1
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Chen G, Jaffrelot Inizan T, Plé T, Lagardère L, Piquemal JP, Maday Y. Advancing Force Fields Parameterization: A Directed Graph Attention Networks Approach. J Chem Theory Comput 2024; 20:5558-5569. [PMID: 38875012 DOI: 10.1021/acs.jctc.3c01421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2024]
Abstract
Force fields (FFs) are an established tool for simulating large and complex molecular systems. However, parametrizing FFs is a challenging and time-consuming task that relies on empirical heuristics, experimental data, and computational data. Recent efforts aim to automate the assignment of FF parameters using pre-existing databases and on-the-fly ab initio data. In this study, we propose a graph-based force field (GB-FFs) model to directly derive parameters for the Generalized Amber Force Field (GAFF) from chemical environments and research into the influence of functional forms. Our end-to-end parametrization approach predicts parameters by aggregating the basic information in directed molecular graphs, eliminating the need for expert-defined procedures and enhances the accuracy and transferability of GAFF across a broader range of molecular complexes. Simulation results are compared to the original GAFF parametrization. In practice, our results demonstrate an improved transferability of the model, showcasing its improved accuracy in modeling intermolecular and torsional interactions, as well as improved solvation free energies. The optimization approach developed in this work is fully applicable to other nonpolarizable FFs as well as to polarizable ones.
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Affiliation(s)
- Gong Chen
- Sorbonne Université, CNRS, Université Paris Cité, Laboratoire Jacques-Louis Lions (LJLL), UMR 7598 CNRS, 75005 Paris, France
| | - Théo Jaffrelot Inizan
- Sorbonne Université, Laboratoire de Chimie Théorique (LCT), UMR 7616 CNRS, 75005 Paris, France
| | - Thomas Plé
- Sorbonne Université, Laboratoire de Chimie Théorique (LCT), UMR 7616 CNRS, 75005 Paris, France
| | - Louis Lagardère
- Sorbonne Université, Laboratoire de Chimie Théorique (LCT), UMR 7616 CNRS, 75005 Paris, France
| | - Jean-Philip Piquemal
- Sorbonne Université, Laboratoire de Chimie Théorique (LCT), UMR 7616 CNRS, 75005 Paris, France
| | - Yvon Maday
- Sorbonne Université, CNRS, Université Paris Cité, Laboratoire Jacques-Louis Lions (LJLL), UMR 7598 CNRS, 75005 Paris, France
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2
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Liu J, Sun R, Bao X, Yang J, Chen Y, Tang B, Liu Z. Machine Learning Driven Atom-Thin Materials for Fragrance Sensing. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024:e2401066. [PMID: 38973110 DOI: 10.1002/smll.202401066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 06/05/2024] [Indexed: 07/09/2024]
Abstract
Fragrance plays a crucial role in the daily lives. Its importance spans various sectors, from therapeutic purposes to personal care, making the understanding and accurate identification of fragrances essential. To fully harness the potential of fragrances, efficient and precise fragrance sensing and identification are necessary. However, current fragrance sensors face several limitations, particularly in detecting and differentiating complex scent profiles with high accuracy. To address these challenges, the use of atom-thin materials in fragrance sensors has emerged as a groundbreaking approach. These atom-thin sensors, characterized by their enhanced sensitivity and selectivity, offer significant improvements over traditional sensing technology. Moreover, the integration of Machine Learning (ML) into fragrance sensing has opened new opportunities in the field. ML algorithms applied to fragrance sensing facilitate advancements in four key domains: accurate fragrance identification, precise discrimination between different fragrances, improved detection thresholds for subtle scents, and prediction of fragrance properties. This comprehensive review delves into the synergistic use of atom-thin materials and ML in fragrance sensing, providing an in-depth analysis of how these technologies are revolutionizing the field, offering insights into their current applications and future potential in enhancing the understanding and utilization of fragrances.
