France
| | - Dominique Barth
- Université Paris-Saclay, Univ Versailles SQ, DAVID, Versailles, France
| | - Marie-Pierre Gaigeot
- Université Paris-Saclay, Univ Evry, CNRS, LAMBE UMR8587, Evry-Courcouronnes, France
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12
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Applications of molecular dynamics simulation in nanomedicine. Nanomedicine (Lond) 2023. [DOI: 10.1016/b978-0-12-818627-5.00007-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2023] Open
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13
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Rydzewski J, Chen M, Ghosh TK, Valsson O. Reweighted Manifold Learning of Collective Variables from Enhanced Sampling Simulations. J Chem Theory Comput 2022; 18:7179-7192. [PMID: 36367826 DOI: 10.1021/acs.jctc.2c00873] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Enhanced sampling methods are indispensable in computational chemistry and physics, where atomistic simulations cannot exhaustively sample the high-dimensional configuration space of dynamical systems due to the sampling problem. A class of such enhanced sampling methods works by identifying a few slow degrees of freedom, termed collective variables (CVs), and enhancing the sampling along these CVs. Selecting CVs to analyze and drive the sampling is not trivial and often relies on chemical intuition. Despite routinely circumventing this issue using manifold learning to estimate CVs directly from standard simulations, such methods cannot provide mappings to a low-dimensional manifold from enhanced sampling simulations, as the geometry and density of the learned manifold are biased. Here, we address this crucial issue and provide a general reweighting framework based on anisotropic diffusion maps for manifold learning that takes into account that the learning data set is sampled from a biased probability distribution. We consider manifold learning methods based on constructing a Markov chain describing transition probabilities between high-dimensional samples. We show that our framework reverts the biasing effect, yielding CVs that correctly describe the equilibrium density. This advancement enables the construction of low-dimensional CVs using manifold learning directly from the data generated by enhanced sampling simulations. We call our framework reweighted manifold learning. We show that it can be used in many manifold learning techniques on data from both standard and enhanced sampling simulations.
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Affiliation(s)
- Jakub Rydzewski
- Institute of Physics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University, Grudziadzka 5, 87-100 Toruń, Poland
| | - Ming Chen
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
| | - Tushar K Ghosh
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
| | - Omar Valsson
- Department of Chemistry, University of North Texas, Denton, Texas 76201, United States
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14
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Abstract
Chemiluminescence (CL) utilizing chemiexcitation for energy transformation is one of the most highly sensitive and useful analytical techniques. The chemiexcitation is a chemical process of a ground-state reactant producing an excited-state product, in which a nonadiabatic event is facilitated by conical intersections (CIs), the specific molecular geometries where electronic states are degenerated. Cyclic peroxides, especially 1,2-dioxetane/dioxetanone derivatives, are the iconic chemiluminescent substances. In this Perspective, we concentrated on the CIs in the CL of cyclic peroxides. We first present a computational overview on the role of CIs between the ground (S0) state and the lowest singlet excited (S1) state in the thermolysis of cyclic peroxides. Subsequently, we discuss the role of the S0/S1 CI in the CL efficiency and point out misunderstandings in some theoretical studies on the singlet chemiexcitations of cyclic peroxides. Finally, we address the challenges and future prospects in theoretically calculating S0/S1 CIs and simulating the dynamics and chemiexcitation efficiency in the CL of cyclic peroxides.
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Affiliation(s)
- Ling Yue
- Key Laboratory for Non-equilibrium Synthesis and Modulation of Condensed Matter, Ministry of Education, School of Chemistry, Xi'an Jiaotong University, Xi'an, Shaanxi710049, China
| | - Ya-Jun Liu
- Center for Advanced Materials Research, Beijing Normal University, Zhuhai519087, China
- Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing100875, China
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15
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Gentili D, Ori G. Reversible assembly of nanoparticles: theory, strategies and computational simulations. NANOSCALE 2022; 14:14385-14432. [PMID: 36169572 DOI: 10.1039/d2nr02640f] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
The significant advances in synthesis and functionalization have enabled the preparation of high-quality nanoparticles that have found a plethora of successful applications. The unique physicochemical properties of nanoparticles can be manipulated through the control of size, shape, composition, and surface chemistry, but their technological application possibilities can be further expanded by exploiting the properties that emerge from their assembly. The ability to control the assembly of nanoparticles not only is required for many real technological applications, but allows the combination of the intrinsic properties of nanoparticles and opens the way to the exploitation of their complex interplay, giving access to collective properties. Significant advances and knowledge gained over the past few decades on nanoparticle assembly have made it possible to implement a growing number of strategies for reversible assembly of nanoparticles. In addition to being of interest for basic studies, such advances further broaden the range of applications and the possibility of developing innovative devices using nanoparticles. This review focuses on the reversible assembly of nanoparticles and includes the theoretical aspects related to the concept of reversibility, an up-to-date assessment of the experimental approaches applied to this field and the advanced computational schemes that offer key insights into the assembly mechanisms. We aim to provide readers with a comprehensive guide to address the challenges in assembling reversible nanoparticles and promote their applications.
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Affiliation(s)
- Denis Gentili
- Consiglio Nazionale delle Ricerche, Istituto per lo Studio dei Materiali Nanostrutturati (CNR-ISMN), Via P. Gobetti 101, 40129 Bologna, Italy.
| | - Guido Ori
- Université de Strasbourg, CNRS, Institut de Physique et Chimie des Matériaux de Strasbourg, UMR 7504, Rue du Loess 23, F-67034 Strasbourg, France.
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16
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Zhu Y, Peng J, Kang X, Xu C, Lan Z. The principal component analysis of the ring deformation in the nonadiabatic surface hopping dynamics. Phys Chem Chem Phys 2022; 24:24362-24382. [PMID: 36178471 DOI: 10.1039/d2cp03323b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The analysis of the leading active molecular motions in the on-the-fly trajectory surface hopping simulation provides the essential information to understand the geometric evolution in nonadiabatic dynamics. When the ring deformation is involved, the identification of the key active coordinates becomes challenging. A "hierarchical" protocol based on the dimensionality reduction and clustering approaches is proposed for the automatic analysis of the ring deformation in the nonadiabatic molecular dynamics. The representative system keto isocytosine is taken as the prototype to illustrate this protocol. The results indicate that the current hierarchical analysis protocol is a powerful way to clearly clarify both the major and minor active molecular motions of the ring distortion in nonadiabatic dynamics.
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Affiliation(s)
- Yifei Zhu
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, School of Environment, South China Normal University, Guangzhou 510006, P. R. China. .,MOE Key Laboratory of Environmental Theoretical Chemistry, South China Normal University, Guangzhou 510006, P. R. China
| | - Jiawei Peng
- MOE Key Laboratory of Environmental Theoretical Chemistry, South China Normal University, Guangzhou 510006, P. R. China.,School of Chemistry, South China Normal University, Guangzhou 510006, P. R. China
| | - Xu Kang
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, School of Environment, South China Normal University, Guangzhou 510006, P. R. China. .,MOE Key Laboratory of Environmental Theoretical Chemistry, South China Normal University, Guangzhou 510006, P. R. China
| | - Chao Xu
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, School of Environment, South China Normal University, Guangzhou 510006, P. R. China. .,MOE Key Laboratory of Environmental Theoretical Chemistry, South China Normal University, Guangzhou 510006, P. R. China
| | - Zhenggang Lan
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, School of Environment, South China Normal University, Guangzhou 510006, P. R. China. .,MOE Key Laboratory of Environmental Theoretical Chemistry, South China Normal University, Guangzhou 510006, P. R. China
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17
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Kuntz D, Wilson AK. Machine learning, artificial intelligence, and chemistry: how smart algorithms are reshaping simulation and the laboratory. PURE APPL CHEM 2022. [DOI: 10.1515/pac-2022-0202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
Machine learning and artificial intelligence are increasingly gaining in prominence through image analysis, language processing, and automation, to name a few applications. Machine learning is also making profound changes in chemistry. From revisiting decades-old analytical techniques for the purpose of creating better calibration curves, to assisting and accelerating traditional in silico simulations, to automating entire scientific workflows, to being used as an approach to deduce underlying physics of unexplained chemical phenomena, machine learning and artificial intelligence are reshaping chemistry, accelerating scientific discovery, and yielding new insights. This review provides an overview of machine learning and artificial intelligence from a chemist’s perspective and focuses on a number of examples of the use of these approaches in computational chemistry and in the laboratory.
