1
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Kushwaha A, Chahal P, Dhilip Kumar TJ. Rotational dynamics of CNCN by p-H2 and o-H2 collision at interstellar temperatures. J Chem Phys 2024; 161:064302. [PMID: 39120031 DOI: 10.1063/5.0220608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Accepted: 07/23/2024] [Indexed: 08/10/2024] Open
Abstract
The rotational dynamics of isocyanogen (CNCN) is studied for its collision with para (p-) and ortho (o-) hydrogen (H2) in the temperature range of 1-100 K. These temperatures correspond to the cold dense molecular clouds in the interstellar medium where molecular hydrogen is the primary collider. An ab initio 4D potential energy surface (PES) is constructed keeping the two molecules under rigid rotor approximation. The PES is generated using the CCSD(T)-F12b/AVTZ level of theory. The 4D PES is further fitted into a neural network (NN) model, which can augment the surface and account for missing data points within spectroscopic accuracy. This NN-fitted PES is then expanded over a bispherical harmonics function to get radial terms, which are expressed into analytic functions. Thereafter, the cross sections (σ) are computed for rotational transitions of CNCN (j → j') using the close-coupling and centrifugal sudden methods for both p-H2 (jc = 0) and o-H2 (jc = 1) collision till 194 cm-1. In addition, p-H2 (jc = 0, 2) cross sections are also computed using the centrifugal sudden approximation method. The collisional rates are achieved by taking the Boltzmann distribution of σ over the translational energy of H2 till 100 K. Finally, the CNCN-H2 rates are compared to CNCN-He and NCCN-H2 collisional rates. Comparing even and odd transitions for the CNCN-H2 rates show a propensity toward higher rates for even transitions especially for o-H2 collisions considering low-order transitions.
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Affiliation(s)
- Apoorv Kushwaha
- Quantum Dynamics Lab, Department of Chemistry, Indian Institute of Technology Ropar, Rupnagar 140001, India
| | - Pooja Chahal
- Quantum Dynamics Lab, Department of Chemistry, Indian Institute of Technology Ropar, Rupnagar 140001, India
| | - T J Dhilip Kumar
- Quantum Dynamics Lab, Department of Chemistry, Indian Institute of Technology Ropar, Rupnagar 140001, India
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2
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Berquist E, Dumi A, Upadhyay S, Abarbanel OD, Cho M, Gaur S, Cano Gil VH, Hutchison GR, Lee OS, Rosen AS, Schamnad S, Schneider FSS, Steinmann C, Stolyarchuk M, Vandezande JE, Zak W, Langner KM. cclib 2.0: An updated architecture for interoperable computational chemistry. J Chem Phys 2024; 161:042501. [PMID: 39051837 DOI: 10.1063/5.0216778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Accepted: 07/01/2024] [Indexed: 07/27/2024] Open
Abstract
Interoperability in computational chemistry is elusive, impeded by the independent development of software packages and idiosyncratic nature of their output files. The cclib library was introduced in 2006 as an attempt to improve this situation by providing a consistent interface to the results of various quantum chemistry programs. The shared API across programs enabled by cclib has allowed users to focus on results as opposed to output and to combine data from multiple programs or develop generic downstream tools. Initial development, however, did not anticipate the rapid progress of computational capabilities, novel methods, and new programs; nor did it foresee the growing need for customizability. Here, we recount this history and present cclib 2, focused on extensibility and modularity. We also introduce recent design pivots-the formalization of cclib's intermediate data representation as a tree-based structure, a new combinator-based parser organization, and parsed chemical properties as extensible objects.
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Affiliation(s)
- Eric Berquist
- Sandia National Laboratories, Albuquerque, New Mexico 87185, USA
| | - Amanda Dumi
- Sandia National Laboratories, Albuquerque, New Mexico 87185, USA
| | - Shiv Upadhyay
- Department of Chemistry, University of Washington, Seattle, Washington 98195, USA
| | - Omri D Abarbanel
- Department of Chemistry, University of Pittsburgh, 219 Parkman Avenue, Pittsburgh, Pennsylvania 15260, USA
| | - Minsik Cho
- Department of Chemistry, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, USA
| | - Sagar Gaur
- MarkovML 23, Geary St. Suite 600, San Francisco, California 94108, USA
- International Institute of Information Technology, Prof. CR Rao Road Gachibowli, Hyderabad 500032, Telangana, India
| | | | - Geoffrey R Hutchison
- Department of Chemistry, University of Pittsburgh, 219 Parkman Avenue, Pittsburgh, Pennsylvania 15260, USA
| | - Oliver S Lee
- Organic Semiconductor Centre, EaStCHEM School of Chemistry, University of St Andrews, St. Andrews KY16 9ST, United Kingdom
- Organic Semiconductor Centre, SUPA School of Physics and Astronomy, University of St Andrews, St. Andrews KY16 9SS, United Kingdom
| | - Andrew S Rosen
- Sandia National Laboratories, Albuquerque, New Mexico 87185, USA
- Department of Materials Science and Engineering, University of California, Berkeley, California 94720, USA
| | | | | | - Casper Steinmann
- Department of Chemistry and Bioscience, Aalborg University, DK-9230 Aalborg, Denmark
| | | | | | - Weronika Zak
- Department of Computer Science, Loughborough University, Epinal Way, Loughborough, Leicestershire LE11 3TU, United Kingdom
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3
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Yang J, Li J, Li J, Li J. Gaussian Process Regression for State-to-State Integral Cross Sections: The Case of the O + O 2 Collision Dissociation Reactions. J Phys Chem A 2024; 128:4966-4975. [PMID: 38869143 DOI: 10.1021/acs.jpca.4c01445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2024]
Abstract
Research on hypersonic vehicles has become increasingly important worldwide in recent years. However, accurately simulating the dynamics of the nonequilibrium high-temperature reactions that are in the hypersonic flow around the vehicles presents a significant challenge as a large number of states and transitions are accessible even for the smallest atom-diatom reaction systems. It is quite difficult, sometimes even impossible, to exhaustively investigate all relevant combinations or determine high-dimensional analytical representations for the state-to-state reaction probabilities. In this study, we used Gaussian process regression (GPR) to fit a model based on only 807 QCT data for training. The confidence interval of the GPR prediction and the Kullback-Leibler (KL) divergence were used to help minimize the sampling amount of data for fitting the converged GPR model. The model aims to predict the state-to-state integral cross section (ICS) of the O + O2 → 3O dissociation reaction under random initial conditions (Et, v, j). In total, it took almost a month to obtain this converged GPR model, but it took only a few seconds to predict the ICS value for any initial condition. For 330 initial conditions not included in the training set, the mean-square error (MSE) between the QCT-calculated ICSs and the GPR-predicted ones is only 0.08 Å2 and the R2 is 0.9986, indicating that the GPR model can replace the direct expensive QCT calculation with high accuracy. Finally, we calculated the equilibrium dissociation rate coefficients based on the StS ICS values predicted by the GPR model, and the results were in good agreement with available experimental and theoretical results. Thus, this study provides an effective and accurate approach to the extensive direct state-to-state reaction dynamic calculations.