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Affiliation(s)
- Juanjuan Liu
- College of Landscape Architecture and Horticulture, Southwest Forestry University, Kunming, 650224, China
| | - Ruijia Sun
- School of Materials Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore
| | - Xuan Bao
- College of Landscape Architecture and Horticulture, Southwest Forestry University, Kunming, 650224, China
| | - Jiefu Yang
- School of Materials Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore
| | - Yanling Chen
- College of Landscape Architecture and Horticulture, Southwest Forestry University, Kunming, 650224, China
| | - Bijun Tang
- School of Materials Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore
| | - Zheng Liu
- School of Materials Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore
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3
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Bowles J, Jähnigen S, Agostini F, Vuilleumier R, Zehnacker A, Calvo F, Clavaguéra C. Vibrational Circular Dichroism Spectroscopy with a Classical Polarizable Force Field: Alanine in the Gas and Condensed Phases. Chemphyschem 2024; 25:e202300982. [PMID: 38318765 DOI: 10.1002/cphc.202300982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 02/05/2024] [Accepted: 02/06/2024] [Indexed: 02/07/2024]
Abstract
Polarizable force fields are an essential component for the chemically accurate modeling of complex molecular systems with a significant degree of fluxionality, beyond harmonic or perturbative approximations. In this contribution we examine the performance of such an approach for the vibrational spectroscopy of the alanine amino acid, in the gas and condensed phases, from the Fourier transform of appropriate time correlation functions generated along molecular dynamics (MD) trajectories. While the infrared (IR) spectrum only requires the electric dipole moment, the vibrational circular dichroism (VCD) spectrum further requires knowledge of the magnetic dipole moment, for which we provide relevant expressions to be used with polarizable force fields. The AMOEBA force field was employed here to model alanine in the neutral and zwitterionic isolated forms, solvated by water or nitrogen, and as a crystal. Within this framework, comparison of the electric and magnetic dipole moments to those obtained with nuclear velocity perturbation theory based on density-functional theory for the same MD trajectories are found to agree well with one another. The statistical convergence of the IR and VCD spectra is examined and found to be more demanding in the latter case. Comparisons with experimental frequencies are also provided for the condensed phases.
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Affiliation(s)
- Jessica Bowles
- Université Paris-Saclay, CNRS, Institut de Chimie Physique UMR8000, 91405, Orsay, France
| | - Sascha Jähnigen
- PASTEUR Laboratory, Département de Chimie, Ecole Normale Supérieure, PSL University, Sorbonne Université, CNRS, 75005, Paris, France
| | - Federica Agostini
- Université Paris-Saclay, CNRS, Institut de Chimie Physique UMR8000, 91405, Orsay, France
| | - Rodolphe Vuilleumier
- PASTEUR Laboratory, Département de Chimie, Ecole Normale Supérieure, PSL University, Sorbonne Université, CNRS, 75005, Paris, France
| | - Anne Zehnacker
- Université Paris-Saclay, CNRS, Institut des Sciences Moléculaires d'Orsay UMR8214, 91405, Orsay, France
| | - Florent Calvo
- Université Grenoble Alpes, CNRS, LIPhy, 38000, Grenoble, France
| | - Carine Clavaguéra
- Université Paris-Saclay, CNRS, Institut de Chimie Physique UMR8000, 91405, Orsay, France
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4
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Dral PO, Ge F, Hou YF, Zheng P, Chen Y, Barbatti M, Isayev O, Wang C, Xue BX, Pinheiro Jr M, Su Y, Dai Y, Chen Y, Zhang L, Zhang S, Ullah A, Zhang Q, Ou Y. MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows. J Chem Theory Comput 2024; 20:1193-1213. [PMID: 38270978 PMCID: PMC10867807 DOI: 10.1021/acs.jctc.3c01203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 12/29/2023] [Accepted: 01/03/2024] [Indexed: 01/26/2024]
Abstract
Machine learning (ML) is increasingly becoming a common tool in computational chemistry. At the same time, the rapid development of ML methods requires a flexible software framework for designing custom workflows. MLatom 3 is a program package designed to leverage the power of ML to enhance typical computational chemistry simulations and to create complex workflows. This open-source package provides plenty of choice to the users who can run simulations with the command-line options, input files, or with scripts using MLatom as a Python package, both on their computers and on the online XACS cloud computing service at XACScloud.com. Computational chemists can calculate energies and thermochemical properties, optimize geometries, run molecular and quantum dynamics, and simulate (ro)vibrational, one-photon UV/vis absorption, and two-photon absorption spectra with ML, quantum mechanical, and combined models. The users can choose from an extensive library of methods containing pretrained ML models and quantum mechanical approximations such as AIQM1 approaching coupled-cluster accuracy. The developers can build their own models using various ML algorithms. The great flexibility of MLatom is largely due to the extensive use of the interfaces to many state-of-the-art software packages and libraries.
