1
|
Song K, Li J. Fundamental Invariant Neural Network (FI-NN) Potential Energy Surface for the OH + CH 3OH Reaction with Analytical Forces. J Phys Chem A 2024. [PMID: 39096277 DOI: 10.1021/acs.jpca.4c02432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/05/2024]
Abstract
The hydrogen abstraction reaction of OH + CH3OH plays a great role in combustion and atmospheric and interstellar chemistry and has been extensively studied theoretically and experimentally. Theoretically, the numerical gradients with respect to the Cartesian coordinates of atoms in molecular simulations on our recent potential energy surface (PES) for the title reaction trained using the permutationally invariant polynomial neural network (PIP-NN) approach hinder the extensive calculation because of the unaffordable computation cost. To address this issue, we in this work report a new full-dimensional accurate analytical PES for the title reaction using the fundamental invariant neural network (FI-NN) approach based on 140,192 points of the quality UCCSD(T)-F12a/AVTZ. Besides, the spin-orbit (SO) corrections of OH in the entrance channel were determined at the level of complete active space self-consistent field with the AVTZ basis set. As a compromise between computational cost and efficiency, the Δ-machine learning approach was employed to construct the SO-corrected PES. Based on this new FI-NN PES with analytical forces, thermal rate coefficients and various dynamic properties, including the integral cross sections, the differential cross sections, and the product energy partitioning, were determined by running a total of 5.5 million trajectories. The use of analytical gradients of the FI-NN PES accelerated simulations and about 99% of computation cost was saved, compared to that for the PIP-NN PES with numerical gradients. Such a significant acceleration is achieved mainly by replacing PIPs with FIs.
Collapse
Affiliation(s)
- Kaisheng Song
- School of Chemistry and Chemical Engineering & Chongqing Key Laboratory of Chemical Theory and Mechanism, Chongqing University, Chongqing 401331, P.R. China
| | - Jun Li
- School of Chemistry and Chemical Engineering & Chongqing Key Laboratory of Chemical Theory and Mechanism, Chongqing University, Chongqing 401331, P.R. China
| |
Collapse
|
2
|
Yang Y, Zhang S, Ranasinghe KD, Isayev O, Roitberg AE. Machine Learning of Reactive Potentials. Annu Rev Phys Chem 2024; 75:371-395. [PMID: 38941524 DOI: 10.1146/annurev-physchem-062123-024417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/30/2024]
Abstract
In the past two decades, machine learning potentials (MLPs) have driven significant developments in chemical, biological, and material sciences. The construction and training of MLPs enable fast and accurate simulations and analysis of thermodynamic and kinetic properties. This review focuses on the application of MLPs to reaction systems with consideration of bond breaking and formation. We review the development of MLP models, primarily with neural network and kernel-based algorithms, and recent applications of reactive MLPs (RMLPs) to systems at different scales. We show how RMLPs are constructed, how they speed up the calculation of reactive dynamics, and how they facilitate the study of reaction trajectories, reaction rates, free energy calculations, and many other calculations. Different data sampling strategies applied in building RMLPs are also discussed with a focus on how to collect structures for rare events and how to further improve their performance with active learning.
Collapse
Affiliation(s)
- Yinuo Yang
- Department of Chemistry, University of Florida, Gainesville, Florida;
| | - Shuhao Zhang
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania;
| | | | - Olexandr Isayev
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania;
| | - Adrian E Roitberg
- Department of Chemistry, University of Florida, Gainesville, Florida;
| |
Collapse
|
3
|
Tang Z, Bromley ST, Hammer B. A machine learning potential for simulating infrared spectra of nanosilicate clusters. J Chem Phys 2023; 158:2895243. [PMID: 37290080 DOI: 10.1063/5.0150379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 05/23/2023] [Indexed: 06/10/2023] Open
Abstract
The use of machine learning (ML) in chemical physics has enabled the construction of interatomic potentials having the accuracy of ab initio methods and a computational cost comparable to that of classical force fields. Training an ML model requires an efficient method for the generation of training data. Here, we apply an accurate and efficient protocol to collect training data for constructing a neural network-based ML interatomic potential for nanosilicate clusters. Initial training data are taken from normal modes and farthest point sampling. Later on, the set of training data is extended via an active learning strategy in which new data are identified by the disagreement between an ensemble of ML models. The whole process is further accelerated by parallel sampling over structures. We use the ML model to run molecular dynamics simulations of nanosilicate clusters with various sizes, from which infrared spectra with anharmonicity included can be extracted. Such spectroscopic data are needed for understanding the properties of silicate dust grains in the interstellar medium and in circumstellar environments.