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Affiliation(s)
- David Kuntz
- Department of Chemistry , University of North Texas , Denton , TX 76201 , USA
| | - Angela K. Wilson
- Department of Chemistry , Michigan State University , East Lansing , MI 48824 , USA
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18
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Differences in ligand-induced protein dynamics extracted from an unsupervised deep learning approach correlate with protein-ligand binding affinities. Commun Biol 2022; 5:481. [PMID: 35589949 PMCID: PMC9120437 DOI: 10.1038/s42003-022-03416-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 04/26/2022] [Indexed: 11/29/2022] Open
Abstract
Prediction of protein–ligand binding affinity is a major goal in drug discovery. Generally, free energy gap is calculated between two states (e.g., ligand binding and unbinding). The energy gap implicitly includes the effects of changes in protein dynamics induced by ligand binding. However, the relationship between protein dynamics and binding affinity remains unclear. Here, we propose a method that represents ligand-binding-induced protein behavioral change with a simple feature that can be used to predict protein–ligand affinity. From unbiased molecular simulation data, an unsupervised deep learning method measures the differences in protein dynamics at a ligand-binding site depending on the bound ligands. A dimension reduction method extracts a dynamic feature that strongly correlates to the binding affinities. Moreover, the residues that play important roles in protein–ligand interactions are specified based on their contribution to the differences. These results indicate the potential for binding dynamics-based drug discovery. Differences in ligand-induced protein dynamics extracted as a single feature from a deep learning-based analysis of MD simulations correlate with ligand binding affinity.
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19
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Salahub DR. Multiscale molecular modelling: from electronic structure to dynamics of nanosystems and beyond. Phys Chem Chem Phys 2022; 24:9051-9081. [PMID: 35389399 DOI: 10.1039/d1cp05928a] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Important contemporary biological and materials problems often depend on interactions that span orders of magnitude differences in spatial and temporal dimensions. This Tutorial Review attempts to provide an introduction to such fascinating problems through a series of case studies, aimed at beginning researchers, graduate students, postdocs and more senior colleagues who are changing direction to focus on multiscale aspects of their research. The choice of specific examples is highly personal, with examples either chosen from our own work or outstanding multiscale efforts from the literature. I start with various embedding schemes, as exemplified by polarizable continuum models, 3-D RISM, molecular DFT and frozen-density embedding. Next, QM/MM (quantum mechanical/molecular mechanical) techniques are the workhorse of pm-to-nm/ps-to-ns simulations; examples are drawn from enzymes and from nanocatalysis for oil-sands upgrading. Using polarizable force-fields in the QM/MM framework represents a burgeoning subfield; with examples from ion channels and electron dynamics in molecules subject to strong external fields, probing the atto-second dynamics of the electrons with RT-TDDFT (real-time - time-dependent density functional theory) eventually coupled with nuclear motion through the Ehrenfest approximation. This is followed by a section on coarse graining, bridging dimensions from atoms to cells. The penultimate chapter gives a quick overview of multiscale approaches that extend into the meso- and macro-scales, building on atomistic and coarse-grained techniques to enter the world of materials engineering, on the one hand, and cell biology, on the other. A final chapter gives just a glimpse of the burgeoning impact of machine learning on the structure-dynamics front. I aim to capture the excitement of contemporary leading-edge breakthroughs in the description of physico-chemical systems and processes in complex environments, with only enough historical content to provide context and aid the next generation of methodological development. While I aim also for a clear description of the essence of methodological breakthroughs, equations are kept to a minimum and detailed formalism and implementation details are left to the references. My approach is very selective (case studies) rather than exhaustive. I think that these case studies should provide fodder to build as complete a reference tree on multiscale modelling as the reader may wish, through forward and backward citation analysis. I hope that my choices of cases will excite interest in newcomers and help to fuel the growth of multiscale modelling in general.
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Affiliation(s)
- Dennis R Salahub
- Department of Chemistry, Department of Physics and Astronomy, CMS-Centre for Molecular Simulation, IQST-Institute for Quantum Science and Technology, Quantum Alberta, University of Calgary, Calgary, Alberta, T2N 1N4, Canada.
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20
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Kadupitiya JCS, Fox GC, Jadhao V. Solving Newton’s equations of motion with large timesteps using recurrent neural networks based operators. MACHINE LEARNING: SCIENCE AND TECHNOLOGY 2022. [DOI: 10.1088/2632-2153/ac5f60] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
Classical molecular dynamics simulations are based on solving Newton’s equations of motion. Using a small timestep, numerical integrators such as Verlet generate trajectories of particles as solutions to Newton’s equations. We introduce operators derived using recurrent neural networks that accurately solve Newton’s equations utilizing sequences of past trajectory data, and produce energy-conserving dynamics of particles using timesteps up to 4000 times larger compared to the Verlet timestep. We demonstrate significant speedup in many example problems including 3D systems of up to 16 particles.
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21
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Tolmachev D, Lukasheva N, Ramazanov R, Nazarychev V, Borzdun N, Volgin I, Andreeva M, Glova A, Melnikova S, Dobrovskiy A, Silber SA, Larin S, de Souza RM, Ribeiro MCC, Lyulin S, Karttunen M. Computer Simulations of Deep Eutectic Solvents: Challenges, Solutions, and Perspectives. Int J Mol Sci 2022; 23:645. [PMID: 35054840 PMCID: PMC8775846 DOI: 10.3390/ijms23020645] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 01/02/2022] [Accepted: 01/04/2022] [Indexed: 12/13/2022] Open
Abstract
Deep eutectic solvents (DESs) are one of the most rapidly evolving types of solvents, appearing in a broad range of applications, such as nanotechnology, electrochemistry, biomass transformation, pharmaceuticals, membrane technology, biocomposite development, modern 3D-printing, and many others. The range of their applicability continues to expand, which demands the development of new DESs with improved properties. To do so requires an understanding of the fundamental relationship between the structure and properties of DESs. Computer simulation and machine learning techniques provide a fruitful approach as they can predict and reveal physical mechanisms and readily be linked to experiments. This review is devoted to the computational research of DESs and describes technical features of DES simulations and the corresponding perspectives on various DES applications. The aim is to demonstrate the current frontiers of computational research of DESs and discuss future perspectives.
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Affiliation(s)
- Dmitry Tolmachev
- Institute of Macromolecular Compounds, Russian Academy of Sciences, Bolshoy pr. 31, 199004 St. Petersburg, Russia; (N.L.); (R.R.); (V.N.); (N.B.); (I.V.); (M.A.); (A.G.); (S.M.); (A.D.); (S.L.); (S.L.)
| | - Natalia Lukasheva
- Institute of Macromolecular Compounds, Russian Academy of Sciences, Bolshoy pr. 31, 199004 St. Petersburg, Russia; (N.L.); (R.R.); (V.N.); (N.B.); (I.V.); (M.A.); (A.G.); (S.M.); (A.D.); (S.L.); (S.L.)
| | - Ruslan Ramazanov
- Institute of Macromolecular Compounds, Russian Academy of Sciences, Bolshoy pr. 31, 199004 St. Petersburg, Russia; (N.L.); (R.R.); (V.N.); (N.B.); (I.V.); (M.A.); (A.G.); (S.M.); (A.D.); (S.L.); (S.L.)
| | - Victor Nazarychev
- Institute of Macromolecular Compounds, Russian Academy of Sciences, Bolshoy pr. 31, 199004 St. Petersburg, Russia; (N.L.); (R.R.); (V.N.); (N.B.); (I.V.); (M.A.); (A.G.); (S.M.); (A.D.); (S.L.); (S.L.)
| | - Natalia Borzdun
- Institute of Macromolecular Compounds, Russian Academy of Sciences, Bolshoy pr. 31, 199004 St. Petersburg, Russia; (N.L.); (R.R.); (V.N.); (N.B.); (I.V.); (M.A.); (A.G.); (S.M.); (A.D.); (S.L.); (S.L.)
| | - Igor Volgin
- Institute of Macromolecular Compounds, Russian Academy of Sciences, Bolshoy pr. 31, 199004 St. Petersburg, Russia; (N.L.); (R.R.); (V.N.); (N.B.); (I.V.); (M.A.); (A.G.); (S.M.); (A.D.); (S.L.); (S.L.)
| | - Maria Andreeva
- Institute of Macromolecular Compounds, Russian Academy of Sciences, Bolshoy pr. 31, 199004 St. Petersburg, Russia; (N.L.); (R.R.); (V.N.); (N.B.); (I.V.); (M.A.); (A.G.); (S.M.); (A.D.); (S.L.); (S.L.)
| | - Artyom Glova
- Institute of Macromolecular Compounds, Russian Academy of Sciences, Bolshoy pr. 31, 199004 St. Petersburg, Russia; (N.L.); (R.R.); (V.N.); (N.B.); (I.V.); (M.A.); (A.G.); (S.M.); (A.D.); (S.L.); (S.L.)
| | - Sofia Melnikova
- Institute of Macromolecular Compounds, Russian Academy of Sciences, Bolshoy pr. 31, 199004 St. Petersburg, Russia; (N.L.); (R.R.); (V.N.); (N.B.); (I.V.); (M.A.); (A.G.); (S.M.); (A.D.); (S.L.); (S.L.)
| | - Alexey Dobrovskiy
- Institute of Macromolecular Compounds, Russian Academy of Sciences, Bolshoy pr. 31, 199004 St. Petersburg, Russia; (N.L.); (R.R.); (V.N.); (N.B.); (I.V.); (M.A.); (A.G.); (S.M.); (A.D.); (S.L.); (S.L.)