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Affiliation(s)
- Jiawei Yang
- School of Chemistry and Chemical Engineering & Chongqing Key Laboratory of Chemical Theory and Mechanism, Chongqing University, Chongqing 401331, China
| | - Jia Li
- School of Chemistry and Chemical Engineering & Chongqing Key Laboratory of Chemical Theory and Mechanism, Chongqing University, Chongqing 401331, China
| | - Junhong Li
- School of Chemistry and Chemical Engineering & Chongqing Key Laboratory of Chemical Theory and Mechanism, Chongqing University, Chongqing 401331, China
| | - Jun Li
- School of Chemistry and Chemical Engineering & Chongqing Key Laboratory of Chemical Theory and Mechanism, Chongqing University, Chongqing 401331, China
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4
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Schröder B, Rauhut G. From the Automated Calculation of Potential Energy Surfaces to Accurate Infrared Spectra. J Phys Chem Lett 2024; 15:3159-3169. [PMID: 38478898 PMCID: PMC10961845 DOI: 10.1021/acs.jpclett.4c00186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 02/20/2024] [Accepted: 02/28/2024] [Indexed: 03/22/2024]
Abstract
Advances in the development of quantum chemical methods and progress in multicore architectures in computer science made the simulation of infrared spectra of isolated molecules competitive with respect to established experimental methods. Although it is mainly the multidimensional potential energy surface that controls the accuracy of these calculations, the subsequent vibrational structure calculations need to be carefully converged in order to yield accurate results. As both aspects need to be considered in a balanced way, we focus on approaches for molecules of up to 12-15 atoms with respect to both parts, which have been automated to some extent so that they can be employed in routine applications. Alternatives to machine learning will be discussed, which appear to be attractive, as long as local regions of the potential energy surface are sufficient. The automatization of these methods is still in its infancy, and the generalization to molecules with large amplitude motions or molecular clusters is far from trivial, but many systems relevant for astrophysical studies are already in reach.
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Affiliation(s)
- Benjamin Schröder
- Institute
of Physical Chemistry, University of Goettingen, Tammannstrasse 6, Göttingen 37077, Germany
| | - Guntram Rauhut
- Institute
for Theoretical Chemistry, University of
Stuttgart, Pfaffenwaldring 55, Stuttgart 70569, Germany
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5
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Izvekov S, Kroonblawd MP, Larentzos JP, Brennan JK, Rice BM. Maximum Entropy Theory of Multiscale Coarse-Graining via Matching Thermodynamic Forces: Application to a Molecular Crystal (TATB). J Phys Chem B 2024. [PMID: 38489758 DOI: 10.1021/acs.jpcb.3c07078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2024]
Abstract
The MSCG/FM (multiscale coarse-graining via force-matching) approach is an efficient supervised machine learning method to develop microscopically informed coarse-grained (CG) models. We present a theory based on the principle of maximum entropy (PME) enveloping the existing MSCG/FM approaches. This theory views the MSCG/FM method as a special case of matching the thermodynamic forces from the extended ensemble described by the set of thermodynamic (relevant) system coordinates. This set may include CG coordinates, the stress tensor, applied external fields, and so forth, and may be characterized by nonequilibrium conditions. Following the presentation of the theory, we discuss the consistent matching of both bonded and nonbonded interactions. The proposed PME formulation is used as a starting point to extend the MSCG/FM method to the constant strain ensemble, which together with the explicit matching of the bonded forces is better suited for coarse-graining anisotropic media at a submolecular resolution. The theory is demonstrated by performing the fine coarse-graining of crystalline 1,3,5-triamino-2,4,6-trinitrobenzene (TATB), a well-known insensitive molecular energetic material, which exhibits highly anisotropic mechanical properties.
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Affiliation(s)
- Sergei Izvekov
- U.S. Army DEVCOM Army Research Laboratory, Aberdeen Proving Ground, Maryland 21005, United States
| | - Matthew P Kroonblawd
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States
| | - James P Larentzos
- U.S. Army DEVCOM Army Research Laboratory, Aberdeen Proving Ground, Maryland 21005, United States
| | - John K Brennan
- U.S. Army DEVCOM Army Research Laboratory, Aberdeen Proving Ground, Maryland 21005, United States
| | - Betsy M Rice
- U.S. Army DEVCOM Army Research Laboratory, Aberdeen Proving Ground, Maryland 21005, United States
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6
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Chahal P, Kushwaha A, Dhilip Kumar TJ. Quantum rotational dynamics of l-C 4( 3Σ-g) by H 2 at low temperatures employing a machine learning augmented potential energy surface. Phys Chem Chem Phys 2024; 26:7482-7491. [PMID: 38354053 DOI: 10.1039/d3cp05424a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
A new four dimensional (4D) ab initio potential energy surface (PES) is generated for the collision of C4(3Σg-) with H2(1Σg), considering both molecules as rigid rotors. A supervised neural network model is created to augment the ab initio PES and to get the missing data points. Furthermore, space fixed expansion of the augmented PES is carried out using a least squares fit over two spherical harmonics terms, resulting in radial coefficients (λ1, λ2, and λ). The centre of symmetry in both C4 and H2 forces λ1 and λ2 to have even values, respectively. Moreover, the rotational states of C4 are only populated by odd levels due to its ground state triplet symmetry and the nuclear spin (I = 0) of 12C. The cross-sections and rate coefficients with para and ortho H2 partners are studied for various odd state transitions, where the rate coefficients of the ortho are 10-20% higher than those of the latter. The de-excitation rates obtained by the para H2 collisions are also compared to those of He and are found to be ∼1.7-2.8 times the He rates, across various order transitions. The simple scaling of He rates using a factor of 1.38 proves insufficient to describe para H2 rates. Therefore, these results show the importance of explicitly studying H2 as an important colliding partner, governing the kinetics of various rotational processes in the interstellar space.
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Affiliation(s)
- Pooja Chahal
- Quantum Dynamics Lab, Department of Chemistry, Indian Institute of Technology, Ropar, Rupnagar 140001, India.
| | - Apoorv Kushwaha
- Quantum Dynamics Lab, Department of Chemistry, Indian Institute of Technology, Ropar, Rupnagar 140001, India.
| | - T J Dhilip Kumar
- Quantum Dynamics Lab, Department of Chemistry, Indian Institute of Technology, Ropar, Rupnagar 140001, India.
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7
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Montes de Oca-Estévez MJ, Valdés Á, Prosmiti R. A kernel-based machine learning potential and quantum vibrational state analysis of the cationic Ar hydride (Ar 2H +). Phys Chem Chem Phys 2024; 26:7060-7071. [PMID: 38345626 DOI: 10.1039/d3cp05865d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
One of the most fascinating discoveries in recent years, in the cold and low pressure regions of the universe, was the detection of ArH+ and HeH+ species. The identification of such noble gas-containing molecules in space is the key to understanding noble gas chemistry. In the present work, we discuss the possibility of [Ar2H]+ existence as a potentially detectable molecule in the interstellar medium, providing new data on possible astronomical pathways and energetics of this compound. As a first step, a data-driven approach is proposed to construct a full 3D machine-learning potential energy surface (ML-PES) via the reproducing kernel Hilbert space (RKHS) method. The training and testing data sets are generated from CCSD(T)/CBS[56] computations, while a validation protocol is introduced to ensure the quality of the potential. In turn, the resulting ML-PES is employed to compute vibrational levels and molecular spectroscopic constants for the cation. In this way, the most common isotopologue in ISM, [36Ar2H]+, was characterized for the first time, while simultaneously, comparisons with previously reported values available for [40Ar2H]+ are discussed. Our present data could serve as a benchmark for future studies on this system, as well as on higher-order cationic Ar-hydrides of astrophysical interest.
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Affiliation(s)
- María Judit Montes de Oca-Estévez
- Institute of Fundamental Physics (IFF-CSIC), CSIC, Serrano 123, 28006 Madrid, Spain.
- Atelgraphics S.L., Mota de Cuervo 42, 28043, Madrid, Spain
| | - Álvaro Valdés
- Escuela de Física, Universidad Nacional de Colombia, Sede Medellín, A. A., 3840, Medellín, Colombia
| | - Rita Prosmiti
- Institute of Fundamental Physics (IFF-CSIC), CSIC, Serrano 123, 28006 Madrid, Spain.