Collapse
Affiliation(s)
- Zeyuan Tang
- Center for Interstellar Catalysis, Department of Physics and Astronomy, Aarhus University, Ny Munkegade 120, Aarhus C 8000, Denmark
| | - Stefan T Bromley
- Departament de Ciència de Materials i Química Física and Institut de Química Teòrica i Computatcional (IQTCUB), Universitat de Barcelona, c/Martí i Franquès 1-11, 08028 Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluis Companys 23, 08010 Barcelona, Spain
| | - Bjørk Hammer
- Center for Interstellar Catalysis, Department of Physics and Astronomy, Aarhus University, Ny Munkegade 120, Aarhus C 8000, Denmark
| |
Collapse
|
4
|
Herbold M, Behler J. A Hessian-based assessment of atomic forces for training machine learning interatomic potentials. J Chem Phys 2022; 156:114106. [DOI: 10.1063/5.0082952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
In recent years, many types of machine learning potentials (MLPs) have been introduced, which are able to represent high-dimensional potential-energy surfaces (PESs) with close to first-principles accuracy. Most current MLPs rely on atomic energy contributions given as a function of the local chemical environments. Frequently, in addition to total energies, atomic forces are also used to construct the potentials, as they provide detailed local information about the PES. Since many systems are too large for electronic structure calculations, obtaining reliable reference forces from smaller subsystems, such as molecular fragments or clusters, can substantially simplify the construction of the training sets. Here, we propose a method to determine structurally converged molecular fragments, providing reliable atomic forces based on an analysis of the Hessian. The method, which serves as a locality test and allows us to estimate the importance of long-range interactions, is illustrated for a series of molecular model systems and the metal–organic framework MOF-5 as an example for a complex organic–inorganic hybrid material.
Collapse
Affiliation(s)
- Marius Herbold
- Universität Göttingen, Institut für Physikalische Chemie, Theoretische Chemie, Tammannstraße 6, 37077 Göttingen, Germany
| | - Jörg Behler
- Universität Göttingen, Institut für Physikalische Chemie, Theoretische Chemie, Tammannstraße 6, 37077 Göttingen, Germany
| |
Collapse
|
5
|
Du Y, Meng Z, Yan Q, Wang CL, Tian Y, Duan W, Zhang S, Lin P. Deep potential for face-centered cubic Cu system at finite temperatures. Phys Chem Chem Phys 2022; 24:18361-18369. [DOI: 10.1039/d2cp02758e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Potential function is critical for molecular dynamics simulation and the state-of-the-art method generating potential functions used in molecular dynamics is based on machine learning with neural networks. This method provides...
Collapse
|
6
|
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.
Collapse
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
| |
Collapse
|
7
|
Dong HC, Ho TH, Nguyen TM, Kawazoe Y, Le HM. Dissociation of hydrogen peroxide in water and methanol through a biased molecular dynamics investigation. J Comput Chem 2021; 42:1344-1353. [PMID: 33977539 DOI: 10.1002/jcc.26539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Revised: 02/23/2021] [Accepted: 04/04/2021] [Indexed: 11/06/2022]
Abstract
The two dissociation channels of HOOH, namely, HOOH and HOOH, in water and methanol are investigated using umbrella-sampling ab initio molecular dynamics. Our potential of mean force calculations reveals the HOOH dissociation to be more favorable in methanol with a free energy barrier of 7.56 kcal/mol, while the HOOH dissociation possesses a free energy barrier of 11.46 kcal/mol. In water, the HOOH dissociation channel is more favorable (8.25 kcal/mol), while the HOOH dissociation process requires a higher free energy (11.28 kcal/mol). Such reaction favorability can be explained by inspecting the formation of secondary radical species during the course of multiple hydrogen donating-accepting processes in each reaction channel. The radical species, that is, H3 O• (observed in water) and CH3 OH2 • (observed in methanol), are the first subordinate species upon the HOOH dissociation. For the HOOH dissociation channel in methanol, the secondary species such as water and formaldehyde can be observed, while the re-generation of HOOH in water can be spotted.