| | - Steven A. Silber
- Department of Physics and Astronomy, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5B7, Canada;
- The Centre of Advanced Materials and Biomaterials Research, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5B7, Canada
| | - Sergey Larin
- Institute of Macromolecular Compounds, Russian Academy of Sciences, Bolshoy pr. 31, 199004 St. Petersburg, Russia; (N.L.); (R.R.); (V.N.); (N.B.); (I.V.); (M.A.); (A.G.); (S.M.); (A.D.); (S.L.); (S.L.)
| | - Rafael Maglia de Souza
- Departamento de Química Fundamental, Instituto de Química, Universidade de São Paulo, Avenida Professor Lineu Prestes 748, São Paulo 05508-070, Brazil; (R.M.d.S.); (M.C.C.R.)
| | - Mauro Carlos Costa Ribeiro
- Departamento de Química Fundamental, Instituto de Química, Universidade de São Paulo, Avenida Professor Lineu Prestes 748, São Paulo 05508-070, Brazil; (R.M.d.S.); (M.C.C.R.)
| | - Sergey Lyulin
- Institute of Macromolecular Compounds, Russian Academy of Sciences, Bolshoy pr. 31, 199004 St. Petersburg, Russia; (N.L.); (R.R.); (V.N.); (N.B.); (I.V.); (M.A.); (A.G.); (S.M.); (A.D.); (S.L.); (S.L.)
| | - Mikko Karttunen
- Institute of Macromolecular Compounds, Russian Academy of Sciences, Bolshoy pr. 31, 199004 St. Petersburg, Russia; (N.L.); (R.R.); (V.N.); (N.B.); (I.V.); (M.A.); (A.G.); (S.M.); (A.D.); (S.L.); (S.L.)
- Department of Physics and Astronomy, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5B7, Canada;
- The Centre of Advanced Materials and Biomaterials Research, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5B7, Canada
- Department of Chemistry, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5B7, Canada
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22
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Chen CH, Pepper K, Ulmschneider JP, Ulmschneider MB, Lu TK. Predicting Membrane-Active Peptide Dynamics in Fluidic Lipid Membranes. Methods Mol Biol 2022; 2405:115-136. [PMID: 35298811 DOI: 10.1007/978-1-0716-1855-4_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Understanding the interactions between peptides and lipid membranes could not only accelerate the development of antimicrobial peptides as treatments for infections but also be applied to finding targeted therapies for cancer and other diseases. However, designing biophysical experiments to study molecular interactions between flexible peptides and fluidic lipid membranes has been an ongoing challenge. Recently, with hardware advances, algorithm improvements, and more accurate parameterizations (i.e., force fields), all-atom molecular dynamics (MD) simulations have been used as a "computational microscope" to investigate the molecular interactions and mechanisms of membrane-active peptides in cell membranes (Chen et al., Curr Opin Struct Biol 61:160-166, 2020; Ulmschneider and Ulmschneider, Acc Chem Res 51(5):1106-1116, 2018; Dror et al., Annu Rev Biophys 41:429-452, 2012). In this chapter, we describe how to utilize MD simulations to predict and study peptide dynamics and how to validate the simulations by circular dichroism, intrinsic fluorescent probe, membrane leakage assay, electrical impedance, and isothermal titration calorimetry. Experimentally validated MD simulations open a new route towards peptide design starting from sequence and structure and leading to desirable functions.
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Affiliation(s)
- Charles H Chen
- Synthetic Biology Group, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Karen Pepper
- Synthetic Biology Group, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jakob P Ulmschneider
- Department of Physics, Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, China
| | | | - Timothy K Lu
- Synthetic Biology Group, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
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23
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Piccini G, Lee MS, Yuk SF, Zhang D, Collinge G, Kollias L, Nguyen MT, Glezakou VA, Rousseau R. Ab initio molecular dynamics with enhanced sampling in heterogeneous catalysis. Catal Sci Technol 2022. [DOI: 10.1039/d1cy01329g] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Enhanced sampling ab initio simulations enable to study chemical phenomena in catalytic systems including thermal effects & anharmonicity, & collective dynamics describing enthalpic & entropic contributions, which can significantly impact on reaction free energy landscapes.
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Affiliation(s)
- GiovanniMaria Piccini
- Basic & Applied Molecular Foundations, Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
- Istituto Eulero, Università della Svizzera italiana, Via Giuseppe Buffi 13, Lugano, Ticino, Switzerland
| | - Mal-Soon Lee
- Basic & Applied Molecular Foundations, Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Simuck F. Yuk
- Basic & Applied Molecular Foundations, Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
- Department of Chemistry and Life Science, United States Military Academy, West Point, NY 10996, USA
| | - Difan Zhang
- Basic & Applied Molecular Foundations, Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Greg Collinge
- Basic & Applied Molecular Foundations, Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Loukas Kollias
- Basic & Applied Molecular Foundations, Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Manh-Thuong Nguyen
- Basic & Applied Molecular Foundations, Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Vassiliki-Alexandra Glezakou
- Basic & Applied Molecular Foundations, Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Roger Rousseau
- Basic & Applied Molecular Foundations, Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
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24
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Zhang L, Mao H, Zhuang Y, Wang L, Liu L, Dong Y, Du J, Xie W, Yuan Z. Odor prediction and aroma mixture design using machine learning model and molecular surface charge density profiles. Chem Eng Sci 2021. [DOI: 10.1016/j.ces.2021.116947] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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25
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Maleki R, Rezvantalab S, Shahbazi MA. Role of molecular simulation in the future of nanomedicine. Nanomedicine (Lond) 2021; 16:2133-2136. [PMID: 34519542 DOI: 10.2217/nnm-2021-0120] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Affiliation(s)
- Reza Maleki
- Computational Biology & Chemistry Group, Universal Scientific Education & Research Network, Tehran, Iran
| | - Sima Rezvantalab
- Renewable Energies Department, Faculty of Chemical Engineering, Urmia University of Technology, Urmia, 57166 419, Iran
| | - Mohammad-Ali Shahbazi
- Drug Research Program, Division of Pharmaceutical Chemistry & Technology, Faculty of Pharmacy, University of Helsinki, Helsinki, FI-00014, Finland
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26
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Goings JJ, Hu H, Yang C, Li X. Reinforcement Learning Configuration Interaction. J Chem Theory Comput 2021; 17:5482-5491. [PMID: 34423637 DOI: 10.1021/acs.jctc.1c00010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Selected configuration interaction (sCI) methods exploit the sparsity of the full configuration interaction (FCI) wave function, yielding significant computational savings and wave function compression without sacrificing the accuracy. Despite recent advances in sCI methods, the selection of important determinants remains an open problem. We explore the possibility of utilizing reinforcement learning approaches to solve the sCI problem. By mapping the configuration interaction problem onto a sequential decision-making process, the agent learns on-the-fly which determinants to include and which to ignore, yielding a compressed wave function at near-FCI accuracy. This method, which we call reinforcement-learned configuration interaction, adds another weapon to the sCI arsenal and highlights how reinforcement learning approaches can potentially help solve challenging problems in electronic structure theory.
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Affiliation(s)
- Joshua J Goings
- Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Hang Hu
- Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Chao Yang
- Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Xiaosong Li
- Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
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27
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Westermayr J, Marquetand P. Machine Learning for Electronically Excited States of Molecules. Chem Rev 2021; 121:9873-9926. [PMID: 33211478 PMCID: PMC8391943 DOI: 10.1021/acs.chemrev.0c00749] [Citation(s) in RCA: 162] [Impact Index Per Article: 54.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Indexed: 12/11/2022]
Abstract
Electronically excited states of molecules are at the heart of photochemistry, photophysics, as well as photobiology and also play a role in material science. Their theoretical description requires highly accurate quantum chemical calculations, which are computationally expensive. In this review, we focus on not only how machine learning is employed to speed up such excited-state simulations but also how this branch of artificial intelligence can be used to advance this exciting research field in all its aspects. Discussed applications of machine learning for excited states include excited-state dynamics simulations, static calculations of absorption spectra, as well as many others. In order to put these studies into context, we discuss the promises and pitfalls of the involved machine learning techniques. Since the latter are mostly based on quantum chemistry calculations, we also provide a short introduction into excited-state electronic structure methods and approaches for nonadiabatic dynamics simulations and describe tricks and problems when using them in machine learning for excited states of molecules.
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Affiliation(s)
- Julia Westermayr
- Institute
of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Strasse 17, 1090 Vienna, Austria
| | - Philipp Marquetand
- Institute
of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Strasse 17, 1090 Vienna, Austria
- Vienna
Research Platform on Accelerating Photoreaction Discovery, University of Vienna, Währinger Strasse 17, 1090 Vienna, Austria
- Data
Science @ Uni Vienna, University of Vienna, Währinger Strasse 29, 1090 Vienna, Austria
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28
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Abstract
Electronically excited states of molecules are at the heart of photochemistry, photophysics, as well as photobiology and also play a role in material science. Their theoretical description requires highly accurate quantum chemical calculations, which are computationally expensive. In this review, we focus on not only how machine learning is employed to speed up such excited-state simulations but also how this branch of artificial intelligence can be used to advance this exciting research field in all its aspects. Discussed applications of machine learning for excited states include excited-state dynamics simulations, static calculations of absorption spectra, as well as many others. In order to put these studies into context, we discuss the promises and pitfalls of the involved machine learning techniques. Since the latter are mostly based on quantum chemistry calculations, we also provide a short introduction into excited-state electronic structure methods and approaches for nonadiabatic dynamics simulations and describe tricks and problems when using them in machine learning for excited states of molecules.