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8
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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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9
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Kushwaha A, Dhilip Kumar TJ. 4D potential energy surface of NCCN-H2 collision: Rotational dynamics by p-H2 and o-H2 at interstellar temperatures. J Chem Phys 2023; 159:074304. [PMID: 37602806 DOI: 10.1063/5.0161335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 07/28/2023] [Indexed: 08/22/2023] Open
Abstract
The rotational excitation rates of NCCN species are studied for its collision with hydrogen (H2) in temperatures ranging from 1 to 100 K. Such collisions can occur in the interstellar medium with H2 in either para (p-) or ortho (o-) state, of which the p-H2 state can be approximated via its collision with He (using a scaling factor) or with a reduced rigid rotor-H2 surface (by averaging over various orientations of H2). In the current work, a four-dimensional (4D) ab initio potential energy surface (PES) is considered to study the collision dynamics of H2 in both p- and o-states and the results are compared with previous approximations. The 4D surface is constructed using the explicitly correlated coupled-cluster method CCSD(T)-F12b with the augmented triple zeta basis AVTZ and then fitted into an artificial neural networks (NN) model to augment the surface and account for missing data points. The radial coefficients are obtained from this NN fitted 4D PES via a least square fit over two spherical harmonics functions. The cross sections (σ) are computed using the close-coupling (CC) method (until 230 cm-1) for both p- and o-H2 collisions, and the rates are obtained by Boltzmann distribution over the translational energy of H2 until 100 K. The o-H2 rates are found to be higher by 25%-30% and 10%-20% compared to the p-H2 rates for Δj = 2 and higher order transitions, respectively. The coupled-state/centrifugal sudden approximated rates are also computed and found to have deviations as large as 40% when compared to CC rates, thus making quantitative descriptions unreliable.
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Affiliation(s)
- Apoorv Kushwaha
- Quantum Dynamics Lab, Department of Chemistry Indian Institute of Technology Ropar, Rupnagar 140001, India
| | - T J Dhilip Kumar
- Quantum Dynamics Lab, Department of Chemistry Indian Institute of Technology Ropar, Rupnagar 140001, India
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10
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Hagg A, Kirschner KN. Open-Source Machine Learning in Computational Chemistry. J Chem Inf Model 2023; 63:4505-4532. [PMID: 37466636 PMCID: PMC10430767 DOI: 10.1021/acs.jcim.3c00643] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Indexed: 07/20/2023]
Abstract
The field of computational chemistry has seen a significant increase in the integration of machine learning concepts and algorithms. In this Perspective, we surveyed 179 open-source software projects, with corresponding peer-reviewed papers published within the last 5 years, to better understand the topics within the field being investigated by machine learning approaches. For each project, we provide a short description, the link to the code, the accompanying license type, and whether the training data and resulting models are made publicly available. Based on those deposited in GitHub repositories, the most popular employed Python libraries are identified. We hope that this survey will serve as a resource to learn about machine learning or specific architectures thereof by identifying accessible codes with accompanying papers on a topic basis. To this end, we also include computational chemistry open-source software for generating training data and fundamental Python libraries for machine learning. Based on our observations and considering the three pillars of collaborative machine learning work, open data, open source (code), and open models, we provide some suggestions to the community.
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Affiliation(s)
- Alexander Hagg
- Institute
of Technology, Resource and Energy-Efficient Engineering (TREE), University of Applied Sciences Bonn-Rhein-Sieg, 53757 Sankt Augustin, Germany
- Department
of Electrical Engineering, Mechanical Engineering and Technical Journalism, University of Applied Sciences Bonn-Rhein-Sieg, 53757 Sankt Augustin, Germany
| | - Karl N. Kirschner
- Institute
of Technology, Resource and Energy-Efficient Engineering (TREE), University of Applied Sciences Bonn-Rhein-Sieg, 53757 Sankt Augustin, Germany
- Department
of Computer Science, University of Applied
Sciences Bonn-Rhein-Sieg, 53757 Sankt Augustin, Germany
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11
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Kraka E, Antonio JJ, Freindorf M. Reaction mechanism - explored with the unified reaction valley approach. Chem Commun (Camb) 2023; 59:7151-7165. [PMID: 37233449 DOI: 10.1039/d3cc01576a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
One of the ultimate goals of chemistry is to understand and manipulate chemical reactions, which implies the ability to monitor the reaction and its underlying mechanism at an atomic scale. In this article, we introduce the Unified Reaction Valley Approach (URVA) as a tool for elucidating reaction mechanisms, complementing existing computational procedures. URVA combines the concept of the potential energy surface with vibrational spectroscopy and describes a chemical reaction via the reaction path and the surrounding reaction valley traced out by the reacting species on the potential energy surface on their way from the entrance to the exit channel, where the products are located. The key feature of URVA is the focus on the curving of the reaction path. Moving along the reaction path, any electronic structure change of the reacting species is registered by a change in the normal vibrational modes spanning the reaction valley and their coupling with the path, which recovers the curvature of the reaction path. This leads to a unique curvature profile for each chemical reaction, with curvature minima reflecting minimal change and curvature maxima indicating the location of important chemical events such as bond breaking/formation, charge polarization and transfer, rehybridization, etc. A decomposition of the path curvature into internal coordinate components or other coordinates of relevance for the reaction under consideration, provides comprehensive insight into the origin of the chemical changes taking place. After giving an overview of current experimental and computational efforts to gain insight into the mechanism of a chemical reaction and presenting the theoretical background of URVA, we illustrate how URVA works for three diverse processes, (i) [1,3] hydrogen transfer reactions; (ii) α-keto-amino inhibitor for SARS-CoV-2 Mpro; (iii) Rh-catalyzed cyanation. We hope that this article will inspire our computational colleagues to add URVA to their repertoire and will serve as an incubator for new reaction mechanisms to be studied in collaboration with our experimental experts in the field.
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Affiliation(s)
- Elfi Kraka
- Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Ave, Dallas, TX 75275-0314, USA.
| | - Juliana J Antonio
- Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Ave, Dallas, TX 75275-0314, USA.
| | - Marek Freindorf
- Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Ave, Dallas, TX 75275-0314, USA.
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12
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Izvekov S, Rice BM. Hierarchical Machine Learning of Low-Resolution Coarse-Grained Free Energy Potentials. J Chem Theory Comput 2023. [PMID: 37256918 DOI: 10.1021/acs.jctc.3c00128] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
A force-matching-based method for supervised machine learning (ML) of coarse-grained (CG) free energy (FE) potentials─known as multiscale coarse-graining via force-matching (MSCG/FM)─is an efficient method to develop microscopically informed CG models that are thermodynamically and statistically equivalent to the reference microscopic models. For low-resolution models, when the coarse-graining is at supramolecular scales, objective-oriented clustering of nonbonded particles is required and the reduced description becomes a function of the clustering algorithm. In the present work, we explore the dependence of the ML of the CG Helmholtz FE potential on the clustering algorithm. We consider coarse-graining based on partitional (k-means, leading to Voronoi diagram) and hierarchical agglomerative (bottom-up) clustering algorithms common in unsupervised ML and develop theory connecting the MSCG/FM learned CG Helmholtz potential and the clustering statistics. By combining the agglomerative clustering and the MSCG/FM learning in a recursive manner, we propose an efficient ML methodology to develop the fine-to-low resolution hierarchies of the CG models. The methodology does not suffer from degrading accuracy or increased computational cost to construct larger hierarchies and as such does not impose an upper size limitation of the CG particles resulting from the extended hierarchies. The utility of the methodology is demonstrated by obtaining the bottom-up agglomerative hierarchy for liquid nitromethane from all-atom molecular dynamics (MD) simulations. For agglomerative hierarchies, we prove the existence of renormalization group transformations that indicate self-similarity and allow for learning the low-resolution MSCG/FM potentials at low computational cost by rescaling and renormalizing the certain finer-resolution members of the hierarchy. The hierarchies of the CG models can be used to carry out simulations under constant-pressure conditions.
