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Deffner M, Weise MP, Zhang H, Mücke M, Proppe J, Franco I, Herrmann C. Learning Conductance: Gaussian Process Regression for Molecular Electronics. J Chem Theory Comput 2023; 19:992-1002. [PMID: 36692968 DOI: 10.1021/acs.jctc.2c00648] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Experimental studies of charge transport through single molecules often rely on break junction setups, where molecular junctions are repeatedly formed and broken while measuring the conductance, leading to a statistical distribution of conductance values. Modeling this experimental situation and the resulting conductance histograms is challenging for theoretical methods, as computations need to capture structural changes in experiments, including the statistics of junction formation and rupture. This type of extensive structural sampling implies that even when evaluating conductance from computationally efficient electronic structure methods, which typically are of reduced accuracy, the evaluation of conductance histograms is too expensive to be a routine task. Highly accurate quantum transport computations are only computationally feasible for a few selected conformations and thus necessarily ignore the rich conformational space probed in experiments. To overcome these limitations, we investigate the potential of machine learning for modeling conductance histograms, in particular by Gaussian process regression. We show that by selecting specific structural parameters as features, Gaussian process regression can be used to efficiently predict the zero-bias conductance from molecular structures, reducing the computational cost of simulating conductance histograms by an order of magnitude. This enables the efficient calculation of conductance histograms even on the basis of computationally expensive first-principles approaches by effectively reducing the number of necessary charge transport calculations, paving the way toward their routine evaluation.
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Affiliation(s)
- Michael Deffner
- Institute of Inorganic and Applied Chemistry, University of Hamburg, Hamburg22761, Germany.,The Hamburg Centre for Ultrafast Imaging, Hamburg22761, Germany
| | - Marc Philipp Weise
- Institute of Inorganic and Applied Chemistry, University of Hamburg, Hamburg22761, Germany
| | - Haitao Zhang
- Institute of Inorganic and Applied Chemistry, University of Hamburg, Hamburg22761, Germany
| | - Maike Mücke
- Institute of Physical Chemistry, Georg-August University, Göttingen37077, Germany
| | - Jonny Proppe
- Institute of Physical and Theoretical Chemistry, TU Braunschweig, Braunschweig38106, Germany
| | - Ignacio Franco
- Departments of Chemistry and Physics, University of Rochester, Rochester, New York14627-0216, United States
| | - Carmen Herrmann
- Institute of Inorganic and Applied Chemistry, University of Hamburg, Hamburg22761, Germany.,The Hamburg Centre for Ultrafast Imaging, Hamburg22761, Germany
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2
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Muther T, Dahaghi AK, Syed FI, Van Pham V. Physical laws meet machine intelligence: current developments and future directions. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10329-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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3
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Muzas A, Serrano Jiménez A, Ovčar J, Lončarić I, Alducin M, Juaristi JI. Absence of isotope effects in the photo-induced desorption of CO from saturated Pd(111) at high laser fluence. Chem Phys 2022. [DOI: 10.1016/j.chemphys.2022.111518] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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4
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Shi H, Liu T, Fu Y, Wu H, Fu B, Zhang DH. Fundamental invariant-neural network potential energy surface and dissociative chemisorption dynamics of N2 on rigid Ni(111). COMPUT THEOR CHEM 2022. [DOI: 10.1016/j.comptc.2022.113679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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5
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Shu Y, Varga Z, Kanchanakungwankul S, Zhang L, Truhlar DG. Diabatic States of Molecules. J Phys Chem A 2022; 126:992-1018. [PMID: 35138102 DOI: 10.1021/acs.jpca.1c10583] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Quantitative simulations of electronically nonadiabatic molecular processes require both accurate dynamics algorithms and accurate electronic structure information. Direct semiclassical nonadiabatic dynamics is expensive due to the high cost of electronic structure calculations, and hence it is limited to small systems, limited ensemble averaging, ultrafast processes, and/or electronic structure methods that are only semiquantitatively accurate. The cost of dynamics calculations can be made manageable if analytic fits are made to the electronic structure data, and such fits are most conveniently carried out in a diabatic representation because the surfaces are smooth and the couplings between states are smooth scalar functions. Diabatic representations, unlike the adiabatic ones produced by most electronic structure methods, are not unique, and finding suitable diabatic representations often involves time-consuming nonsystematic diabatization steps. The biggest drawback of using diabatic bases is that it can require large amounts of effort to perform a globally consistent diabatization, and one of our goals has been to develop methods to do this efficiently and automatically. In this Feature Article, we introduce the mathematical framework of diabatic representations, and we discuss diabatization methods, including adiabatic-to-diabatic transformations and recent progress toward the goal of automatization.