Collapse
Affiliation(s)
- Hieu C Dong
- Future Materials and Devices Laboratory, Institute of Fundamental and Applied Sciences, Duy Tan University, Ho Chi Minh City, 700000, Vietnam.,Faculty of Natural Sciences, Duy Tan University, Da Nang, 550000, Vietnam
| | - Thi H Ho
- Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Vietnam
| | - Thu M Nguyen
- Future Materials and Devices Laboratory, Institute of Fundamental and Applied Sciences, Duy Tan University, Ho Chi Minh City, 700000, Vietnam.,Faculty of Natural Sciences, Duy Tan University, Da Nang, 550000, Vietnam
| | - Yoshiyuki Kawazoe
- New Industry Creation Hatchery Center, Tohoku University, Sendai, Japan.,Department of Physics and Nanotechnology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India
| | - Hung M Le
- Future Materials and Devices Laboratory, Institute of Fundamental and Applied Sciences, Duy Tan University, Ho Chi Minh City, 700000, Vietnam.,Faculty of Natural Sciences, Duy Tan University, Da Nang, 550000, Vietnam
| |
Collapse
|
8
|
Affiliation(s)
- Jörg Behler
- Universität Göttingen, Institut für Physikalische Chemie, Theoretische Chemie, Tammannstraße 6, 37077 Göttingen, Germany
| |
Collapse
|
9
|
Liu Z, Lin L, Jia Q, Cheng Z, Jiang Y, Guo Y, Ma J. Transferable Multilevel Attention Neural Network for Accurate Prediction of Quantum Chemistry Properties via Multitask Learning. J Chem Inf Model 2021; 61:1066-1082. [PMID: 33629839 DOI: 10.1021/acs.jcim.0c01224] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
The development of efficient models for predicting specific properties through machine learning is of great importance for the innovation of chemistry and material science. However, predicting global electronic structure properties like Frontier molecular orbital highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy levels and their HOMO-LUMO gaps from the small-sized molecule data to larger molecules remains a challenge. Here, we develop a multilevel attention neural network, named DeepMoleNet, to enable chemical interpretable insights being fused into multitask learning through (1) weighting contributions from various atoms and (2) taking the atom-centered symmetry functions (ACSFs) as the teacher descriptor. The efficient prediction of 12 properties including dipole moment, HOMO, and Gibbs free energy within chemical accuracy is achieved by using multiple benchmarks, both at the equilibrium and nonequilibrium geometries, including up to 110,000 records of data in QM9, 400,000 records in MD17, and 280,000 records in ANI-1ccx for random split evaluation. The good transferability for predicting larger molecules outside the training set is demonstrated in both equilibrium QM9 and Alchemy data sets at the density functional theory (DFT) level. Additional tests on nonequilibrium molecular conformations from DFT-based MD17 data set and ANI-1ccx data set with coupled cluster accuracy as well as the public test sets of singlet fission molecules, biomolecules, long oligomers, and protein with up to 140 atoms show reasonable predictions for thermodynamics and electronic structure properties. The proposed multilevel attention neural network is applicable to high-throughput screening of numerous chemical species in both equilibrium and nonequilibrium molecular spaces to accelerate rational designs of drug-like molecules, material candidates, and chemical reactions.
Collapse
Affiliation(s)
- Ziteng Liu
- Key Laboratory of Mesoscopic Chemistry of Ministry of Education, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, P. R. China
| | - Liqiang Lin
- National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, P. R. China
| | - Qingqing Jia
- Key Laboratory of Mesoscopic Chemistry of Ministry of Education, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, P. R. China
| | - Zheng Cheng
- Key Laboratory of Mesoscopic Chemistry of Ministry of Education, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, P. R. China
| | - Yanyan Jiang
- National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, P. R. China
| | - Yanwen Guo
- National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, P. R. China
| | - Jing Ma
- Key Laboratory of Mesoscopic Chemistry of Ministry of Education, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, P. R. China.,Jiangsu Key Laboratory of Advanced Organic Materials, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, P. R. China
| |
Collapse
|
10
|
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.
Collapse
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
| |
Collapse
|
11
|
Dral PO, Owens A, Dral A, Csányi G. Hierarchical machine learning of potential energy surfaces. J Chem Phys 2020; 152:204110. [DOI: 10.1063/5.0006498] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
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
| | - Alec Owens
- Department of Physics and Astronomy, University College London, Gower Street, WC1E 6BT London, United Kingdom
| | - Alexey Dral
- BigData Team, 1A Tormoznoye Shosse Off 17, Yaroslavl, Yaroslavl 150022, Russian Federation
| | - Gábor Csányi
- Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, United Kingdom
| |
Collapse
|
12
|
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.