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Affiliation(s)
- Sergei Izvekov
- U.S. Army DEVCOM Army Research Laboratory, Aberdeen Proving Ground, Maryland 21005, United States
| | - Betsy M Rice
- U.S. Army DEVCOM Army Research Laboratory, Aberdeen Proving Ground, Maryland 21005, United States
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13
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Li L, Liu H, Qin Y, Wang H, Han J, Zhu X, Ge Q. Coupled oxygen desorption and structural reconstruction accompanying reduction of copper oxide. J Chem Phys 2023; 158:054702. [PMID: 36754813 DOI: 10.1063/5.0136537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
Understanding structural transformation and phase transition accompanying reactions in a solid as a catalyst or oxygen carrier is important to the design and optimization of many catalytic or chemical looping reaction processes. Herein, we combined density functional theory calculation with the stochastic surface walking global optimization approach to track the structural transformation accompanying the reduction of CuO upon releasing oxygen. We then used machine learning (ML) methods to correlate the structural properties of CuOx with varying x. By decomposing a reduction step into oxygen detachment and structural reconstruction, we identified two types of pathways: (1) uniform reduction with minimal structural changes; (2) segregated reduction with significant reconstruction. The results of ML analysis showed that the most important feature is the radial distribution functions of Cu-O at a percentage of oxygen vacancy [C(OV)] < 50% and Cu-Cu at C(OV) > 50% for CuOx formation. These features reflect the underlying physicochemical origin, i.e., Cu-O breaking and Cu-Cu formation in the respective stage of reduction. Phase diagram analysis indicates that CuO will be reduced to Cu2O under a typical oxygen uncoupling condition. This work demonstrates the complexity of solid structural transformation and the potential of ML methods in studying solid state materials involved in many chemical processes.
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Affiliation(s)
- Liwen Li
- Collaborative Innovation Center of Chemical Science and Engineering, Key Laboratory for Green Chemical Technology, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China
| | - Huixian Liu
- Collaborative Innovation Center of Chemical Science and Engineering, Key Laboratory for Green Chemical Technology, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China
| | - Yuyao Qin
- Collaborative Innovation Center of Chemical Science and Engineering, Key Laboratory for Green Chemical Technology, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China
| | - Hua Wang
- Collaborative Innovation Center of Chemical Science and Engineering, Key Laboratory for Green Chemical Technology, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China
| | - Jinyu Han
- Collaborative Innovation Center of Chemical Science and Engineering, Key Laboratory for Green Chemical Technology, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China
| | - Xinli Zhu
- Collaborative Innovation Center of Chemical Science and Engineering, Key Laboratory for Green Chemical Technology, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China
| | - Qingfeng Ge
- Department of Chemistry and Biochemistry, Southern Illinois University, Carbondale, Illinois 62901, USA
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14
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Thie A, Menger MF, Faraji S. HOAX: a hyperparameter optimisation algorithm explorer for neural networks. Mol Phys 2023. [DOI: 10.1080/00268976.2023.2172732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Albert Thie
- Zernike Institute for Advanced Materials, Faculty of Science and Engineering, University of Groningen, Groningen, The Netherlands
| | - Maximilian F.S.J. Menger
- Zernike Institute for Advanced Materials, Faculty of Science and Engineering, University of Groningen, Groningen, The Netherlands
| | - Shirin Faraji
- Zernike Institute for Advanced Materials, Faculty of Science and Engineering, University of Groningen, Groningen, The Netherlands
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15
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Houston PL, Qu C, Yu Q, Conte R, Nandi A, Li JK, Bowman JM. PESPIP: Software to fit complex molecular and many-body potential energy surfaces with permutationally invariant polynomials. J Chem Phys 2023; 158:044109. [PMID: 36725524 DOI: 10.1063/5.0134442] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
We wish to describe a potential energy surface by using a basis of permutationally invariant polynomials whose coefficients will be determined by numerical regression so as to smoothly fit a dataset of electronic energies as well as, perhaps, gradients. The polynomials will be powers of transformed internuclear distances, usually either Morse variables, exp(-ri,j/λ), where λ is a constant range hyperparameter, or reciprocals of the distances, 1/ri,j. The question we address is how to create the most efficient basis, including (a) which polynomials to keep or discard, (b) how many polynomials will be needed, (c) how to make sure the polynomials correctly reproduce the zero interaction at a large distance, (d) how to ensure special symmetries, and (e) how to calculate gradients efficiently. This article discusses how these questions can be answered by using a set of programs to choose and manipulate the polynomials as well as to write efficient Fortran programs for the calculation of energies and gradients. A user-friendly interface for access to monomial symmetrization approach results is also described. The software for these programs is now publicly available.
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Affiliation(s)
- Paul L Houston
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, USA and Department of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
| | - Chen Qu
- Independent Researcher, Toronto, Ontario M9B0E3, Canada
| | - Qi Yu
- Department of Chemistry, Yale University, New Haven, Connecticut 06520, USA
| | - Riccardo Conte
- Dipartimento di Chimica, Università Degli Studi di Milano, Via Golgi 19, 20133 Milano, Italy
| | - Apurba Nandi
- Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, USA
| | - Jeffrey K Li
- Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, USA
| | - Joel M Bowman
- Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, USA
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16
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Park S, Han H, Kim H, Choi S. Machine Learning Applications for Chemical Reactions. Chem Asian J 2022; 17:e202200203. [PMID: 35471772 PMCID: PMC9401034 DOI: 10.1002/asia.202200203] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/26/2022] [Indexed: 11/30/2022]
Abstract
Machine learning (ML) approaches have enabled rapid and efficient molecular property predictions as well as the design of new novel materials. In addition to great success for molecular problems, ML techniques are applied to various chemical reaction problems that require huge costs to solve with the existing experimental and simulation methods. In this review, starting with basic representations of chemical reactions, we summarized recent achievements of ML studies on two different problems; predicting reaction properties and synthetic routes. The various ML models are used to predict physical properties related to chemical reaction properties (e. g. thermodynamic changes, activation barriers, and reaction rates). Furthermore, the predictions of reactivity, self-optimization of reaction, and designing retrosynthetic reaction paths are also tackled by ML approaches. Herein we illustrate various ML strategies utilized in the various context of chemical reaction studies.
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Affiliation(s)
- Sanggil Park
- Department of ChemistryIncheon Natoinal University and Research Institute of Basic SciencesIncheon22012Republic of Korea
| | - Herim Han
- Digital Bio R&D CenterMediazenSeoul07789Republic of Korea
- Department of Polymer Science and EngineeringDankook UniversityYongin, Gyeonggi16890Republic of Korea
| | - Hyungjun Kim
- Department of ChemistryIncheon Natoinal University and Research Institute of Basic SciencesIncheon22012Republic of Korea
| | - Sunghwan Choi
- Division of National SupercomputingKorea Institute of Science and Technology InformationDaejeon34141Republic of Korea
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17
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Zhang Y, Xia J, Jiang B. REANN: A PyTorch-based end-to-end multi-functional deep neural network package for molecular, reactive, and periodic systems. J Chem Phys 2022; 156:114801. [DOI: 10.1063/5.0080766] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
In this work, we present a general purpose deep neural network package for representing energies, forces, dipole moments, and polarizabilities of atomistic systems. This so-called recursively embedded atom neural network model takes advantages of both the physically inspired atomic descriptor based neural networks and the message-passing based neural networks. Implemented in the PyTorch framework, the training process is parallelized on both the central processing unit and the graphics processing unit with high efficiency and low memory in which all hyperparameters can be optimized automatically. We demonstrate the state-of-the-art accuracy, high efficiency, scalability, and universality of this package by learning not only energies (with or without forces) but also dipole moment vectors and polarizability tensors in various molecular, reactive, and periodic systems. An interface between a trained model and LAMMPs is provided for large scale molecular dynamics simulations. We hope that this open-source toolbox will allow for future method development and applications of machine learned potential energy surfaces and quantum-chemical properties of molecules, reactions, and materials.