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Affiliation(s)
- Yinan Shu
- Department of Chemistry, Chemical Theory Center, and Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United States
| | - Zoltan Varga
- Department of Chemistry, Chemical Theory Center, and Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United States
| | - Siriluk Kanchanakungwankul
- Department of Chemistry, Chemical Theory Center, and Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United States
| | - Linyao Zhang
- Department of Chemistry, Chemical Theory Center, and Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United States.,School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, P. R. China
| | - Donald G Truhlar
- Department of Chemistry, Chemical Theory Center, and Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United States
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6
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Effects of vibrational and rotational excitations on the dissociative chemisorption dynamics of N 2 on Fe(111). CHINESE J CHEM PHYS 2022. [DOI: 10.1063/1674-0068/cjcp2201009] [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]
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7
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Kroes GJ. Computational approaches to dissociative chemisorption on metals: towards chemical accuracy. Phys Chem Chem Phys 2021; 23:8962-9048. [PMID: 33885053 DOI: 10.1039/d1cp00044f] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
We review the state-of-the-art in the theory of dissociative chemisorption (DC) of small gas phase molecules on metal surfaces, which is important to modeling heterogeneous catalysis for practical reasons, and for achieving an understanding of the wealth of experimental information that exists for this topic, for fundamental reasons. We first give a quick overview of the experimental state of the field. Turning to the theory, we address the challenge that barrier heights (Eb, which are not observables) for DC on metals cannot yet be calculated with chemical accuracy, although embedded correlated wave function theory and diffusion Monte-Carlo are moving in this direction. For benchmarking, at present chemically accurate Eb can only be derived from dynamics calculations based on a semi-empirically derived density functional (DF), by computing a sticking curve and demonstrating that it is shifted from the curve measured in a supersonic beam experiment by no more than 1 kcal mol-1. The approach capable of delivering this accuracy is called the specific reaction parameter (SRP) approach to density functional theory (DFT). SRP-DFT relies on DFT and on dynamics calculations, which are most efficiently performed if a potential energy surface (PES) is available. We therefore present a brief review of the DFs that now exist, also considering their performance on databases for Eb for gas phase reactions and DC on metals, and for adsorption to metals. We also consider expressions for SRP-DFs and briefly discuss other electronic structure methods that have addressed the interaction of molecules with metal surfaces. An overview is presented of dynamical models, which make a distinction as to whether or not, and which dissipative channels are modeled, the dissipative channels being surface phonons and electronically non-adiabatic channels such as electron-hole pair excitation. We also discuss the dynamical methods that have been used, such as the quasi-classical trajectory method and quantum dynamical methods like the time-dependent wave packet method and the reaction path Hamiltonian method. Limits on the accuracy of these methods are discussed for DC of diatomic and polyatomic molecules on metal surfaces, paying particular attention to reduced dimensionality approximations that still have to be invoked in wave packet calculations on polyatomic molecules like CH4. We also address the accuracy of fitting methods, such as recent machine learning methods (like neural network methods) and the corrugation reducing procedure. In discussing the calculation of observables we emphasize the importance of modeling the properties of the supersonic beams in simulating the sticking probability curves measured in the associated experiments. We show that chemically accurate barrier heights have now been extracted for DC in 11 molecule-metal surface systems, some of which form the most accurate core of the only existing database of Eb for DC reactions on metal surfaces (SBH10). The SRP-DFs (or candidate SRP-DFs) that have been derived show transferability in many cases, i.e., they have been shown also to yield chemically accurate Eb for chemically related systems. This can in principle be exploited in simulating rates of catalyzed reactions on nano-particles containing facets and edges, as SRP-DFs may be transferable among systems in which a molecule dissociates on low index and stepped surfaces of the same metal. In many instances SRP-DFs have allowed important conclusions regarding the mechanisms underlying observed experimental trends. An important recent observation is that SRP-DFT based on semi-local exchange DFs has so far only been successful for systems for which the difference of the metal work function and the molecule's electron affinity exceeds 7 eV. A main challenge to SRP-DFT is to extend its applicability to the other systems, which involve a range of important DC reactions of e.g. O2, H2O, NH3, CO2, and CH3OH. Recent calculations employing a PES based on a screened hybrid exchange functional suggest that the road to success may be based on using exchange functionals of this category.