Collapse
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
| |
Collapse
|
13
|
Mueller T, Hernandez A, Wang C. Machine learning for interatomic potential models. J Chem Phys 2020; 152:050902. [PMID: 32035452 DOI: 10.1063/1.5126336] [Citation(s) in RCA: 109] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The use of supervised machine learning to develop fast and accurate interatomic potential models is transforming molecular and materials research by greatly accelerating atomic-scale simulations with little loss of accuracy. Three years ago, Jörg Behler published a perspective in this journal providing an overview of some of the leading methods in this field. In this perspective, we provide an updated discussion of recent developments, emerging trends, and promising areas for future research in this field. We include in this discussion an overview of three emerging approaches to developing machine-learned interatomic potential models that have not been extensively discussed in existing reviews: moment tensor potentials, message-passing networks, and symbolic regression.
Collapse
Affiliation(s)
- Tim Mueller
- Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Alberto Hernandez
- Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Chuhong Wang
- Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
| |
Collapse
|
14
|
Abbott AS, Turney JM, Zhang B, Smith DGA, Altarawy D, Schaefer HF. PES-Learn: An Open-Source Software Package for the Automated Generation of Machine Learning Models of Molecular Potential Energy Surfaces. J Chem Theory Comput 2019; 15:4386-4398. [DOI: 10.1021/acs.jctc.9b00312] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Adam S. Abbott
- Center for Computational Quantum Chemistry, The University of Georgia, Athens, Georgia 30602, United States
| | - Justin M. Turney
- Center for Computational Quantum Chemistry, The University of Georgia, Athens, Georgia 30602, United States
| | - Boyi Zhang
- Center for Computational Quantum Chemistry, The University of Georgia, Athens, Georgia 30602, United States
| | - Daniel G. A. Smith
- Molecular Sciences Software Institute, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Doaa Altarawy
- Molecular Sciences Software Institute, Virginia Tech, Blacksburg, Virginia 24061, United States
- Computer and Systems Engineering Department, Alexandria University, Alexandria, Egypt
| | - Henry F. Schaefer
- Center for Computational Quantum Chemistry, The University of Georgia, Athens, Georgia 30602, United States
| |
Collapse
|
15
|
Brorsen KR. Reproducing global potential energy surfaces with continuous-filter convolutional neural networks. J Chem Phys 2019; 150:204104. [DOI: 10.1063/1.5093908] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Affiliation(s)
- Kurt R. Brorsen
- Department of Chemistry, University of Missouri, Columbia, Missouri 65203, USA
| |
Collapse
|
16
|
McConnell SR, Kästner J. Instanton rate constant calculations using interpolated potential energy surfaces in nonredundant, rotationally and translationally invariant coordinates. J Comput Chem 2019; 40:866-874. [PMID: 30677168 DOI: 10.1002/jcc.25770] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Revised: 11/25/2018] [Accepted: 11/27/2018] [Indexed: 11/07/2022]
Abstract
A trivial flaw in the utilization of artificial neural networks in interpolating chemical potential energy surfaces (PES) whose descriptors are Cartesian coordinates is their dependence on simple translations and rotations of the molecule under consideration. A different set of descriptors can be chosen to circumvent this problem, internuclear distances, inverse internuclear distances or z-matrix coordinates are three such descriptors. The objective is to use an interpolated PES in instanton rate constant calculations, hence information on the energy, gradient, and Hessian is required at coordinates in the vicinity of the tunneling path. Instanton theory relies on smoothly fitted Hessians, therefore we use energy, gradients, and Hessians in the training procedure. A major challenge is presented in the proper back-transformation of the output gradients and Hessians from internal coordinates to Cartesian coordinates. We perform comparisons between our method, a previous approach and on-the-fly rate constant calcuations on the hydrogen abstraction from methanol and on the hydrogen addition to isocyanic acid. © 2018Wiley Periodicals, Inc.