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Affiliation(s)
- Pavlo O. Dral
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and Innovation Laboratory for
Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
- Fujian
Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen, Fujian 361005, China
| | - Fuchun Ge
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and Innovation Laboratory for
Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
- Fujian
Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen, Fujian 361005, China
| | - Yi-Fan Hou
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and Innovation Laboratory for
Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
- Fujian
Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen, Fujian 361005, China
| | - Peikun Zheng
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and Innovation Laboratory for
Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
- Fujian
Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen, Fujian 361005, China
| | - Yuxinxin Chen
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and Innovation Laboratory for
Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
- Fujian
Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen, Fujian 361005, China
| | - Mario Barbatti
- Aix
Marseille University, CNRS, ICR, Marseille 13013, France
- Institut
Universitaire de France, Paris 75231, France
| | - Olexandr Isayev
- Department
of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania15213, United States
| | - Cheng Wang
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and Innovation Laboratory for
Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
- iChem, Xiamen University, Xiamen, Fujian 361005, China
| | - Bao-Xin Xue
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and Innovation Laboratory for
Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
- Fujian
Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen, Fujian 361005, China
| | - Max Pinheiro Jr
- Aix
Marseille University, CNRS, ICR, Marseille 13013, France
| | - Yuming Su
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and Innovation Laboratory for
Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
- iChem, Xiamen University, Xiamen, Fujian 361005, China
| | - Yiheng Dai
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and Innovation Laboratory for
Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
- iChem, Xiamen University, Xiamen, Fujian 361005, China
| | - Yangtao Chen
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and Innovation Laboratory for
Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
- iChem, Xiamen University, Xiamen, Fujian 361005, China
| | - Lina Zhang
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and Innovation Laboratory for
Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
- Fujian
Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen, Fujian 361005, China
| | - Shuang Zhang
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and Innovation Laboratory for
Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
- Fujian
Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen, Fujian 361005, China
| | - Arif Ullah
- School
of Physics and Optoelectronic Engineering, Anhui University, Hefei230601, China
| | - Quanhao Zhang
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and Innovation Laboratory for
Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
- Fujian
Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen, Fujian 361005, China
| | - Yanchi Ou
- State
Key Laboratory of Physical Chemistry of Solid Surfaces, College of
Chemistry and Chemical Engineering, and Innovation Laboratory for
Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
- Fujian
Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen, Fujian 361005, China
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5
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Coste A, Slejko E, Zavadlav J, Praprotnik M. Developing an Implicit Solvation Machine Learning Model for Molecular Simulations of Ionic Media. J Chem Theory Comput 2024; 20:411-420. [PMID: 38118122 PMCID: PMC10782447 DOI: 10.1021/acs.jctc.3c00984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 12/04/2023] [Accepted: 12/04/2023] [Indexed: 12/22/2023]
Abstract
Molecular dynamics (MD) simulations of biophysical systems require accurate modeling of their native environment, i.e., aqueous ionic solution, as it critically impacts the structure and function of biomolecules. On the other hand, the models should be computationally efficient to enable simulations of large spatiotemporal scales. Here, we present the deep implicit solvation model for sodium chloride solutions that satisfies both requirements. Owing to the use of the neural network potential, the model can capture the many-body potential of mean force, while the implicit water treatment renders the model inexpensive. We demonstrate our approach first for pure ionic solutions with concentrations ranging from physiological to 2 M. We then extend the model to capture the effective ion interactions in the vicinity and far away from a DNA molecule. In both cases, the structural properties are in good agreement with all-atom MD, showcasing a general methodology for the efficient and accurate modeling of ionic media.