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Affiliation(s)
- Geert-Jan Kroes
- Leiden Institute of Chemistry, Gorlaeus Laboratories, Leiden University, P.O. Box 9502, 2300 RA Leiden, The Netherlands.
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8
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Affiliation(s)
- Jörg Behler
- Universität Göttingen, Institut für Physikalische Chemie, Theoretische Chemie, Tammannstraße 6, 37077 Göttingen, Germany
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9
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Gerrits N, Smeets EWF, Vuckovic S, Powell AD, Doblhoff-Dier K, Kroes GJ. Density Functional Theory for Molecule-Metal Surface Reactions: When Does the Generalized Gradient Approximation Get It Right, and What to Do If It Does Not. J Phys Chem Lett 2020; 11:10552-10560. [PMID: 33295770 PMCID: PMC7751010 DOI: 10.1021/acs.jpclett.0c02452] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
While density functional theory (DFT) is perhaps the most used electronic structure theory in chemistry, many of its practical aspects remain poorly understood. For instance, DFT at the generalized gradient approximation (GGA) tends to fail miserably at describing gas-phase reaction barriers, while it performs surprisingly well for many molecule-metal surface reactions. GGA-DFT also fails for many systems in the latter category, and up to now it has not been clear when one may expect it to work. We show that GGA-DFT tends to work if the difference between the work function of the metal and the molecule's electron affinity is greater than ∼7 eV and to fail if this difference is smaller, with sticking of O2 on Al(111) being a spectacular example. Using dynamics calculations we show that, for this system, the DFT problem may be solved as done for gas-phase reactions, i.e., by resorting to hybrid functionals, but using screening at long-range to obtain a correct description of the metal. Our results suggest the GGA error in the O2 + Al(111) barrier height to be functional driven. Our results also suggest the possibility to compute potential energy surfaces for the difficult-to-treat systems with computationally cheap nonself-consistent calculations in which a hybrid functional is applied to a GGA density.
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Affiliation(s)
- Nick Gerrits
- Leiden
Institute of Chemistry, Leiden University, Gorlaeus Laboratories, P.O. Box 9502, 2300 RA Leiden, The Netherlands
| | - Egidius W. F. Smeets
- Leiden
Institute of Chemistry, Leiden University, Gorlaeus Laboratories, P.O. Box 9502, 2300 RA Leiden, The Netherlands
| | - Stefan Vuckovic
- Department
of Chemistry, University of California, Irvine, California 92697, United States
| | - Andrew D. Powell
- Leiden
Institute of Chemistry, Leiden University, Gorlaeus Laboratories, P.O. Box 9502, 2300 RA Leiden, The Netherlands
| | - Katharina Doblhoff-Dier
- Leiden
Institute of Chemistry, Leiden University, Gorlaeus Laboratories, P.O. Box 9502, 2300 RA Leiden, The Netherlands
| | - Geert-Jan Kroes
- Leiden
Institute of Chemistry, Leiden University, Gorlaeus Laboratories, P.O. Box 9502, 2300 RA Leiden, The Netherlands
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10
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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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11
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Chen X, Chen D, Weng M, Jiang Y, Wei GW, Pan F. Topology-Based Machine Learning Strategy for Cluster Structure Prediction. J Phys Chem Lett 2020; 11:4392-4401. [PMID: 32320253 PMCID: PMC7351018 DOI: 10.1021/acs.jpclett.0c00974] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
In cluster physics, the determination of the ground-state structure of medium-sized and large-sized clusters is a challenge due to the number of local minimal values on the potential energy surface growing exponentially with cluster size. Although machine learning approaches have had much success in materials sciences, their applications in clusters are often hindered by the geometric complexity clusters. Persistent homology provides a new topological strategy to simplify geometric complexity while retaining important chemical and physical information without having to "downgrade" the original data. We further propose persistent pairwise independence (PPI) to enhance the predictive power of persistent homology. We construct topology-based machine learning models to reveal hidden structure-energy relationships in lithium (Li) clusters. We integrate the topology-based machine learning models, a particle swarm optimization algorithm, and density functional theory calculations to accelerate the search of the globally stable structure of clusters.