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Affiliation(s)
- Julia Westermayr
- Institute of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Strasse 17, 1090 Vienna, Austria
| | - Philipp Marquetand
- Institute of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Strasse 17, 1090 Vienna, Austria
- Vienna Research Platform on Accelerating Photoreaction Discovery, University of Vienna, Währinger Strasse 17, 1090 Vienna, Austria
- Data Science @ Uni Vienna, University of Vienna, Währinger Strasse 29, 1090 Vienna, Austria
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29
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Bannigan P, Aldeghi M, Bao Z, Häse F, Aspuru-Guzik A, Allen C. Machine learning directed drug formulation development. Adv Drug Deliv Rev 2021; 175:113806. [PMID: 34019959 DOI: 10.1016/j.addr.2021.05.016] [Citation(s) in RCA: 71] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 03/31/2021] [Accepted: 05/14/2021] [Indexed: 12/12/2022]
Abstract
Machine learning (ML) has enabled ground-breaking advances in the healthcare and pharmaceutical sectors, from improvements in cancer diagnosis, to the identification of novel drugs and drug targets as well as protein structure prediction. Drug formulation is an essential stage in the discovery and development of new medicines. Through the design of drug formulations, pharmaceutical scientists can engineer important properties of new medicines, such as improved bioavailability and targeted delivery. The traditional approach to drug formulation development relies on iterative trial-and-error, requiring a large number of resource-intensive and time-consuming in vitro and in vivo experiments. This review introduces the basic concepts of ML-directed workflows and discusses how these tools can be used to aid in the development of various types of drug formulations. ML-directed drug formulation development offers unparalleled opportunities to fast-track development efforts, uncover new materials, innovative formulations, and generate new knowledge in drug formulation science. The review also highlights the latest artificial intelligence (AI) technologies, such as generative models, Bayesian deep learning, reinforcement learning, and self-driving laboratories, which have been gaining momentum in drug discovery and chemistry and have potential in drug formulation development.
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Affiliation(s)
- Pauric Bannigan
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON M5S 3M2, Canada
| | - Matteo Aldeghi
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, ON M5S 3H6, Canada; Department of Computer Science, University of Toronto, Toronto, ON M5S 3H6, Canada; Vector Institute for Artificial Intelligence, Toronto, ON M5S 1M1, Canada
| | - Zeqing Bao
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON M5S 3M2, Canada
| | - Florian Häse
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, ON M5S 3H6, Canada; Department of Computer Science, University of Toronto, Toronto, ON M5S 3H6, Canada; Vector Institute for Artificial Intelligence, Toronto, ON M5S 1M1, Canada
| | - Alán Aspuru-Guzik
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, ON M5S 3H6, Canada; Department of Computer Science, University of Toronto, Toronto, ON M5S 3H6, Canada; Vector Institute for Artificial Intelligence, Toronto, ON M5S 1M1, Canada; Lebovic Fellow, Canadian Institute for Advanced Research, Toronto, ON M5S 1M1, Canada.
| | - Christine Allen
- Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON M5S 3M2, Canada.
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30
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Westermayr J, Gastegger M, Schütt KT, Maurer RJ. Perspective on integrating machine learning into computational chemistry and materials science. J Chem Phys 2021; 154:230903. [PMID: 34241249 DOI: 10.1063/5.0047760] [Citation(s) in RCA: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
Machine learning (ML) methods are being used in almost every conceivable area of electronic structure theory and molecular simulation. In particular, ML has become firmly established in the construction of high-dimensional interatomic potentials. Not a day goes by without another proof of principle being published on how ML methods can represent and predict quantum mechanical properties-be they observable, such as molecular polarizabilities, or not, such as atomic charges. As ML is becoming pervasive in electronic structure theory and molecular simulation, we provide an overview of how atomistic computational modeling is being transformed by the incorporation of ML approaches. From the perspective of the practitioner in the field, we assess how common workflows to predict structure, dynamics, and spectroscopy are affected by ML. Finally, we discuss how a tighter and lasting integration of ML methods with computational chemistry and materials science can be achieved and what it will mean for research practice, software development, and postgraduate training.
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Affiliation(s)
- Julia Westermayr
- Department of Chemistry, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, United Kingdom
| | - Michael Gastegger
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany
| | - Kristof T Schütt
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany
| | - Reinhard J Maurer
- Department of Chemistry, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, United Kingdom
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31
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Tapavicza E, von Rudorff GF, De Haan DO, Contin M, George C, Riva M, von Lilienfeld OA. Elucidating an Atmospheric Brown Carbon Species-Toward Supplanting Chemical Intuition with Exhaustive Enumeration and Machine Learning. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:8447-8457. [PMID: 34080853 DOI: 10.1021/acs.est.1c00885] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Brown carbon (BrC) is involved in atmospheric light absorption and climate forcing and can cause adverse health effects. Understanding the formation mechanisms and molecular structure of BrC is of key importance in developing strategies to control its environment and health impact. Structure determination of BrC is challenging, due to the lack of experiments providing molecular fingerprints and the sheer number of molecular candidates with identical mass. Suggestions based on chemical intuition are prone to errors due to the inherent bias. We present an unbiased algorithm, using graph-based molecule generation and machine learning, which can identify all molecular structures of compounds involved in biomass burning and the composition of BrC. We apply this algorithm to C12H12O7, a light-absorbing "test case" molecule identified in chamber experiments on the aqueous photo-oxidation of syringol, a prevalent marker in wood smoke. Of the 260 million molecular graphs, the algorithm leaves only 36,518 (0.01%) as viable candidates matching the spectrum. Although no unique molecular structure is obtained from only a chemical formula and a UV/vis absorption spectrum, we discuss further reduction strategies and their efficacy. With additional data, the method can potentially more rapidly identify isomers extracted from lab and field aerosol particles without introducing human bias.
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Affiliation(s)
- Enrico Tapavicza
- Department of Chemistry and Biochemistry, California State University, Long Beach, 1250 Bellflower Boulevard, Long Beach, California 90840, United States
| | - Guido Falk von Rudorff
- Faculty of Physics, University of Vienna, Kolingasse 14-16, AT-1090 Wien, Austria
- Institute of Physical Chemistry and National Center for Computational Design and Discovery of Novel Materials (MARVEL), Department of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland
| | - David O De Haan
- Department of Chemistry and Biochemistry, University of San Diego, 5998 Alcala Park, San Diego, California 92110, United States
| | - Mario Contin
- Facultad de Farmacia y Bioquímica, Departamento de Química Analitica y Fisicoquímica, Universidad de Buenos Aires, Junín 956, Buenos Aires C1113AAD, Argentina
| | - Christian George
- Université Lyon, Université Claude Bernard Lyon 1, CNRS, IRCELYON, 69626 Villeurbanne, France
| | - Matthieu Riva
- Université Lyon, Université Claude Bernard Lyon 1, CNRS, IRCELYON, 69626 Villeurbanne, France
| | - O Anatole von Lilienfeld
- Faculty of Physics, University of Vienna, Kolingasse 14-16, AT-1090 Wien, Austria
- Institute of Physical Chemistry and National Center for Computational Design and Discovery of Novel Materials (MARVEL), Department of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland
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32
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Maley SM, Melville J, Yu S, Teynor MS, Carlsen R, Hargis C, Hamilton RS, Grant BO, Ess DH. Machine learning classification of disrotatory IRC and conrotatory non-IRC trajectory motion for cyclopropyl radical ring opening. Phys Chem Chem Phys 2021; 23:12309-12320. [PMID: 34018524 DOI: 10.1039/d1cp00612f] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Quasiclassical trajectory analysis is now a standard tool to analyze non-minimum energy pathway motion of organic reactions. However, due to the large amount of information associated with trajectories, quantitative analysis of the dynamic origin of reaction selectivity is complex. For the electrocyclic ring opening of cyclopropyl radical, more than 4000 trajectories were run showing that allyl radicals are formed through a mixture of disrotatory intrinsic reaction coordinate (IRC) motion as well as conrotatory non-IRC motion. Geometric, vibrational mode, and atomic velocity transition-state features from these trajectories were used for supervised machine learning analysis with classification algorithms. Accuracy >80% with a random forest model enabled quantitative and qualitative assessment of transition-state trajectory features controlling disrotatory IRC versus conrotatory non-IRC motion. This analysis revealed that there are two key vibrational modes where their directional combination provides prediction of IRC versus non-IRC motion.
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Affiliation(s)
- Steven M Maley
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, USA.
| | - Jesse Melville
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, USA.
| | - Spencer Yu
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, USA.
| | - Matthew S Teynor
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, USA.
| | - Ryan Carlsen
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, USA.
| | - Cal Hargis
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, USA.
| | - R Spencer Hamilton
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, USA.
| | - Benjamin O Grant
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, USA.
| | - Daniel H Ess
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, USA.