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Affiliation(s)
- Yaolong Zhang
- School of Chemistry and Materials Science, Department of Chemical Physics, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Junfan Xia
- School of Chemistry and Materials Science, Department of Chemical Physics, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Bin Jiang
- School of Chemistry and Materials Science, Department of Chemical Physics, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, Anhui 230026, China
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18
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Győri T, Czako G. ManyHF: A pragmatic automated method of finding lower-energy Hartree−Fock solutions for potential energy surface development. J Chem Phys 2022; 156:071101. [DOI: 10.1063/5.0080817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Tibor Győri
- Chemistry, University of Szeged Faculty of Science and Informatics, Hungary
| | - Gabor Czako
- Chemistry, University of Szeged Faculty of Science and Informatics, Hungary
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19
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Saito K, Hashimoto Y, Takayanagi T. Ring-Polymer Molecular Dynamics Calculations of Thermal Rate Coefficients and Branching Ratios for the Interstellar H 3+ + CO → H 2 + HCO +/HOC + Reaction and Its Deuterated Analogue. J Phys Chem A 2021; 125:10750-10756. [PMID: 34918514 DOI: 10.1021/acs.jpca.1c09160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The reaction between H3+ and CO is important in understanding the H3+ destruction mechanism in the interstellar medium. In this work, thermal rate coefficients for the H3+ + CO and D3+ + CO reactions are calculated using ring-polymer molecular dynamics (RPMD) on a high-level machine-learning potential energy surface. The RPMD results agree well with the classical molecular dynamics results, where nuclear quantum effects are completely ignored, whereas the agreement between the RPMD results and the previous quasi-classical trajectory is good only at low temperatures. The calculated [HCO+]/[HOC+] product branching ratios decrease as the temperature increases, and the product branching is exclusively determined by the initial collisional orientation, which governs the formation of an ion-dipole complex, H3+···CO or H3+···OC, that dissociates into H2 + HCO+ or H2 + HOC+, respectively, via a direct mechanism. However, the contribution of the indirect mechanism via the rearrangement between H3+···CO and H3+···OC increases as the temperature increases, although its absolute fraction is small.
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Affiliation(s)
- Kohei Saito
- Department of Chemistry, Saitama University, Shimo-Okubo 255, Sakura-ku, Saitama 338-8570, Japan
| | - Yu Hashimoto
- Department of Chemistry, Saitama University, Shimo-Okubo 255, Sakura-ku, Saitama 338-8570, Japan
| | - Toshiyuki Takayanagi
- Department of Chemistry, Saitama University, Shimo-Okubo 255, Sakura-ku, Saitama 338-8570, Japan
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20
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Xu M, Zhu T, Zhang JZH. Automated Construction of Neural Network Potential Energy Surface: The Enhanced Self-Organizing Incremental Neural Network Deep Potential Method. J Chem Inf Model 2021; 61:5425-5437. [PMID: 34752095 DOI: 10.1021/acs.jcim.1c01125] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
In recent years, the use of deep learning (neural network) potential energy surface (NNPES) in molecular dynamics simulation has experienced explosive growth as it can be as accurate as quantum chemistry methods while being as efficient as classical mechanic methods. However, the development of NNPES is highly nontrivial. In particular, it has been troubling to construct a dataset that is as small as possible yet can cover the target chemical space. In this work, an ESOINN-DP method is developed, which has the enhanced self-organizing incremental neural network (ESOINN) and a newly proposed error indicator at its core. With ESOINN-DP, one can construct the NNPES with little human intervention, and this method ensures that the constructed reference dataset covers the target chemical space with minimum redundancy. The performance of the ESOINN-DP method has been well validated by developing neural network potential energy surfaces for water clusters, tripeptides, and by de-redundancy of a sub-dataset of the ANI-1 database. We believe that the ESOINN-DP method provides a novel idea for the construction of NNPES and, especially, the reference datasets, and it can be used for molecular dynamics (MD) simulations of various gas-phase and condensed-phase chemical systems.
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Affiliation(s)
- Mingyuan Xu
- Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, Shanghai Key Laboratory of Green Chemistry & Chemical Process, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China
| | - Tong Zhu
- Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, Shanghai Key Laboratory of Green Chemistry & Chemical Process, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China.,NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
| | - John Z H Zhang
- Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, Shanghai Key Laboratory of Green Chemistry & Chemical Process, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China.,NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China.,Department of Chemistry, New York University, New York, New York 10003, United States.,Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, Shanxi 030006, China
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21
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Pinheiro M, Ge F, Ferré N, Dral PO, Barbatti M. Choosing the right molecular machine learning potential. Chem Sci 2021; 12:14396-14413. [PMID: 34880991 PMCID: PMC8580106 DOI: 10.1039/d1sc03564a] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 09/14/2021] [Indexed: 11/21/2022] Open
Abstract
Quantum-chemistry simulations based on potential energy surfaces of molecules provide invaluable insight into the physicochemical processes at the atomistic level and yield such important observables as reaction rates and spectra. Machine learning potentials promise to significantly reduce the computational cost and hence enable otherwise unfeasible simulations. However, the surging number of such potentials begs the question of which one to choose or whether we still need to develop yet another one. Here, we address this question by evaluating the performance of popular machine learning potentials in terms of accuracy and computational cost. In addition, we deliver structured information for non-specialists in machine learning to guide them through the maze of acronyms, recognize each potential's main features, and judge what they could expect from each one.
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Affiliation(s)
- Max Pinheiro
- Aix Marseille University, CNRS, ICR Marseille France
| | - Fuchun Ge
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, College of Chemistry and Chemical Engineering, Xiamen University China
| | - Nicolas Ferré
- Aix Marseille University, CNRS, ICR Marseille France
| | - Pavlo O Dral
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, College of Chemistry and Chemical Engineering, Xiamen University China
| | - Mario Barbatti
- Aix Marseille University, CNRS, ICR Marseille France
- Institut Universitaire de France 75231 Paris France
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22
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Moberg DR, Jasper AW. Permutationally Invariant Polynomial Expansions with Unrestricted Complexity. J Chem Theory Comput 2021; 17:5440-5455. [PMID: 34469127 DOI: 10.1021/acs.jctc.1c00352] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
A general strategy is presented for constructing and validating permutationally invariant polynomial (PIP) expansions for chemical systems of any stoichiometry. Demonstrations are made for three categories of gas-phase dynamics and kinetics: collisional energy-transfer trajectories for predicting pressure-dependent kinetics, three-body collisions for describing transient van der Waals adducts relevant to atmospheric chemistry, and nonthermal reactivity via quasiclassical trajectories. In total, 30 systems are considered with up to 15 atoms and 39 degrees of freedom. Permutational invariance is enforced in PIP expansions with as many as 13 million terms and 13 permutationally distinct atom types by taking advantage of petascale computational resources. The quality of the PIP expansions is demonstrated through the systematic convergence of in-sample and out-of-sample errors with respect to both the number of training data and the order of the expansion, and these errors are shown to predict errors in the dynamics for both reactive and nonreactive applications. The parallelized code distributed as part of this work enables the automation of PIP generation for complex systems with multiple channels and flexible user-defined symmetry constraints and for automatically removing unphysical unconnected terms from the basis set expansions, all of which are required for simulating complex reactive systems.