Collapse
Affiliation(s)
- Sean R McConnell
- Institute for Theoretical Chemistry, University of Stuttgart, 70569, Stuttgart, Germany
| | - Johannes Kästner
- Institute for Theoretical Chemistry, University of Stuttgart, 70569, Stuttgart, Germany
| |
Collapse
|
17
|
Petty C, Spada RFK, Machado FBC, Poirier B. Accurate rovibrational energies of ozone isotopologues up toJ= 10 utilizing artificial neural networks. J Chem Phys 2018; 149:024307. [DOI: 10.1063/1.5036602] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Corey Petty
- Departamento de Química, Instituto Tecnológico de Aeronáutica, São José dos Campos, 12.228-900, SP, Brazil
| | - Rene F. K. Spada
- Departamento de Física, Instituto Tecnológico de Aeronáutica, São José dos Campos, 12.228-900, SP, Brazil
| | - Francisco B. C. Machado
- Departamento de Química, Instituto Tecnológico de Aeronáutica, São José dos Campos, 12.228-900, SP, Brazil
| | - Bill Poirier
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas 79409, USA
| |
Collapse
|
18
|
Cooper AM, Hallmen PP, Kästner J. Potential energy surface interpolation with neural networks for instanton rate calculations. J Chem Phys 2018. [DOI: 10.1063/1.5015950] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Affiliation(s)
- April M. Cooper
- Institute for Theoretical Chemistry, University of Stuttgart, Pfaffenwaldring 55, 70569 Stuttgart, Germany
| | - Philipp P. Hallmen
- Institute for Theoretical Chemistry, University of Stuttgart, Pfaffenwaldring 55, 70569 Stuttgart, Germany
| | - Johannes Kästner
- Institute for Theoretical Chemistry, University of Stuttgart, Pfaffenwaldring 55, 70569 Stuttgart, Germany
| |
Collapse
|
19
|
Sandhiya L, Zipse H. OO bond homolysis in hydrogen peroxide. J Comput Chem 2017; 38:2186-2192. [DOI: 10.1002/jcc.24870] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2017] [Revised: 05/11/2017] [Accepted: 06/13/2017] [Indexed: 11/08/2022]
Affiliation(s)
| | - Hendrik Zipse
- Department of Chemistry; LMU München; München D-81377 Germany
| |
Collapse
|
20
|
Jiang B, Li J, Guo H. Potential energy surfaces from high fidelity fitting ofab initiopoints: the permutation invariant polynomial - neural network approach. INT REV PHYS CHEM 2016. [DOI: 10.1080/0144235x.2016.1200347] [Citation(s) in RCA: 210] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
|
21
|
Ho TH, Pham-Tran NN, Kawazoe Y, Le HM. Ab Initio Investigation of O-H Dissociation from the Al-OH2 Complex Using Molecular Dynamics and Neural Network Fitting. J Phys Chem A 2016; 120:346-55. [PMID: 26741404 DOI: 10.1021/acs.jpca.5b09497] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The dissociation dynamics of the O-H bond in Al-OH2 is investigated on an approximated ab initio potential energy surface (PES). By adopting a dynamic sampling method, we obtain a database of 92 834 configurations. The potential energy for each point is calculated using MP2/6-311G (3df, 2p) calculations; then, a 60-neuron feed-forward neural network is utilized to fit the data to construct an analytic PES. The root-mean-square error (rmse) for the training set is reported as 0.0036 eV, while the rmse for the independent testing set is 0.0034 eV. Such excellent fitting accuracy indeed confirms the reliability of the constructed PES. Subsequently, quasi-classical molecular dynamics (MD) trajectories are performed on the constructed PES at various levels of vibrational excitation in the range of 1.03 to 2.23 eV to investigate the probability of O-H bond dissociation. The results indicate a linear relationship between reaction probability and internal energy, from which we can determine the minimum activation internal energy required for the dissociation as 0.62 eV. Moreover, the O-H bond rupture is shown to be highly correlated with the formation of Al-O bond.