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33
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Casier B, Chagas da Silva M, Badawi M, Pascale F, Bučko T, Lebègue S, Rocca D. Hybrid localized graph kernel for machine learning energy-related properties of molecules and solids. J Comput Chem 2021; 42:1390-1401. [PMID: 34009668 DOI: 10.1002/jcc.26550] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 04/07/2021] [Accepted: 04/21/2021] [Indexed: 11/10/2022]
Abstract
Nowadays, the coupling of electronic structure and machine learning techniques serves as a powerful tool to predict chemical and physical properties of a broad range of systems. With the aim of improving the accuracy of predictions, a large number of representations for molecules and solids for machine learning applications has been developed. In this work we propose a novel descriptor based on the notion of molecular graph. While graphs are largely employed in classification problems in cheminformatics or bioinformatics, they are not often used in regression problem, especially of energy-related properties. Our method is based on a local decomposition of atomic environments and on the hybridization of two kernel functions: a graph kernel contribution that describes the chemical pattern and a Coulomb label contribution that encodes finer details of the local geometry. The accuracy of this new kernel method in energy predictions of molecular and condensed phase systems is demonstrated by considering the popular QM7 and BA10 datasets. These examples show that the hybrid localized graph kernel outperforms traditional approaches such as, for example, the smooth overlap of atomic positions and the Coulomb matrices.
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Affiliation(s)
- Bastien Casier
- Université de Lorraine and CNRS, LPCT, UMR 7019, F-54000 Nancy, France
| | | | - Michael Badawi
- Université de Lorraine and CNRS, LPCT, UMR 7019, F-54000 Nancy, France
| | | | - Tomáš Bučko
- Department of Physical and Theoretical Chemistry, Faculty of Natural Sciences, Comenius University in Bratislava, Bratislava, Slovakia.,Institute of Inorganic Chemistry, Slovak Academy of Sciences, Bratislava, Slovakia
| | - Sébastien Lebègue
- Université de Lorraine and CNRS, LPCT, UMR 7019, F-54000 Nancy, France
| | - Dario Rocca
- Université de Lorraine and CNRS, LPCT, UMR 7019, F-54000 Nancy, France
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34
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Remodelling structure-based drug design using machine learning. Emerg Top Life Sci 2021; 5:13-27. [PMID: 33825834 DOI: 10.1042/etls20200253] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 03/17/2021] [Accepted: 03/30/2021] [Indexed: 12/13/2022]
Abstract
To keep up with the pace of rapid discoveries in biomedicine, a plethora of research endeavors had been directed toward Rational Drug Development that slowly gave way to Structure-Based Drug Design (SBDD). In the past few decades, SBDD played a stupendous role in identification of novel drug-like molecules that are capable of altering the structures and/or functions of the target macromolecules involved in different disease pathways and networks. Unfortunately, post-delivery drug failures due to adverse drug interactions have constrained the use of SBDD in biomedical applications. However, recent technological advancements, along with parallel surge in clinical research have led to the concomitant establishment of other powerful computational techniques such as Artificial Intelligence (AI) and Machine Learning (ML). These leading-edge tools with the ability to successfully predict side-effects of a wide range of drugs have eventually taken over the field of drug design. ML, a subset of AI, is a robust computational tool that is capable of data analysis and analytical model building with minimal human intervention. It is based on powerful algorithms that use huge sets of 'training data' as inputs to predict new output values, which improve iteratively through experience. In this review, along with a brief discussion on the evolution of the drug discovery process, we have focused on the methodologies pertaining to the technological advancements of machine learning. This review, with specific examples, also emphasises the tremendous contributions of ML in the field of biomedicine, while exploring possibilities for future developments.
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35
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Koutsoukos S, Philippi F, Malaret F, Welton T. A review on machine learning algorithms for the ionic liquid chemical space. Chem Sci 2021; 12:6820-6843. [PMID: 34123314 PMCID: PMC8153233 DOI: 10.1039/d1sc01000j] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 04/28/2021] [Indexed: 01/05/2023] Open
Abstract
There are thousands of papers published every year investigating the properties and possible applications of ionic liquids. Industrial use of these exceptional fluids requires adequate understanding of their physical properties, in order to create the ionic liquid that will optimally suit the application. Computational property prediction arose from the urgent need to minimise the time and cost that would be required to experimentally test different combinations of ions. This review discusses the use of machine learning algorithms as property prediction tools for ionic liquids (either as standalone methods or in conjunction with molecular dynamics simulations), presents common problems of training datasets and proposes ways that could lead to more accurate and efficient models.
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Affiliation(s)
- Spyridon Koutsoukos
- Department of Chemistry, Molecular Sciences Research Hub, Imperial College London White City Campus London W12 0BZ UK
| | - Frederik Philippi
- Department of Chemistry, Molecular Sciences Research Hub, Imperial College London White City Campus London W12 0BZ UK
| | - Francisco Malaret
- Department of Chemical Engineering, Imperial College London South Kensington Campus London SW7 2AZ UK
| | - Tom Welton
- Department of Chemistry, Molecular Sciences Research Hub, Imperial College London White City Campus London W12 0BZ UK
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36
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Peng J, Xie Y, Hu D, Lan Z. Analysis of bath motion in MM-SQC dynamics via dimensionality reduction approach: Principal component analysis. J Chem Phys 2021; 154:094122. [PMID: 33685149 DOI: 10.1063/5.0039743] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
The system-plus-bath model is an important tool to understand the nonadiabatic dynamics of large molecular systems. Understanding the collective motion of a large number of bath modes is essential for revealing their key roles in the overall dynamics. Here, we applied principal component analysis (PCA) to investigate the bath motion in the basis of a large dataset generated from the symmetrical quasi-classical dynamics method based on the Meyer-Miller mapping Hamiltonian nonadiabatic dynamics for the excited-state energy transfer in the Frenkel-exciton model. The PCA method clearly elucidated that two types of bath modes, which either display strong vibronic coupling or have frequencies close to that of the electronic transition, are important to the nonadiabatic dynamics. These observations were fully consistent with the physical insights. The conclusions were based on the PCA of the trajectory data and did not involve significant pre-defined physical knowledge. The results show that the PCA approach, which is one of the simplest unsupervised machine learning dimensionality reduction methods, is a powerful one for analyzing complicated nonadiabatic dynamics in the condensed phase with many degrees of freedom.
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Affiliation(s)
- Jiawei Peng
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China
| | - Yu Xie
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China
| | - Deping Hu
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China
| | - Zhenggang Lan
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China
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37
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Meng F, Liang Z, Zhao K, Luo C. Drug design targeting active posttranslational modification protein isoforms. Med Res Rev 2020; 41:1701-1750. [PMID: 33355944 DOI: 10.1002/med.21774] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 11/29/2020] [Accepted: 12/03/2020] [Indexed: 12/11/2022]
Abstract
Modern drug design aims to discover novel lead compounds with attractable chemical profiles to enable further exploration of the intersection of chemical space and biological space. Identification of small molecules with good ligand efficiency, high activity, and selectivity is crucial toward developing effective and safe drugs. However, the intersection is one of the most challenging tasks in the pharmaceutical industry, as chemical space is almost infinity and continuous, whereas the biological space is very limited and discrete. This bottleneck potentially limits the discovery of molecules with desirable properties for lead optimization. Herein, we present a new direction leveraging posttranslational modification (PTM) protein isoforms target space to inspire drug design termed as "Post-translational Modification Inspired Drug Design (PTMI-DD)." PTMI-DD aims to extend the intersections of chemical space and biological space. We further rationalized and highlighted the importance of PTM protein isoforms and their roles in various diseases and biological functions. We then laid out a few directions to elaborate the PTMI-DD in drug design including discovering covalent binding inhibitors mimicking PTMs, targeting PTM protein isoforms with distinctive binding sites from that of wild-type counterpart, targeting protein-protein interactions involving PTMs, and hijacking protein degeneration by ubiquitination for PTM protein isoforms. These directions will lead to a significant expansion of the biological space and/or increase the tractability of compounds, primarily due to precisely targeting PTM protein isoforms or complexes which are highly relevant to biological functions. Importantly, this new avenue will further enrich the personalized treatment opportunity through precision medicine targeting PTM isoforms.
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Affiliation(s)
- Fanwang Meng
- Drug Discovery and Design Center, the Center for Chemical Biology, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.,Department of Chemistry and Chemical Biology, McMaster University, Hamilton, Ontario, Canada
| | - Zhongjie Liang
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
| | - Kehao Zhao
- School of Pharmacy, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, Yantai University, Yantai, China
| | - Cheng Luo
- Drug Discovery and Design Center, the Center for Chemical Biology, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
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38
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Wang XY, Chen BB, Zhang J, Zhou ZR, Lv J, Geng XP, Qian RC. Exploiting deep learning for predictable carbon dot design. Chem Commun (Camb) 2020; 57:532-535. [PMID: 33336670 DOI: 10.1039/d0cc07882d] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
In this study, we developed a deep convolution neural network (DCNN) model for predicting the optical properties of carbon dots (CDs), including spectral properties and fluorescence color under ultraviolet irradiation. These results demonstrate the powerful potential of DCNN for guiding the synthesis of CDs.