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Affiliation(s)
- Daniel R Moberg
- Chemical Sciences and Engineering Division, Argonne National Laboratory, Lemont, Illinois 60439, United States
| | - Ahren W Jasper
- Chemical Sciences and Engineering Division, Argonne National Laboratory, Lemont, Illinois 60439, United States
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23
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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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24
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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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25
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Beć KB, Grabska J, Huck CW. Current and future research directions in computer-aided near-infrared spectroscopy: A perspective. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 254:119625. [PMID: 33706116 DOI: 10.1016/j.saa.2021.119625] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 02/09/2021] [Accepted: 02/09/2021] [Indexed: 06/12/2023]
Abstract
The present review aims to draw a perspective on the vibrational spectroscopy combined with the tools of computational chemistry. This includes an overview of the accomplishments made so far, the assessment of the present development trends and the prospects for continuing these advances. State-of-the-art methods, current challenges and the expected future advances are evaluated from the point-of-view of the practical application in vibrational spectroscopy. A special attention is given to near-infrared (NIR) spectroscopy, which occupies a distinct position among the techniques of vibrational spectroscopy. As the result of intrinsically complex spectra, reliance on the anharmonicity as well as keen interest given to complex materials, NIR spectroscopy may particularly benefit from computational chemistry. The present key limitations hindering development of NIR spectroscopy are identified; these constitute primarily the limit in the treatable system size and the inability to effectively include chemical matrix effects. Given the expanding role of NIR spectroscopy in science and industry, lifting these limitations would directly enhance the general potential of this technique.
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Affiliation(s)
- Krzysztof B Beć
- Institute of Analytical Chemistry and Radiochemistry, Leopold-Franzens University, CCB-Center for Chemistry and Biomedicine, Innrain 80/82, 6020 Innsbruck, Austria
| | - Justyna Grabska
- Institute of Analytical Chemistry and Radiochemistry, Leopold-Franzens University, CCB-Center for Chemistry and Biomedicine, Innrain 80/82, 6020 Innsbruck, Austria.
| | - Christian W Huck
- Institute of Analytical Chemistry and Radiochemistry, Leopold-Franzens University, CCB-Center for Chemistry and Biomedicine, Innrain 80/82, 6020 Innsbruck, Austria
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26
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Takayanagi T. Application of Reaction Path Search Calculations to Potential Energy Surface Fits. J Phys Chem A 2021; 125:3994-4002. [PMID: 33915053 DOI: 10.1021/acs.jpca.1c01512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
There has been significant progress in recent years in the use of machine learning techniques to model high-dimensional reactive potential energy surfaces using large-scale data obtained from ab initio electronic structure calculations. In these methods, the strategy used to gather data becomes a key issue as the molecular size increases. In this work, we examine the applicability of the reaction path search algorithm implemented in the Global Reaction Route Mapping (GRRM) code as a data-gathering approach. The electronic energies and gradients sampled by using the GRRM calculation are directly used in potential energy surface fitting to a permutationally invariant polynomial function. This simple approach was applied to the HNS and HCNO reaction systems, and we found that the fitted potential energy surfaces reasonably reproduce the features of the electronic structure calculations used in the GRRM calculations. This suggests that the GRRM sampling scheme can be used to construct an initial potential energy surface.
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Affiliation(s)
- Toshiyuki Takayanagi
- Department of Chemistry, Saitama University, Shimo-Okubo 255, Saitama City, Saitama 338-8570, Japan
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27
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Lin Q, Zhang L, Zhang Y, Jiang B. Searching Configurations in Uncertainty Space: Active Learning of High-Dimensional Neural Network Reactive Potentials. J Chem Theory Comput 2021; 17:2691-2701. [PMID: 33904718 DOI: 10.1021/acs.jctc.1c00166] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Neural network (NN) potential energy surfaces (PESs) have been widely used in atomistic simulations with ab initio accuracy. While constructing NN PESs, their training data points are often sampled by molecular dynamics trajectories. This strategy can be however inefficient for reactive systems involving rare events. Here, we develop an uncertainty-driven active learning strategy to automatically and efficiently generate high-dimensional NN-based reactive potentials, taking a gas-surface reaction as an example. The difference between two independent NN models is used as a simple and differentiable uncertainty metric, allowing us to quickly search in the uncertainty space and place new samples at which the PES is less reliable. By interfacing this algorithm with the first-principles simulation package, we demonstrate that a globally accurate NN potential of the H2 + Ag(111) system can be constructed with merely ∼150 data points. This PES can be further refined to describe H2 dissociation on Ag(100) by adding ∼130 more configurations on this facet. The entire process is completely automatic and self-terminated once the relative error criterion is fulfilled. Impressively, data points sampled by this uncertainty-driven strategy are substantially fewer than by the traditional trajectory-based sampling. The final NN PES not only converges well the quantum dissociation probability of the molecule but also well-reproduces the phonon properties of the substrate and is capable of describing surface temperature effects. These results show the potential of this active learning approach in developing high-dimensional NN reactive potentials in gas and condensed phases.
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Affiliation(s)
- Qidong Lin
- Hefei National Laboratory for Physical Science at the Microscale, Department of Chemical Physics, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Liang Zhang
- Hefei National Laboratory for Physical Science at the Microscale, Department of Chemical Physics, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Yaolong Zhang
- Hefei National Laboratory for Physical Science at the Microscale, Department of Chemical Physics, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Bin Jiang
- Hefei National Laboratory for Physical Science at the Microscale, Department of Chemical Physics, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, Anhui 230026, China
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28
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Abbott AS, Abbott BZ, Turney JM, Schaefer HF. Arbitrary-Order Derivatives of Quantum Chemical Methods via Automatic Differentiation. J Phys Chem Lett 2021; 12:3232-3239. [PMID: 33764068 DOI: 10.1021/acs.jpclett.1c00607] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Herein, we present for the first time a general methodology for obtaining arbitrary-order nuclear coordinate derivatives of electronic energies derived from quantum chemistry methods. By leveraging modern advances in automatic differentiation software, we demonstrate that exact derivatives can be obtained for any method. This innovation completely bypasses the issues associated with the computational stability of applying numerical differentiation methods and dispenses the need to derive challenging formulae for analytic energy derivatives. We describe a freely available and open-source software implementation of our scheme and demonstrate its use in obtaining exact nuclear derivatives of energies from Hartree-Fock theory, second-order Møller-Plesset perturbation theory (MP2), and coupled cluster theory with single, double, and perturbative triple excitations [CCSD(T)]. Our sample computations include up to sextic derivatives and span a variety of test systems with up to 100 basis functions, confirming the viability of this scheme for a wide range of applications. Many of the results obtained have hitherto been unobtainable by exact means due to a lack of higher-order derivative formulae. The details of our implementation and possible further developments are discussed.
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Affiliation(s)
- Adam S Abbott
- Center for Computational Quantum Chemistry, University of Georgia, Athens, Georgia 30602, United States
| | - Boyi Z Abbott
- Center for Computational Quantum Chemistry, University of Georgia, Athens, Georgia 30602, United States
| | - Justin M Turney
- Center for Computational Quantum Chemistry, University of Georgia, Athens, Georgia 30602, United States
| | - Henry F Schaefer
- Center for Computational Quantum Chemistry, University of Georgia, Athens, Georgia 30602, United States
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29
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Affiliation(s)
- Kazuo Takatsuka
- Fukui Institute for Fundamental Chemistry, Kyoto University, 606-8103 Kyoto, Japan
| | - Yasuki Arasaki
- Fukui Institute for Fundamental Chemistry, Kyoto University, 606-8103 Kyoto, Japan
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30
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Quintas-Sánchez E, Dawes R. Spectroscopy and Scattering Studies Using Interpolated Ab Initio Potentials. Annu Rev Phys Chem 2021; 72:399-421. [PMID: 33503385 DOI: 10.1146/annurev-physchem-090519-051837] [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] [Indexed: 11/09/2022]
Abstract
The Born-Oppenheimer potential energy surface (PES) has come a long way since its introduction in the 1920s, both conceptually and in predictive power for practical applications. Nevertheless, nearly 100 years later-despite astonishing advances in computational power-the state-of-the-art first-principles prediction of observables related to spectroscopy and scattering dynamics is surprisingly limited. For example, the water dimer, (H2O)2, with only six nuclei and 20 electrons, still presents a formidable challenge for full-dimensional variational calculations of bound states and is considered out of reach for rigorous scattering calculations. The extremely poor scaling of the most rigorous quantum methods is fundamental; however, recent progress in development of approximate methodologies has opened the door to fairly routine high-quality predictions, unthinkable 20 years ago. In this review, in relation to the workflow of spectroscopy and/or scattering studies, we summarize progress and challenges in the component areas of electronic structure calculations, PES fitting, and quantum dynamical calculations.