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Affiliation(s)
- Amaury Coste
- Laboratory
for Molecular Modeling, National Institute of Chemistry, Ljubljana SI-1001, Slovenia
| | - Ema Slejko
- Laboratory
for Molecular Modeling, National Institute of Chemistry, Ljubljana SI-1001, Slovenia
- Department
of Physics, Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana SI-1000, Slovenia
| | - Julija Zavadlav
- Professorship
of Multiscale Modeling of Fluid Materials, TUM School of Engineering
and Design, Technical University of Munich, Garching Near Munich DE-85748, Germany
| | - Matej Praprotnik
- Laboratory
for Molecular Modeling, National Institute of Chemistry, Ljubljana SI-1001, Slovenia
- Department
of Physics, Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana SI-1000, Slovenia
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6
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Jensen AB, Højlund MG, Zoccante A, Madsen NK, Christiansen O. Efficient time-dependent vibrational coupled cluster computations with time-dependent basis sets at the two-mode coupling level: Full and hybrid TDMVCC[2]. J Chem Phys 2023; 159:204106. [PMID: 38010335 DOI: 10.1063/5.0175506] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 11/05/2023] [Indexed: 11/29/2023] Open
Abstract
The computation of the nuclear quantum dynamics of molecules is challenging, requiring both accuracy and efficiency to be applicable to systems of interest. Recently, theories have been developed for employing time-dependent basis functions (denoted modals) with vibrational coupled cluster theory (TDMVCC). The TDMVCC method was introduced along with a pilot implementation, which illustrated good accuracy in benchmark computations. In this paper, we report an efficient implementation of TDMVCC, covering the case where the wave function and Hamiltonian contain up to two-mode couplings. After a careful regrouping of terms, the wave function can be propagated with a cubic computational scaling with respect to the number of degrees of freedom. We discuss the use of a restricted set of active one-mode basis functions for each mode, as well as two interesting limits: (i) the use of a full active basis where the variational modal determination amounts essentially to the variational determination of a time-dependent reference state for the cluster expansion; and (ii) the use of a single function as an active basis for some degrees of freedom. The latter case defines a hybrid TDMVCC/TDH (time-dependent Hartree) approach that can obtain even lower computational scaling. The resulting computational scaling for hybrid and full TDMVCC[2] is illustrated for polyaromatic hydrocarbons with up to 264 modes. Finally, computations on the internal vibrational redistribution of benzoic acid (39 modes) are used to show the faster convergence of TDMVCC/TDH hybrid computations towards TDMVCC compared to simple neglect of some degrees of freedom.
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Affiliation(s)
| | - Mads Greisen Højlund
- Department of Chemistry, Aarhus University, Langelandsgade 140, DK-8000 Aarhus C, Denmark
| | - Alberto Zoccante
- Dipartimento di Scienze e Innovazione Tecnologica, Università del Piemonte Orientale (UPO), Via T. Michel 11, 15100 Alessandria, Italy
| | - Niels Kristian Madsen
- Department of Chemistry, Aarhus University, Langelandsgade 140, DK-8000 Aarhus C, Denmark
| | - Ove Christiansen
- Department of Chemistry, Aarhus University, Langelandsgade 140, DK-8000 Aarhus C, Denmark
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7
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Plé T, Lagardère L, Piquemal JP. Force-field-enhanced neural network interactions: from local equivariant embedding to atom-in-molecule properties and long-range effects. Chem Sci 2023; 14:12554-12569. [PMID: 38020379 PMCID: PMC10646944 DOI: 10.1039/d3sc02581k] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 10/03/2023] [Indexed: 12/01/2023] Open
Abstract
We introduce FENNIX (Force-Field-Enhanced Neural Network InteraXions), a hybrid approach between machine-learning and force-fields. We leverage state-of-the-art equivariant neural networks to predict local energy contributions and multiple atom-in-molecule properties that are then used as geometry-dependent parameters for physically-motivated energy terms which account for long-range electrostatics and dispersion. Using high-accuracy ab initio data (small organic molecules/dimers), we trained a first version of the model. Exhibiting accurate gas-phase energy predictions, FENNIX is transferable to the condensed phase. It is able to produce stable Molecular Dynamics simulations, including nuclear quantum effects, for water predicting accurate liquid properties. The extrapolating power of the hybrid physically-driven machine learning FENNIX approach is exemplified by computing: (i) the solvated alanine dipeptide free energy landscape; (ii) the reactive dissociation of small molecules.