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Affiliation(s)
- Xiao-Yuan Wang
- Key Laboratory for Advanced Materials School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai, 200237, P. R. China.
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39
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Wen M, Blau SM, Spotte-Smith EWC, Dwaraknath S, Persson KA. BonDNet: a graph neural network for the prediction of bond dissociation energies for charged molecules. Chem Sci 2020; 12:1858-1868. [PMID: 34163950 PMCID: PMC8179073 DOI: 10.1039/d0sc05251e] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
A broad collection of technologies, including e.g. drug metabolism, biofuel combustion, photochemical decontamination of water, and interfacial passivation in energy production/storage systems rely on chemical processes that involve bond-breaking molecular reactions. In this context, a fundamental thermodynamic property of interest is the bond dissociation energy (BDE) which measures the strength of a chemical bond. Fast and accurate prediction of BDEs for arbitrary molecules would lay the groundwork for data-driven projections of complex reaction cascades and hence a deeper understanding of these critical chemical processes and, ultimately, how to reverse design them. In this paper, we propose a chemically inspired graph neural network machine learning model, BonDNet, for the rapid and accurate prediction of BDEs. BonDNet maps the difference between the molecular representations of the reactants and products to the reaction BDE. Because of the use of this difference representation and the introduction of global features, including molecular charge, it is the first machine learning model capable of predicting both homolytic and heterolytic BDEs for molecules of any charge. To test the model, we have constructed a dataset of both homolytic and heterolytic BDEs for neutral and charged (−1 and +1) molecules. BonDNet achieves a mean absolute error (MAE) of 0.022 eV for unseen test data, significantly below chemical accuracy (0.043 eV). Besides the ability to handle complex bond dissociation reactions that no previous model could consider, BonDNet distinguishes itself even in only predicting homolytic BDEs for neutral molecules; it achieves an MAE of 0.020 eV on the PubChem BDE dataset, a 20% improvement over the previous best performing model. We gain additional insight into the model's predictions by analyzing the patterns in the features representing the molecules and the bond dissociation reactions, which are qualitatively consistent with chemical rules and intuition. BonDNet is just one application of our general approach to representing and learning chemical reactivity, and it could be easily extended to the prediction of other reaction properties in the future. Prediction of bond dissociation energies for charged molecules with a graph neural network enabled by global molecular features and reaction difference features between products and reactants.![]()
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Affiliation(s)
- Mingjian Wen
- Department of Materials Science and Engineering, University of California Berkeley CA 94720 USA.,Energy Technologies Area, Lawrence Berkeley National Laboratory Berkeley CA 94720 USA
| | - Samuel M Blau
- Energy Technologies Area, Lawrence Berkeley National Laboratory Berkeley CA 94720 USA
| | - Evan Walter Clark Spotte-Smith
- Department of Materials Science and Engineering, University of California Berkeley CA 94720 USA.,Energy Technologies Area, Lawrence Berkeley National Laboratory Berkeley CA 94720 USA
| | - Shyam Dwaraknath
- Energy Technologies Area, Lawrence Berkeley National Laboratory Berkeley CA 94720 USA
| | - Kristin A Persson
- Department of Materials Science and Engineering, University of California Berkeley CA 94720 USA.,Molecular Foundry, Lawrence Berkeley National Laboratory Berkeley CA 94720 USA
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40
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Krenn M, Häse F, Nigam A, Friederich P, Aspuru-Guzik A. Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation. MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2020. [DOI: 10.1088/2632-2153/aba947] [Citation(s) in RCA: 119] [Impact Index Per Article: 29.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
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41
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Goings JJ, Hammes-Schiffer S. Nonequilibrium Dynamics of Proton-Coupled Electron Transfer in Proton Wires: Concerted but Asynchronous Mechanisms. ACS CENTRAL SCIENCE 2020; 6:1594-1601. [PMID: 32999935 PMCID: PMC7517869 DOI: 10.1021/acscentsci.0c00756] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Indexed: 05/29/2023]
Abstract
The coupling between electrons and protons and the long-range transport of protons play important roles throughout biology. Biomimetic systems derived from benzimidazole-phenol (BIP) constructs have been designed to undergo proton-coupled electron transfer (PCET) upon electrochemical or photochemical oxidation. Moreover, these systems can transport protons along hydrogen-bonded networks or proton wires through multiproton PCET. Herein, the nonequilibrium dynamics of both single and double proton transfer in BIP molecules initiated by oxidation are investigated with first-principles molecular dynamics simulations. Although these processes are concerted in that no thermodynamically stable intermediate is observed, the simulations predict that they are predominantly asynchronous on the ultrafast time scale. For both systems, the first proton transfer typically occurs ∼100 fs after electron transfer. For the double proton transfer system, typically the second proton transfer occurs hundreds of femtoseconds after the initial proton transfer. A machine learning algorithm was used to identify the key molecular vibrational modes essential for proton transfer: a slow, in-plane bending mode that dominates the overall inner-sphere reorganization, the proton donor-acceptor motion that leads to vibrational coherence, and the faster donor-hydrogen stretching mode. The asynchronous double proton transfer mechanism can be understood in terms of a significant mode corresponding to the two anticorrelated proton donor-acceptor motions, typically decreasing only one donor-acceptor distance at a time. Although these PCET processes appear concerted on the time scale of typical electrochemical experiments, attaching these BIP constructs to photosensitizers may enable the detection of the asynchronicity of the electron and multiple proton transfers with ultrafast two-dimensional spectroscopy. Understanding the fundamental PCET mechanisms at this level will guide the design of PCET systems for catalysis and energy conversion processes.
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Affiliation(s)
- Joshua J. Goings
- Department of Chemistry, Yale University, 225
Prospect Street, New Haven, Connecticut 06520, United States
| | - Sharon Hammes-Schiffer
- Department of Chemistry, Yale University, 225
Prospect Street, New Haven, Connecticut 06520, United States
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42
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Bahlke MP, Mogos N, Proppe J, Herrmann C. Exchange Spin Coupling from Gaussian Process Regression. J Phys Chem A 2020; 124:8708-8723. [DOI: 10.1021/acs.jpca.0c05983] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Marc Philipp Bahlke
- Department of Chemistry, University of Hamburg, Martin-Luther-King-Platz 6, 20146 Hamburg, Germany
| | - Natnael Mogos
- Department of Chemistry, University of Hamburg, Martin-Luther-King-Platz 6, 20146 Hamburg, Germany
| | - Jonny Proppe
- Institute of Physical Chemistry, Georg-August University, Tammannstr. 6, 37077 Göttingen, Germany
| | - Carmen Herrmann
- Department of Chemistry, University of Hamburg, Martin-Luther-King-Platz 6, 20146 Hamburg, Germany
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43
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Westermayr J, Marquetand P. Machine learning and excited-state molecular dynamics. MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2020. [DOI: 10.1088/2632-2153/ab9c3e] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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44
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Häse F, Roch LM, Friederich P, Aspuru-Guzik A. Designing and understanding light-harvesting devices with machine learning. Nat Commun 2020; 11:4587. [PMID: 32917886 PMCID: PMC7486390 DOI: 10.1038/s41467-020-17995-8] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Accepted: 07/16/2020] [Indexed: 01/27/2023] Open
Abstract
Understanding the fundamental processes of light-harvesting is crucial to the development of clean energy materials and devices. Biological organisms have evolved complex metabolic mechanisms to efficiently convert sunlight into chemical energy. Unraveling the secrets of this conversion has inspired the design of clean energy technologies, including solar cells and photocatalytic water splitting. Describing the emergence of macroscopic properties from microscopic processes poses the challenge to bridge length and time scales of several orders of magnitude. Machine learning experiences increased popularity as a tool to bridge the gap between multi-level theoretical models and Edisonian trial-and-error approaches. Machine learning offers opportunities to gain detailed scientific insights into the underlying principles governing light-harvesting phenomena and can accelerate the fabrication of light-harvesting devices.
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Affiliation(s)
- Florian Häse
- Department of Chemistry and Chemical Biology, Harvard University, 12 Oxford Street, Cambridge, 02138, MA, USA
- CIFAR AI Chair, Vector Institute for Artificial Intelligence, 661 University Avenue, Toronto, ON, M5S 1M1, Canada
- Department of Computer Science, University of Toronto, 214 College Street, Toronto, ON, M5S 3H6, Canada
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
| | - Loïc M Roch
- CIFAR AI Chair, Vector Institute for Artificial Intelligence, 661 University Avenue, Toronto, ON, M5S 1M1, Canada
- Department of Computer Science, University of Toronto, 214 College Street, Toronto, ON, M5S 3H6, Canada
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
- ChemOS Sàrl, Lausanne, VD, 1006, Switzerland
| | - Pascal Friederich
- Department of Computer Science, University of Toronto, 214 College Street, Toronto, ON, M5S 3H6, Canada
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
- Institute of Nanotechnology, Karlsruhe Insititute of Technology, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
| | - Alán Aspuru-Guzik
- CIFAR AI Chair, Vector Institute for Artificial Intelligence, 661 University Avenue, Toronto, ON, M5S 1M1, Canada.