Collapse
Affiliation(s)
- Thi H Ho
- Department of Materials Science, University of Science, Vietnam National University , Ho Chi Minh City, Vietnam
| | - Nguyen-Nguyen Pham-Tran
- Department of Chemistry, University of Science, Vietnam National University , Ho Chi Minh City, Vietnam
| | - Yoshiyuki Kawazoe
- New Industry Creation Hatchery Center, Tohoku University , Sendai City, Japan.,Thermophysics Institute, Siberian Branch, Russian Academy of Sciences , Novosibirsk, Russia
| | - Hung M Le
- Computational Chemistry Research Group, Ton Duc Thang University , Ho Chi Minh City, Vietnam.,Faculty of Applied Sciences, Ton Duc Thang University , Ho Chi Minh City, Vietnam
| |
Collapse
|
22
|
An implementation of the Levenberg–Marquardt algorithm for simultaneous-energy-gradient fitting using two-layer feed-forward neural networks. Chem Phys Lett 2015. [DOI: 10.1016/j.cplett.2015.04.019] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
23
|
Li J, Jiang B, Song H, Ma J, Zhao B, Dawes R, Guo H. From ab Initio Potential Energy Surfaces to State-Resolved Reactivities: X + H2O ↔ HX + OH [X = F, Cl, and O(3P)] Reactions. J Phys Chem A 2015; 119:4667-87. [DOI: 10.1021/acs.jpca.5b02510] [Citation(s) in RCA: 85] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Jun Li
- Department
of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, United States
- School of Chemistry
and Chemical Engineering, Chongqing University, Chongqing 400044, China
| | - Bin Jiang
- Department
of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, United States
| | - Hongwei Song
- Department
of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, United States
| | - Jianyi Ma
- Institute of Atomic
and Molecular Physics, Sichuan University, Chengdu, Sichuan 610065, China
| | - Bin Zhao
- Department
of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, United States
| | - Richard Dawes
- Department
of Chemistry, Missouri University of Science and Technology, Rolla, Missouri 65409, United States
| | - Hua Guo
- Department
of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, United States
| |
Collapse
|
24
|
Gastegger M, Marquetand P. High-Dimensional Neural Network Potentials for Organic Reactions and an Improved Training Algorithm. J Chem Theory Comput 2015; 11:2187-98. [PMID: 26574419 DOI: 10.1021/acs.jctc.5b00211] [Citation(s) in RCA: 81] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Artificial neural networks (NNs) represent a relatively recent approach for the prediction of molecular potential energies, suitable for simulations of large molecules and long time scales. By using NNs to fit electronic structure data, it is possible to obtain empirical potentials of high accuracy combined with the computational efficiency of conventional force fields. However, as opposed to the latter, changing bonding patterns and unusual coordination geometries can be described due to the underlying flexible functional form of the NNs. One of the most promising approaches in this field is the high-dimensional neural network (HDNN) method, which is especially adapted to the prediction of molecular properties. While HDNNs have been mostly used to model solid state systems and surface interactions, we present here the first application of the HDNN approach to an organic reaction, the Claisen rearrangement of allyl vinyl ether to 4-pentenal. To construct the corresponding HDNN potential, a new training algorithm is introduced. This algorithm is termed "element-decoupled" global extended Kalman filter (ED-GEKF) and is based on the decoupled Kalman filter. Using a metadynamics trajectory computed with density functional theory as reference data, we show that the ED-GEKF exhibits superior performance - both in terms of accuracy and training speed - compared to other variants of the Kalman filter hitherto employed in HDNN training. In addition, the effect of including forces during ED-GEKF training on the resulting potentials was studied.
Collapse
Affiliation(s)
- Michael Gastegger
- Institute of Theoretical Chemistry, University of Vienna , Währinger Str. 17, 1090 Vienna, Austria
| | - Philipp Marquetand
- Institute of Theoretical Chemistry, University of Vienna , Währinger Str. 17, 1090 Vienna, Austria
| |
Collapse
|
25
|
Xu X, Chen J, Zhang DH. Global Potential Energy Surface for the H+CH4↔H2+CH3 Reaction using Neural Networks. CHINESE J CHEM PHYS 2014. [DOI: 10.1063/1674-0068/27/04/373-379] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
|
26
|
Behler J. Representing potential energy surfaces by high-dimensional neural network potentials. JOURNAL OF PHYSICS. CONDENSED MATTER : AN INSTITUTE OF PHYSICS JOURNAL 2014; 26:183001. [PMID: 24758952 DOI: 10.1088/0953-8984/26/18/183001] [Citation(s) in RCA: 158] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
The development of interatomic potentials employing artificial neural networks has seen tremendous progress in recent years. While until recently the applicability of neural network potentials (NNPs) has been restricted to low-dimensional systems, this limitation has now been overcome and high-dimensional NNPs can be used in large-scale molecular dynamics simulations of thousands of atoms. NNPs are constructed by adjusting a set of parameters using data from electronic structure calculations, and in many cases energies and forces can be obtained with very high accuracy. Therefore, NNP-based simulation results are often very close to those gained by a direct application of first-principles methods. In this review, the basic methodology of high-dimensional NNPs will be presented with a special focus on the scope and the remaining limitations of this approach. The development of NNPs requires substantial computational effort as typically thousands of reference calculations are required. Still, if the problem to be studied involves very large systems or long simulation times this overhead is regained quickly. Further, the method is still limited to systems containing about three or four chemical elements due to the rapidly increasing complexity of the configuration space, although many atoms of each species can be present. Due to the ability of NNPs to describe even extremely complex atomic configurations with excellent accuracy irrespective of the nature of the atomic interactions, they represent a general and therefore widely applicable technique, e.g. for addressing problems in materials science, for investigating properties of interfaces, and for studying solvation processes.