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Affiliation(s)
- Thomas Plé
- Sorbonne Université, LCT, UMR 7616 CNRS F-75005 Paris France thomas.ple@sorbonne-université louis.lagardere@sorbonne-université jean-philip.piquemal@sorbonne-université
| | - Louis Lagardère
- Sorbonne Université, LCT, UMR 7616 CNRS F-75005 Paris France thomas.ple@sorbonne-université louis.lagardere@sorbonne-université jean-philip.piquemal@sorbonne-université
| | - Jean-Philip Piquemal
- Sorbonne Université, LCT, UMR 7616 CNRS F-75005 Paris France thomas.ple@sorbonne-université louis.lagardere@sorbonne-université jean-philip.piquemal@sorbonne-université
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8
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Feldman YMY, Hirshberg B. Quadratic scaling bosonic path integral molecular dynamics. J Chem Phys 2023; 159:154107. [PMID: 37855315 DOI: 10.1063/5.0173749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 09/15/2023] [Indexed: 10/20/2023] Open
Abstract
Bosonic exchange symmetry leads to fascinating quantum phenomena, from exciton condensation in quantum materials to the superfluidity of liquid 4He. Unfortunately, path integral molecular dynamics (PIMD) simulations of bosons are computationally prohibitive beyond ∼100 particles, due to a cubic scaling with the system size. We present an algorithm that reduces the complexity from cubic to quadratic, allowing the first simulations of thousands of bosons using PIMD. Our method is orders of magnitude faster, with a speedup that scales linearly with the number of particles and the number of imaginary time slices (beads). Simulations that would have otherwise taken decades can now be done in days. In practice, the new algorithm eliminates most of the added computational cost of including bosonic exchange effects, making them almost as accessible as PIMD simulations of distinguishable particles.
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Affiliation(s)
- Yotam M Y Feldman
- School of Chemistry, Tel Aviv University, Tel Aviv 6997801, Israel
- The Ratner Center for Single Molecule Science, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Barak Hirshberg
- School of Chemistry, Tel Aviv University, Tel Aviv 6997801, Israel
- The Ratner Center for Single Molecule Science, Tel Aviv University, Tel Aviv 6997801, Israel
- The Center for Computational Molecular and Materials Science, Tel Aviv University, Tel Aviv 6997801, Israel
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9
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Prlj A, Hollas D, Curchod BFE. Deciphering the Influence of Ground-State Distributions on the Calculation of Photolysis Observables. J Phys Chem A 2023; 127:7400-7409. [PMID: 37556330 PMCID: PMC10493954 DOI: 10.1021/acs.jpca.3c02333] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 07/17/2023] [Indexed: 08/11/2023]
Abstract
Nonadiabatic molecular dynamics offers a powerful tool for studying the photochemistry of molecular systems. Key to any nonadiabatic molecular dynamics simulation is the definition of its initial conditions (ICs), ideally representing the initial molecular quantum state of the system of interest. In this work, we provide a detailed analysis of how ICs may influence the calculation of experimental observables by focusing on the photochemistry of methylhydroperoxide (MHP), the simplest and most abundant organic peroxide in our atmosphere. We investigate the outcome of trajectory surface hopping simulations for distinct sets of ICs sampled from different approximate quantum distributions, namely harmonic Wigner functions and ab initio molecular dynamics using a quantum thermostat (QT). Calculating photoabsorption cross-sections, quantum yields, and translational kinetic energy maps from the results of these simulations reveals the significant effect of the ICs, in particular when low-frequency (∼ a few hundred cm-1) normal modes are connected to the photophysics of the molecule. Overall, our results indicate that sampling ICs from ab initio molecular dynamics using a QT is preferable for flexible molecules with photoactive low-frequency modes. From a photochemical perspective, our nonadiabatic dynamics simulations offer an explanation for a low-energy tail observed at high excitation energy in the translational kinetic energy map of MHP.