- Department of Computer Science, University of Toronto, 214 College Street, Toronto, ON, M5S 3H6, Canada.
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada.
- Lebovic Fellow, Canadian Institute for Advanced Research (CIFAR), 661 University Avenue, Toronto, ON, M5S 1M1, Canada.
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45
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Gkeka P, Stoltz G, Barati Farimani A, Belkacemi Z, Ceriotti M, Chodera JD, Dinner AR, Ferguson AL, Maillet JB, Minoux H, Peter C, Pietrucci F, Silveira A, Tkatchenko A, Trstanova Z, Wiewiora R, Lelièvre T. Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems. J Chem Theory Comput 2020; 16:4757-4775. [PMID: 32559068 PMCID: PMC8312194 DOI: 10.1021/acs.jctc.0c00355] [Citation(s) in RCA: 82] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Machine learning encompasses tools and algorithms that are now becoming popular in almost all scientific and technological fields. This is true for molecular dynamics as well, where machine learning offers promises of extracting valuable information from the enormous amounts of data generated by simulation of complex systems. We provide here a review of our current understanding of goals, benefits, and limitations of machine learning techniques for computational studies on atomistic systems, focusing on the construction of empirical force fields from ab initio databases and the determination of reaction coordinates for free energy computation and enhanced sampling.
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Affiliation(s)
- Paraskevi Gkeka
- Integrated Drug Discovery, Sanofi R&D, 91385 Chilly-Mazarin, France
| | - Gabriel Stoltz
- CERMICS, Ecole des Ponts, Marne-la-Vallée, France
- Matherials Project-Team, Inria Paris, 75012 Paris, France
| | | | - Zineb Belkacemi
- Integrated Drug Discovery, Sanofi R&D, 91385 Chilly-Mazarin, France
- CERMICS, Ecole des Ponts, Marne-la-Vallée, France
| | - Michele Ceriotti
- Laboratory of Computational Science and Modelling, Institute of Materials, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | - Aaron R Dinner
- Department of Chemistry, The University of Chicago, Chicago, Illinois 60637, United States
| | - Andrew L Ferguson
- Pritzker School of Molecular Engineering, University of Chicago, 5640 South Ellis Avenue, Chicago, Illinois 60637, United States
| | | | - Hervé Minoux
- Integrated Drug Discovery, Sanofi R&D, 94403 Vitry-sur-Seine, France
| | | | - Fabio Pietrucci
- UMR CNRS 7590, MNHN, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, Sorbonne Université, 75005 Paris, France
| | - Ana Silveira
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | - Alexandre Tkatchenko
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg
| | - Zofia Trstanova
- School of Mathematics, The University of Edinburgh, Edinburgh EH9 3FD, U.K
| | - Rafal Wiewiora
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | - Tony Lelièvre
- CERMICS, Ecole des Ponts, Marne-la-Vallée, France
- Matherials Project-Team, Inria Paris, 75012 Paris, France
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46
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Rossi K, Jurásková V, Wischert R, Garel L, Corminbœuf C, Ceriotti M. Simulating Solvation and Acidity in Complex Mixtures with First-Principles Accuracy: The Case of CH 3SO 3H and H 2O 2 in Phenol. J Chem Theory Comput 2020; 16:5139-5149. [PMID: 32567854 DOI: 10.1021/acs.jctc.0c00362] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
We present a generally applicable computational framework for the efficient and accurate characterization of molecular structural patterns and acid properties in an explicit solvent using H2O2 and CH3SO3H in phenol as an example. To address the challenges posed by the complexity of the problem, we resort to a set of data-driven methods and enhanced sampling algorithms. The synergistic application of these techniques makes the first-principle estimation of the chemical properties feasible without renouncing to the use of explicit solvation, involving extensive statistical sampling. Ensembles of neural network (NN) potentials are trained on a set of configurations carefully selected out of preliminary simulations performed at a low-cost density functional tight-binding (DFTB) level. The energy and forces of these configurations are then recomputed at the hybrid density functional theory (DFT) level and used to train the neural networks. The stability of the NN model is enhanced by using DFTB energetics as a baseline, but the efficiency of the direct NN (i.e., baseline-free) is exploited via a multiple-time-step integrator. The neural network potentials are combined with enhanced sampling techniques, such as replica exchange and metadynamics, and used to characterize the relevant protonated species and dominant noncovalent interactions in the mixture, also considering nuclear quantum effects.
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Affiliation(s)
- Kevin Rossi
- Laboratory of Computational Science and Modeling (COSMO), Institute of Materials, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland
| | - Veronika Jurásková
- Laboratory for Computational Molecular Design (LCMD), Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland
| | - Raphael Wischert
- Eco-Efficient Products and Processes Laboratory, Solvay, RIC Shanghai, Shanghai 201108, China
| | - Laurent Garel
- Aroma Performance Laboratory, Solvay, RIC Lyon, 69190 Saint-Fons, France
| | - Clémence Corminbœuf
- Laboratory for Computational Molecular Design (LCMD), Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland.,National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Michele Ceriotti
- Laboratory of Computational Science and Modeling (COSMO), Institute of Materials, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland.,National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
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47
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Roch LM, Saikin SK, Häse F, Friederich P, Goldsmith RH, León S, Aspuru-Guzik A. From Absorption Spectra to Charge Transfer in Nanoaggregates of Oligomers with Machine Learning. ACS NANO 2020; 14:6589-6598. [PMID: 32338888 DOI: 10.1021/acsnano.0c00384] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Fast and inexpensive characterization of materials properties is a key element to discover novel functional materials. In this work, we suggest an approach employing three classes of Bayesian machine learning (ML) models to correlate electronic absorption spectra of nanoaggregates with the strength of intermolecular electronic couplings in organic conducting and semiconducting materials. As a specific model system, we consider poly(3,4-ethylenedioxythiophene) (PEDOT) polystyrene sulfonate, a cornerstone material for organic electronic applications, and so analyze the couplings between charged dimers of closely packed PEDOT oligomers that are at the heart of the material's unrivaled conductivity. We demonstrate that ML algorithms can identify correlations between the coupling strengths and the electronic absorption spectra. We also show that ML models can be trained to be transferable across a broad range of spectral resolutions and that the electronic couplings can be predicted from the simulated spectra with an 88% accuracy when ML models are used as classifiers. Although the ML models employed in this study were trained on data generated by a multiscale computational workflow, they were able to leverage experimental data.
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Affiliation(s)
- Loïc M Roch
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, United States
- Vector Institute for Artificial Intelligence, Toronto, ON M5S 1M1, Canada
- Department of Chemistry, University of Toronto, Toronto, ON M5S 3H6, Canada
- Department of Computer Science, University of Toronto, Toronto, ON M5S 3H6, Canada
- ChemOS Sàrl, Lausanne, VD 1006, Switzerland
| | - Semion K Saikin
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, United States
- Kebotix, Inc., Cambridge, Massachusetts 02139, United States
| | - Florian Häse
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, United States
- Vector Institute for Artificial Intelligence, Toronto, ON M5S 1M1, Canada
- Department of Chemistry, University of Toronto, Toronto, ON M5S 3H6, Canada
- Department of Computer Science, University of Toronto, Toronto, ON M5S 3H6, Canada
| | - Pascal Friederich
- Department of Chemistry, University of Toronto, Toronto, ON M5S 3H6, Canada
- Department of Computer Science, University of Toronto, Toronto, ON M5S 3H6, Canada
- Institute of Nanotechnology, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen 76344, Germany
| | - Randall H Goldsmith
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Salvador León
- Department of Industrial Chemical Engineering and Environment, Universidad Politécnica de Madrid, Madrid 28006, Spain
| | - Alán Aspuru-Guzik
- Vector Institute for Artificial Intelligence, Toronto, ON M5S 1M1, Canada
- Department of Chemistry, University of Toronto, Toronto, ON M5S 3H6, Canada
- Department of Computer Science, University of Toronto, Toronto, ON M5S 3H6, Canada
- Lebovic Fellow, Canadian Institute for Advanced Research, Toronto, ON M5G 1M1, Canada
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48
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Casier B, Carniato S, Miteva T, Capron N, Sisourat N. Using principal component analysis for neural network high-dimensional potential energy surface. J Chem Phys 2020; 152:234103. [DOI: 10.1063/5.0009264] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Bastien Casier
- Sorbonne Université, CNRS, Laboratoire de Chimie Physique Matière et Rayonnement, UMR 7614, F-75005 Paris, France
| | - Stéphane Carniato
- Sorbonne Université, CNRS, Laboratoire de Chimie Physique Matière et Rayonnement, UMR 7614, F-75005 Paris, France
| | - Tsveta Miteva
- Sorbonne Université, CNRS, Laboratoire de Chimie Physique Matière et Rayonnement, UMR 7614, F-75005 Paris, France
| | - Nathalie Capron
- Sorbonne Université, CNRS, Laboratoire de Chimie Physique Matière et Rayonnement, UMR 7614, F-75005 Paris, France
| | - Nicolas Sisourat
- Sorbonne Université, CNRS, Laboratoire de Chimie Physique Matière et Rayonnement, UMR 7614, F-75005 Paris, France
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49
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Rollins N, Pugh SL, Maley SM, Grant BO, Hamilton RS, Teynor MS, Carlsen R, Jenkins JR, Ess DH. Machine Learning Analysis of Direct Dynamics Trajectory Outcomes for Thermal Deazetization of 2,3-Diazabicyclo[2.2.1]hept-2-ene. J Phys Chem A 2020; 124:4813-4826. [PMID: 32412755 DOI: 10.1021/acs.jpca.9b10410] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Experimentally, the thermal gas-phase deazetization of 2,3-diazabicyclo[2.2.1]hept-2-ene (1) results in the loss of N2 and the formation of bicyclo products 3 (exo) and 4 (endo) in a nonstatistical ratio, with preference for the exo product. Here, we report unrestricted M06-2X quasiclassical trajectories initialized from the concerted N2 ejection transition state that were able to replicate the experimental preference to form 3. We found that the 3:4 ratio results from the relative amounts of very fast (ballistic) exotype trajectories versus trajectories that lead to the 1,3-diradical intermediate 2. These quasiclassical trajectories provided a set of transition-state vibrational, velocity, momenta, and geometric features for the machine learning analysis. A selection of popular supervised classification algorithms (e.g., random forest) provided poor prediction of trajectory outcomes based on only transition-state vibrational quanta and energy features. However, these machine learning models provided more accurate predictions using atomic velocities and atomic positions, attaining ∼70% accuracy using initial conditions and between 85 and 95% accuracy at later reaction time steps. This increased accuracy allowed the feature importance analysis to reveal that, at the later-time analysis, the methylene bridge out-of-plane bending is correlated with trajectory outcomes for the formation of either the exo product or toward the diradical intermediate. Possible reasons for the struggle of machine learning algorithms to classify trajectories based on transition-state features is the heavily overlapping feature values, the finite but very large possible vibrational mode combinations, and the possibility of chaos as trajectories propagate. We examined this chaos by comparing a set of nearly identical trajectories that differed by only a very small scaling of the kinetic energies resulting from the transition-state reaction coordinate.