Collapse
Affiliation(s)
- J Behler
- Lehrstuhl für Theoretische Chemie, Ruhr-Universität Bochum, D-44780 Bochum, Germany
| |
Collapse
|
27
|
Li J, Jiang B, Guo H. Permutation invariant polynomial neural network approach to fitting potential energy surfaces. II. Four-atom systems. J Chem Phys 2013; 139:204103. [DOI: 10.1063/1.4832697] [Citation(s) in RCA: 237] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
|
28
|
Nguyen-Truong HT, Thi CM, Le HM. Theoretical investigations of BBS (singlet)→BSB (triplet) transformation on a potential energy surface obtained from neural network fitting. Chem Phys 2013. [DOI: 10.1016/j.chemphys.2013.09.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
29
|
|
30
|
Jiang B, Guo H. Permutation invariant polynomial neural network approach to fitting potential energy surfaces. J Chem Phys 2013; 139:054112. [DOI: 10.1063/1.4817187] [Citation(s) in RCA: 320] [Impact Index Per Article: 29.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
|
31
|
Chen J, Xu X, Xu X, Zhang DH. A global potential energy surface for the H2 + OH ↔ H2O + H reaction using neural networks. J Chem Phys 2013; 138:154301. [DOI: 10.1063/1.4801658] [Citation(s) in RCA: 139] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
|
32
|
Nguyen HTT, Le HM. Modified Feed-Forward Neural Network Structures and Combined-Function-Derivative Approximations Incorporating Exchange Symmetry for Potential Energy Surface Fitting. J Phys Chem A 2012; 116:4629-38. [DOI: 10.1021/jp3020386] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Hieu T. T. Nguyen
- Faculty of Materials Science, College
of Science, Vietnam National University, Ho Chi Minh City, Vietnam
| | - Hung M. Le
- Faculty of Materials Science, College
of Science, Vietnam National University, Ho Chi Minh City, Vietnam
| |
Collapse
|
33
|
Le ATH, Vu NH, Dinh TS, Cao TM, Le HM. Molecular dynamics investigations of chlorine peroxide dissociation on a neural network ab initio potential energy surface. Theor Chem Acc 2012. [DOI: 10.1007/s00214-012-1158-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|
34
|
Le HM, Dinh TS, Le HV. Molecular Dynamics Investigations of Ozone on an Ab Initio Potential Energy Surface with the Utilization of Pattern-Recognition Neural Network for Accurate Determination of Product Formation. J Phys Chem A 2011; 115:10862-70. [DOI: 10.1021/jp206531s] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Hung M. Le
- Faculty of Materials Science, College of Science, Vietnam National University, Ho Chi Minh City, Vietnam 750000
| | - Thach S. Dinh
- Faculty of Materials Science, College of Science, Vietnam National University, Ho Chi Minh City, Vietnam 750000
| | - Hieu V. Le
- Faculty of Materials Science, College of Science, Vietnam National University, Ho Chi Minh City, Vietnam 750000
| |
Collapse
|
35
|
Balabin RM, Lomakina EI. Support vector machine regression (LS-SVM)—an alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data? Phys Chem Chem Phys 2011; 13:11710-8. [DOI: 10.1039/c1cp00051a] [Citation(s) in RCA: 139] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
|
36
|
Behler J. Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations. Phys Chem Chem Phys 2011; 13:17930-55. [DOI: 10.1039/c1cp21668f] [Citation(s) in RCA: 477] [Impact Index Per Article: 36.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
|
37
|
Malshe M, Raff LM, Hagan M, Bukkapatnam S, Komanduri R. Input vector optimization of feed-forward neural networks for fitting ab initio potential-energy databases. J Chem Phys 2010; 132:204103. [PMID: 20515084 DOI: 10.1063/1.3431624] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
The variation in the fitting accuracy of neural networks (NNs) when used to fit databases comprising potential energies obtained from ab initio electronic structure calculations is investigated as a function of the number and nature of the elements employed in the input vector to the NN. Ab initio databases for H(2)O(2), HONO, Si(5), and H(2)C[Double Bond]CHBr were employed in the investigations. These systems were chosen so as to include four-, five-, and six-body systems containing first, second, third, and fourth row elements with a wide variety of chemical bonding and whose conformations cover a wide range of structures that occur under high-energy machining conditions and in chemical reactions involving cis-trans isomerizations, six different types of two-center bond ruptures, and two different