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Affiliation(s)
- Antonio Prlj
- Centre
for Computational Chemistry, School of Chemistry, University of Bristol, Bristol BS8 1TS, U.K.
- Division
of Physical Chemistry, Ruđer Bošković
Institute, Zagreb 10000, Croatia
| | - Daniel Hollas
- Centre
for Computational Chemistry, School of Chemistry, University of Bristol, Bristol BS8 1TS, U.K.
| | - Basile F. E. Curchod
- Centre
for Computational Chemistry, School of Chemistry, University of Bristol, Bristol BS8 1TS, U.K.
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10
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Frank I. Nuclear Motion Is Classical: Spectrum of a Magic Protonated Water Cluster. Molecules 2023; 28:6454. [PMID: 37764233 PMCID: PMC10534396 DOI: 10.3390/molecules28186454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 09/01/2023] [Accepted: 09/04/2023] [Indexed: 09/29/2023] Open
Abstract
The assumption that nuclear motion is classical explains many phenomena. The problems of Schrödinger's cat and the EPR paradoxon do not exist in a perfectly deterministic theory. All it needs is to describe nuclear motion classically right from the beginning. To establish this simple idea, it must be tested for as many examples as possible. In the present paper, we use ab initio molecular dynamics to investigate the infrared spectrum of a 'magic' protonated water cluster H3O+(H2O)20 which exhibits some features that were believed to afford a quantum treatment of nuclear motion. The role of the temperature in contrast to a quantum mechanical description is discussed.
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Affiliation(s)
- Irmgard Frank
- Theoretical Chemistry, Leibniz University Hannover, Callinstr. 3A, 30167 Hannover, Germany
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11
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Barbiero D, Bertaina G, Ceotto M, Conte R. Anharmonic Assignment of the Water Octamer Spectrum in the OH Stretch Region. J Phys Chem A 2023; 127:6213-6221. [PMID: 37477983 PMCID: PMC10405218 DOI: 10.1021/acs.jpca.3c02902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 07/03/2023] [Indexed: 07/23/2023]
Abstract
We interface the quasi-classical trajectory approach with an ab initio potential energy surface for water to assign the vibrational spectroscopical features of the OH stretch region of the water octamer cluster, which is considered to be a precursor of ice. An attempt by Li et al. to assign their recent reference experiment involved lower-level calculations based on an ad hoc scaled harmonic approach. Differently from the conclusions of this previous assignment, which invoked the contribution of 5 conformers and a solvated form of the water heptamer in the spectrum, we find out that the spectroscopic features can be related to the 4 conformers of the octamer lying lower in energy.
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Affiliation(s)
- Davide Barbiero
- Dipartimento
di Chimica, Università degli Studi
di Milano, via Golgi 19, 20133 Milano, Italy
| | - Gianluca Bertaina
- Istituto
Nazionale di Ricerca Metrologica, Strada delle Cacce 91, I-10135 Torino, Italy
| | - Michele Ceotto
- Dipartimento
di Chimica, Università degli Studi
di Milano, via Golgi 19, 20133 Milano, Italy
| | - Riccardo Conte
- Dipartimento
di Chimica, Università degli Studi
di Milano, via Golgi 19, 20133 Milano, Italy
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