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Affiliation(s)
- Ernesto Quintas-Sánchez
- Department of Chemistry, Missouri University of Science and Technology, Rolla, Missouri 65409, USA;
| | - Richard Dawes
- Department of Chemistry, Missouri University of Science and Technology, Rolla, Missouri 65409, USA;
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31
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Ha JK, Kim K, Min SK. Machine Learning-Assisted Excited State Molecular Dynamics with the State-Interaction State-Averaged Spin-Restricted Ensemble-Referenced Kohn-Sham Approach. J Chem Theory Comput 2021; 17:694-702. [PMID: 33470100 DOI: 10.1021/acs.jctc.0c01261] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
We present a machine learning-assisted excited state molecular dynamics (ML-ESMD) based on the ensemble density functional theory framework. Since we represent a diabatic Hamiltonian in terms of generalized valence bond ansatz within the state-interaction state-averaged spin-restricted ensemble-referenced Kohn-Sham (SI-SA-REKS) method, we can avoid singularities near conical intersections, which are crucial in excited state molecular dynamics simulations. We train the diabatic Hamiltonian elements and their analytical gradients with the SchNet architecture to construct machine learning models, while the phase freedom of off-diagonal elements of the Hamiltonian is cured by introducing the phase-less loss function. Our machine learning models show reasonable accuracy with mean absolute errors of ∼0.1 kcal/mol and ∼0.5 kcal/mol/Å for the diabatic Hamiltonian elements and their gradients, respectively, for penta-2,4-dieniminium cation. Moreover, by exploiting the diabatic representation, our models can predict correct conical intersection structures and their topologies. In addition, our ML-ESMD simulations give almost identical result with a direct dynamics at the same level of theory.
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Affiliation(s)
- Jong-Kwon Ha
- Department of Chemistry, School of Natural Science, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Ulju-gun, Ulsan 44919, South Korea
| | - Kicheol Kim
- Department of Chemistry, School of Natural Science, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Ulju-gun, Ulsan 44919, South Korea
| | - Seung Kyu Min
- Department of Chemistry, School of Natural Science, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Ulju-gun, Ulsan 44919, South Korea
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32
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Manzhos S, Carrington T. Neural Network Potential Energy Surfaces for Small Molecules and Reactions. Chem Rev 2020; 121:10187-10217. [PMID: 33021368 DOI: 10.1021/acs.chemrev.0c00665] [Citation(s) in RCA: 110] [Impact Index Per Article: 27.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
We review progress in neural network (NN)-based methods for the construction of interatomic potentials from discrete samples (such as ab initio energies) for applications in classical and quantum dynamics including reaction dynamics and computational spectroscopy. The main focus is on methods for building molecular potential energy surfaces (PES) in internal coordinates that explicitly include all many-body contributions, even though some of the methods we review limit the degree of coupling, due either to a desire to limit computational cost or to limited data. Explicit and direct treatment of all many-body contributions is only practical for sufficiently small molecules, which are therefore our primary focus. This includes small molecules on surfaces. We consider direct, single NN PES fitting as well as more complex methods that impose structure (such as a multibody representation) on the PES function, either through the architecture of one NN or by using multiple NNs. We show how NNs are effective in building representations with low-dimensional functions including dimensionality reduction. We consider NN-based approaches to build PESs in the sums-of-product form important for quantum dynamics, ways to treat symmetry, and issues related to sampling data distributions and the relation between PES errors and errors in observables. We highlight combinations of NNs with other ideas such as permutationally invariant polynomials or sums of environment-dependent atomic contributions, which have recently emerged as powerful tools for building highly accurate PESs for relatively large molecular and reactive systems.
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Affiliation(s)
- Sergei Manzhos
- Centre Énergie Matériaux Télécommunications, Institut National de la Recherche Scientifique, 1650, Boulevard Lionel-Boulet, Varennes, Québec City, Québec J3X 1S2, Canada
| | - Tucker Carrington
- Chemistry Department, Queen's University, Kingston Ontario K7L 3N6, Canada
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33
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Schmitz G, Klinting EL, Christiansen O. A Gaussian process regression adaptive density guided approach for potential energy surface construction. J Chem Phys 2020; 153:064105. [DOI: 10.1063/5.0015344] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Affiliation(s)
- Gunnar Schmitz
- Department of Chemistry, Aarhus Universitet, DK-8000 Aarhus, Denmark
| | | | - Ove Christiansen
- Department of Chemistry, Aarhus Universitet, DK-8000 Aarhus, Denmark
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34
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Jiang B, Li J, Guo H. High-Fidelity Potential Energy Surfaces for Gas-Phase and Gas-Surface Scattering Processes from Machine Learning. J Phys Chem Lett 2020; 11:5120-5131. [PMID: 32517472 DOI: 10.1021/acs.jpclett.0c00989] [Citation(s) in RCA: 105] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
In this Perspective, we review recent advances in constructing high-fidelity potential energy surfaces (PESs) from discrete ab initio points, using machine learning tools. Such PESs, albeit with substantial initial investments, provide significantly higher efficiency than direct dynamics methods and/or high accuracy at a level that is not affordable by on-the-fly approaches. These PESs not only are a necessity for quantum dynamical studies because of delocalization of wave packets but also enable the study of low-probability and long-time events in (quasi-)classical treatments. Our focus here is on inelastic and reactive scattering processes, which are more challenging than bound systems because of the involvement of continua. Relevant applications and developments for dynamical processes in both the gas phase and at gas-surface interfaces are discussed.
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Affiliation(s)
- Bin Jiang
- Hefei National Laboratory for Physical Science at the Microscale, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes, Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Jun Li
- School of Chemistry and Chemical Engineering and Chongqing Key Laboratory of Theoretical and Computational Chemistry, Chongqing University, Chongqing 401331, China
| | - Hua Guo
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, United States
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35
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Exploring the Mechanism of Catalysis with the Unified Reaction Valley Approach (URVA)—A Review. Catalysts 2020. [DOI: 10.3390/catal10060691] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
The unified reaction valley approach (URVA) differs from mainstream mechanistic studies, as it describes a chemical reaction via the reaction path and the surrounding reaction valley on the potential energy surface from the van der Waals region to the transition state and far out into the exit channel, where the products are located. The key feature of URVA is the focus on the curving of the reaction path. Moving along the reaction path, any electronic structure change of the reacting molecules is registered by a change in their normal vibrational modes and their coupling with the path, which recovers the curvature of the reaction path. This leads to a unique curvature profile for each chemical reaction with curvature minima reflecting minimal change and curvature maxima, the location of important chemical events such as bond breaking/forming, charge polarization and transfer, rehybridization, etc. A unique decomposition of the path curvature into internal coordinate components provides comprehensive insights into the origins of the chemical changes taking place. After presenting the theoretical background of URVA, we discuss its application to four diverse catalytic processes: (i) the Rh catalyzed methanol carbonylation—the Monsanto process; (ii) the Sharpless epoxidation of allylic alcohols—transition to heterogenous catalysis; (iii) Au(I) assisted [3,3]-sigmatropic rearrangement of allyl acetate; and (iv) the Bacillus subtilis chorismate mutase catalyzed Claisen rearrangement—and show how URVA leads to a new protocol for fine-tuning of existing catalysts and the design of new efficient and eco-friendly catalysts. At the end of this article the pURVA software is introduced. The overall goal of this article is to introduce to the chemical community a new protocol for fine-tuning existing catalytic reactions while aiding in the design of modern and environmentally friendly catalysts.