three-center dissociation reactions. The ab initio databases for these systems were obtained using density functional theory/B3LYP, MP2, and MP4 methods with extended basis sets. A total of 31 input vectors were investigated. In each case, the elements of the input vector were chosen from interatomic distances, inverse powers of the interatomic distance, three-body angles, and dihedral angles. Both redundant and nonredundant input vectors were investigated. The results show that among all the input vectors investigated, the set employed in the Z-matrix specification of the molecular configurations in the electronic structure calculations gave the lowest NN fitting accuracy for both Si(5) and vinyl bromide. The underlying reason for this result appears to be the discontinuity present in the dihedral angle for planar geometries. The use of trigometric functions of the angles as input elements produced significantly improved fitting accuracy as this choice eliminates the discontinuity. The most accurate fitting was obtained when the elements of the input vector were taken to have the form R(ij) (-n), where the R(ij) are the interatomic distances. When the Levenberg-Marquardt procedure was modified to permit error minimization with respect to n as well as the weights and biases of the NN, the optimum powers were all found to lie in the range of 1.625-2.38 for the four systems studied. No statistically significant increase in fitting accuracy was achieved for vinyl bromide when a different value of n was employed and optimized for each bond type. The rate of change in the fitting error with n is found to be very small when n is near its optimum value. Consequently, good fitting accuracy can be achieved by employing a value of n in the middle of the above range. The use of interparticle distances as elements of the input vector rather than the Z-matrix variables employed in the electronic structure calculations is found to reduce the rms fitting errors by factors of 8.86 and 1.67 for Si(5) and vinyl bromide, respectively. If the interparticle distances are replaced with input elements of the form R(ij) (-n) with n optimized, further reductions in the rms error by a factor of 1.31 to 2.83 for the four systems investigated are obtained. A major advantage of using this procedure to increase NN fitting accuracy rather than increasing the number of neurons or the size of the database is that the required increase in computational effort is very small.
Collapse
Affiliation(s)
- M Malshe
- Mechanical and Aerospace Engineering, Oklahoma State University, 218 Engineering North Stillwater, Oklahoma 74078, USA
| | | | | | | | | |
Collapse
|
38
|
Handley CM, Popelier PLA. Potential Energy Surfaces Fitted by Artificial Neural Networks. J Phys Chem A 2010; 114:3371-83. [DOI: 10.1021/jp9105585] [Citation(s) in RCA: 241] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
- Chris M. Handley
- Manchester Interdisciplinary Biocentre (MIB), 131 Princess Street, Manchester M1 7DN, Great Britain, School of Chemistry, University of Manchester, Oxford Road, Manchester M13 9PL, Great Britain, and The University of Warwick, Department of Chemistry, Library Road, Coventry CV4 7AL, Great Britain
| | - Paul L. A. Popelier
- Manchester Interdisciplinary Biocentre (MIB), 131 Princess Street, Manchester M1 7DN, Great Britain, School of Chemistry, University of Manchester, Oxford Road, Manchester M13 9PL, Great Britain, and The University of Warwick, Department of Chemistry, Library Road, Coventry CV4 7AL, Great Britain
| |
Collapse
|
39
|
Le HM, Raff LM. Molecular Dynamics Investigation of the Bimolecular Reaction BeH + H2 → BeH2 + H on an ab Initio Potential-Energy Surface Obtained Using Neural Network Methods with Both Potential and Gradient Accuracy Determination. J Phys Chem A 2009; 114:45-53. [DOI: 10.1021/jp907507z] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Hung M. Le
- Chemistry Department, Oklahoma State University, Stillwater, Oklahoma 74078
| | - Lionel M. Raff
- Chemistry Department, Oklahoma State University, Stillwater, Oklahoma 74078
| |
Collapse
|
40
|
Malshe M, Pukrittayakamee A, Raff LM, Hagan M, Bukkapatnam S, Komanduri R. Accurate prediction of higher-level electronic structure energies for large databases using neural networks, Hartree–Fock energies, and small subsets of the database. J Chem Phys 2009; 131:124127. [DOI: 10.1063/1.3231686] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
|