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Affiliation(s)
- Nick Rollins
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, United States
| | - Samuel L Pugh
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, United States
| | - Steven M Maley
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, United States
| | - Benjamin O Grant
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, United States
| | - R Spencer Hamilton
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, United States
| | - Matthew S Teynor
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, United States
| | - Ryan Carlsen
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, United States
| | - Jordan R Jenkins
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, United States
| | - Daniel H Ess
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, United States
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50
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Aquilante F, Autschbach J, Baiardi A, Battaglia S, Borin VA, Chibotaru LF, Conti I, De Vico L, Delcey M, Fdez Galván I, Ferré N, Freitag L, Garavelli M, Gong X, Knecht S, Larsson ED, Lindh R, Lundberg M, Malmqvist PÅ, Nenov A, Norell J, Odelius M, Olivucci M, Pedersen TB, Pedraza-González L, Phung QM, Pierloot K, Reiher M, Schapiro I, Segarra-Martí J, Segatta F, Seijo L, Sen S, Sergentu DC, Stein CJ, Ungur L, Vacher M, Valentini A, Veryazov V. Modern quantum chemistry with [Open]Molcas. J Chem Phys 2020; 152:214117. [PMID: 32505150 DOI: 10.1063/5.0004835] [Citation(s) in RCA: 226] [Impact Index Per Article: 56.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
MOLCAS/OpenMolcas is an ab initio electronic structure program providing a large set of computational methods from Hartree-Fock and density functional theory to various implementations of multiconfigurational theory. This article provides a comprehensive overview of the main features of the code, specifically reviewing the use of the code in previously reported chemical applications as well as more recent applications including the calculation of magnetic properties from optimized density matrix renormalization group wave functions.
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Affiliation(s)
- Francesco Aquilante
- Theory and Simulation of Materials (THEOS) and National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Jochen Autschbach
- Department of Chemistry, University at Buffalo, Buffalo, New York 14260-3000, USA
| | - Alberto Baiardi
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Stefano Battaglia
- Department of Chemistry - BMC, Uppsala University, P.O. Box 576, SE-751 23 Uppsala, Sweden
| | - Veniamin A Borin
- Fritz Haber Center for Molecular Dynamics Research, Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Liviu F Chibotaru
- Department of Chemistry, KU Leuven, Celestijnenlaan 200F, 3001 Leuven, Belgium
| | - Irene Conti
- Dipartimento di Chimica Industriale "Toso Montanari", Università di Bologna, Viale del Risorgimento 4, Bologna I-40136, Italy
| | - Luca De Vico
- Dipartimento di Biotecnologie, Chimica e Farmacia, Università degli Studi di Siena, via Aldo Moro 2, 53100 Siena, Italy
| | - Mickaël Delcey
- Department of Chemistry - Ångström Laboratory, Uppsala University, SE-751 21 Uppsala, Sweden
| | - Ignacio Fdez Galván
- Department of Chemistry - BMC, Uppsala University, P.O. Box 576, SE-751 23 Uppsala, Sweden
| | - Nicolas Ferré
- Aix-Marseille University, CNRS, Institut Chimie Radicalaire, Marseille, France
| | - Leon Freitag
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Marco Garavelli
- Dipartimento di Chimica Industriale "Toso Montanari", Università di Bologna, Viale del Risorgimento 4, Bologna I-40136, Italy
| | - Xuejun Gong
- Department of Chemistry, University of Singapore, 3 Science Drive 3, 117543 Singapore
| | - Stefan Knecht
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Ernst D Larsson
- Division of Theoretical Chemistry, Lund University, P.O. Box 124, Lund 22100, Sweden
| | - Roland Lindh
- Department of Chemistry - BMC, Uppsala University, P.O. Box 576, SE-751 23 Uppsala, Sweden
| | - Marcus Lundberg
- Department of Chemistry - Ångström Laboratory, Uppsala University, SE-751 21 Uppsala, Sweden
| | - Per Åke Malmqvist
- Division of Theoretical Chemistry, Lund University, P.O. Box 124, Lund 22100, Sweden
| | - Artur Nenov
- Dipartimento di Chimica Industriale "Toso Montanari", Università di Bologna, Viale del Risorgimento 4, Bologna I-40136, Italy
| | - Jesper Norell
- Department of Physics, AlbaNova University Center, Stockholm University, SE-106 91 Stockholm, Sweden
| | - Michael Odelius
- Department of Physics, AlbaNova University Center, Stockholm University, SE-106 91 Stockholm, Sweden
| | - Massimo Olivucci
- Dipartimento di Biotecnologie, Chimica e Farmacia, Università degli Studi di Siena, via Aldo Moro 2, 53100 Siena, Italy
| | - Thomas B Pedersen
- Hylleraas Centre for Quantum Molecular Sciences, Department of Chemistry, University of Oslo, P.O. Box 1033 Blindern, N-0315 Oslo, Norway
| | - Laura Pedraza-González
- Dipartimento di Biotecnologie, Chimica e Farmacia, Università degli Studi di Siena, via Aldo Moro 2, 53100 Siena, Italy
| | - Quan M Phung
- Institute of Transformative Bio-Molecules (WPI-ITbM), Nagoya University, Chikusa, Nagoya 464-8602, Japan
| | - Kristine Pierloot
- Department of Chemistry, KU Leuven, Celestijnenlaan 200F, 3001 Leuven, Belgium
| | - Markus Reiher
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Igor Schapiro
- Fritz Haber Center for Molecular Dynamics Research, Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Javier Segarra-Martí
- Department of Chemistry, Molecular Sciences Research Hub, Imperial College London, White City Campus, 80 Wood Lane, London W12 0BZ, United Kingdom
| | - Francesco Segatta
- Dipartimento di Chimica Industriale "Toso Montanari", Università di Bologna, Viale del Risorgimento 4, Bologna I-40136, Italy
| | - Luis Seijo
- Departamento de Química, Instituto Universitario de Ciencia de Materiales Nicolás Cabrera, and Condensed Matter Physics Center (IFIMAC), Universidad Autónoma de Madrid, 28049 Madrid, Spain
| | - Saumik Sen
- Fritz Haber Center for Molecular Dynamics Research, Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | | | - Christopher J Stein
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Liviu Ungur
- Department of Chemistry, University of Singapore, 3 Science Drive 3, 117543 Singapore
| | - Morgane Vacher
- Laboratoire CEISAM - UMR CNRS 6230, Université de Nantes, 44300 Nantes, France
| | - Alessio Valentini
- Theoretical Physical Chemistry, Research Unit MolSys, Université de Liège, Allée du 6 Août, 11, 4000 Liège, Belgium
| | - Valera Veryazov
- Division of Theoretical Chemistry, Lund University, P.O. Box 124, Lund 22100, Sweden
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