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36
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Peyton BG, Briggs C, D’Cunha R, Margraf JT, Crawford TD. Machine-Learning Coupled Cluster Properties through a Density Tensor Representation. J Phys Chem A 2020; 124:4861-4871. [DOI: 10.1021/acs.jpca.0c02804] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Affiliation(s)
- Benjamin G. Peyton
- Department of Chemistry, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Connor Briggs
- Department of Chemistry, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Ruhee D’Cunha
- Department of Chemistry, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Johannes T. Margraf
- Chair for Theoretical Chemistry, Technische Universität München, Lichtenbergstrasse 4, D-85747 Garching, Germany
| | - T. Daniel Crawford
- Department of Chemistry, Virginia Tech, Blacksburg, Virginia 24061, United States
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37
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Metz MP, Szalewicz K. Automatic Generation of Flexible-Monomer Intermolecular Potential Energy Surfaces. J Chem Theory Comput 2020; 16:2317-2339. [PMID: 32240593 DOI: 10.1021/acs.jctc.9b01241] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
A method is developed for automatic generation of nonreactive intermolecular two-body potential energy surfaces (PESs) including intramonomer degrees of freedom. This method, called flex-autoPES, is an extension of the autoPES method developed earlier, which assumes rigid monomers. In both cases, the whole PES development proceeds without any human intervention. The functional form used is a sum of products of site-site functions (both atomic and off-atomic sites can be used). The leading terms with sites involving different monomers are of physically motivated form. The long-range part of a PES is computed from monomer properties without using any dimer information. The close-range part is fitted to dimer interaction energies computed using electronic structure methods. Virtually any method can be used in such calculations, but the use of symmetry-adapted perturbation theory provides a seamless connection to the long-range part of the PES. The performance of the flex-autoPES code was tested by developing a full-dimensional PES for the water dimer and PESs including only some soft intramonomer degrees of freedom for the ethylene glycol dimer and for the ethylene glycol-water dimer. In the case of the water dimer, the root-mean-square error (RMSE) of the PES from the data points with negative total energies is 0.03 kcal/mol, and we expect this PES to be more accurate than any previously published PES of this type. For the ethylene glycol dimer and the ethylene glycol-water dimers, the analogous RMSEs are 0.25 and 0.1 kcal/mol, respectively.
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Affiliation(s)
- Michael P Metz
- Department of Physics and Astronomy, University of Delaware, Newark, Delaware 19716, United States
| | - Krzysztof Szalewicz
- Department of Physics and Astronomy, University of Delaware, Newark, Delaware 19716, United States
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Abstract
As the quantum chemistry (QC) community embraces machine learning (ML), the number of new methods and applications based on the combination of QC and ML is surging. In this Perspective, a view of the current state of affairs in this new and exciting research field is offered, challenges of using machine learning in quantum chemistry applications are described, and potential future developments are outlined. Specifically, examples of how machine learning is used to improve the accuracy and accelerate quantum chemical research are shown. Generalization and classification of existing techniques are provided to ease the navigation in the sea of literature and to guide researchers entering the field. The emphasis of this Perspective is on supervised machine learning.
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Affiliation(s)
- Pavlo O Dral
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, and College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
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39
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Kurilovich AA, Alexander CT, Pazhetnov EM, Stevenson KJ. Active learning-based framework for optimal reaction mechanism selection from microkinetic modeling: a case study of electrocatalytic oxygen reduction reaction on carbon nanotubes. Phys Chem Chem Phys 2020; 22:4581-4591. [PMID: 32048660 DOI: 10.1039/c9cp06190h] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
The elucidation of complex electrochemical reaction mechanisms requires advanced models with many intermediate reaction steps, which are governed by a large number of parameters like reaction rate constants and charge transfer coefficients. Overcomplicated models introduce high uncertainty in the choice of the parameters and cannot be used to obtain meaningful insights on the reaction pathway. We describe a new framework of optimal reaction mechanism selection based on the mean-field microkinetic modeling approach (MF-MKM) and adaptive sampling of model parameters. The optimal model is selected to provide both the accurate fitting of experimental data within the experimental error and low uncertainty of model parameters choice. Generally, this approach can be applied for any complex heterogeneous electrochemical reaction. We use the "2e-" electrocatalytic oxygen reduction reaction (ORR) on carbon nanotubes (CNTs) as a representative example of a sufficiently complex reaction. Rotating disk electrode (RDE) experimental data for both ORR in O2-saturated 0.1 M KOH solution and hydrogen peroxide oxidation/reduction reaction (HPRR/HPOR) in Ar-purged 0.1 M KOH solution with different HO2- concentrations were used to show the dependence of the model parameters uniqueness on the completeness of the experimental dataset. It is demonstrated that the optimal reaction mechanism for ORR on CNT and available experimental data consists of O2 adsorption step on the electrode surface and effective step of two-electron reduction to HO2- combined with its desorption from the electrode. The low uncertainty of estimated model parameters is provided only within the 2-step model being applied to the full available experimental dataset. The assessment of elementary step mechanisms on electro-catalytic materials including carbon-based electrodes requires more diverse experimental data and/or higher precision of experimental measurements to facilitate more precise microkinetic modeling of more complex reaction mechanisms.
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Affiliation(s)
- Aleksandr A Kurilovich
- Skolkovo Institute of Science and Technology, Skolkovo Innovation Center, Building 3, Moscow, 143026, Russian Federation.
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40
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Győri T, Czakó G. Automating the Development of High-Dimensional Reactive Potential Energy Surfaces with the robosurfer Program System. J Chem Theory Comput 2019; 16:51-66. [DOI: 10.1021/acs.jctc.9b01006] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Tibor Győri
- MTA-SZTE Lendület Computational Reaction Dynamics Research Group, Interdisciplinary Excellence Centre and Department of Physical Chemistry and Materials Science, Institute of Chemistry, University of Szeged, Rerrich Béla tér 1, Szeged H-6720, Hungary
| | - Gábor Czakó
- MTA-SZTE Lendület Computational Reaction Dynamics Research Group, Interdisciplinary Excellence Centre and Department of Physical Chemistry and Materials Science, Institute of Chemistry, University of Szeged, Rerrich Béla tér 1, Szeged H-6720, Hungary
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41
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Davis MM, Weidman JD, Abbott AS, Douberly GE, Turney JM, Schaefer HF. Characterization of the 2-methylvinoxy radical + O2 reaction: A focal point analysis and composite multireference study. J Chem Phys 2019; 151:124302. [DOI: 10.1063/1.5113800] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Affiliation(s)
- Matthew M. Davis
- Center for Computational Quantum Chemistry, University of Georgia, Athens, Georgia 30602, USA
| | - Jared D. Weidman
- Center for Computational Quantum Chemistry, University of Georgia, Athens, Georgia 30602, USA
| | - Adam S. Abbott
- Center for Computational Quantum Chemistry, University of Georgia, Athens, Georgia 30602, USA
| | - Gary E. Douberly
- Center for Computational Quantum Chemistry, University of Georgia, Athens, Georgia 30602, USA
| | - Justin M. Turney
- Center for Computational Quantum Chemistry, University of Georgia, Athens, Georgia 30602, USA
| | - Henry F. Schaefer
- Center for Computational Quantum Chemistry, University of Georgia, Athens, Georgia 30602, USA
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