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Affiliation(s)
- Xin Chen
- School of Advanced Materials, Shenzhen Graduate School, Peking University, Shenzhen 518055, People's Republic of China
| | - Dong Chen
- School of Advanced Materials, Shenzhen Graduate School, Peking University, Shenzhen 518055, People's Republic of China
| | - Mouyi Weng
- School of Advanced Materials, Shenzhen Graduate School, Peking University, Shenzhen 518055, People's Republic of China
| | - Yi Jiang
- School of Advanced Materials, Shenzhen Graduate School, Peking University, Shenzhen 518055, People's Republic of China
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Feng Pan
- School of Advanced Materials, Shenzhen Graduate School, Peking University, Shenzhen 518055, People's Republic of China
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12
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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
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13
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Andolina CM, Williamson P, Saidi WA. Optimization and validation of a deep learning CuZr atomistic potential: Robust applications for crystalline and amorphous phases with near-DFT accuracy. J Chem Phys 2020; 152:154701. [PMID: 32321274 DOI: 10.1063/5.0005347] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
We show that a deep-learning neural network potential (DP) based on density functional theory (DFT) calculations can well describe Cu-Zr materials, an example of a binary alloy system, that can coexist in as ordered intermetallic and as an amorphous phase. The complex phase diagram for Cu-Zr makes it a challenging system for traditional atomistic force-fields that cannot accurately describe the different properties and phases. Instead, we show that a DP approach using a large database with ∼300k configurations can render results generally on par with DFT. The training set includes configurations of pristine and bulk elementary metals and intermetallic structures in the liquid and solid phases in addition to slab and amorphous configurations. The DP model was validated by comparing bulk properties such as lattice constants, elastic constants, bulk moduli, phonon spectra, and surface energies to DFT values for identical structures. Furthermore, we contrast the DP results with values obtained using well-established two embedded atom method potentials. Overall, our DP potential provides near DFT accuracy for the different Cu-Zr phases but with a fraction of its computational cost, thus enabling accurate computations of realistic atomistic models, especially for the amorphous phase.
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Affiliation(s)
- Christopher M Andolina
- Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, Pennsylvania 15216, USA
| | - Philip Williamson
- Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, Pennsylvania 15216, USA
| | - Wissam A Saidi
- Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, Pennsylvania 15216, USA
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14
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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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15
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Smith B, Akimov AV. Modeling nonadiabatic dynamics in condensed matter materials: some recent advances and applications. JOURNAL OF PHYSICS. CONDENSED MATTER : AN INSTITUTE OF PHYSICS JOURNAL 2020; 32:073001. [PMID: 31661681 DOI: 10.1088/1361-648x/ab5246] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
This review focuses on recent developments in the field of nonadiabatic molecular dynamics (NA-MD), with particular attention given to condensed-matter systems. NA-MD simulations for small molecular systems can be performed using high-level electronic structure (ES) calculations, methods accounting for the quantization of nuclear motion, and using fewer approximations in the dynamical methodology itself. Modeling condensed-matter systems imposes many limitations on various aspects of NA-MD computations, requiring approximations at various levels of theory-from the ES, to the ways in which the coupling of electrons and nuclei are accounted for. Nonetheless, the approximate treatment of NA-MD in condensed-phase materials has gained a spin lately in many applied studies. A number of advancements of the methodology and computational tools have been undertaken, including general-purpose methods, as well as those tailored to nanoscale and condensed matter systems. This review summarizes such methodological and software developments, puts them into the broader context of existing approaches, and highlights some of the challenges that remain to be solved.
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Affiliation(s)
- Brendan Smith
- Department of Chemistry, University at Buffalo, The State University of New York, Buffalo, New York 14260-3000, United States of America
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16
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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
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17
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Jiang B, Guo H. Dynamics in reactions on metal surfaces: A theoretical perspective. J Chem Phys 2019; 150:180901. [PMID: 31091904 DOI: 10.1063/1.5096869] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Recent advances in theoretical characterization of reaction dynamics on metal surfaces are reviewed. It is shown that the widely available density functional theory of metals and their interactions with molecules have enabled first principles theoretical models for treating surface reaction dynamics. The new theoretical tools include methods to construct high-dimensional adiabatic potential energy surfaces, to characterize nonadiabatic processes within the electronic friction models, and to describe dynamics both quantum mechanically and classically. Three prototypical surface reactions, namely, dissociative chemisorption, Eley-Rideal reactions, and recombinative desorption, are surveyed with a focus on some representative examples. While principles governing gas phase reaction dynamics may still be applicable, the presence of the surface introduces a higher level of complexity due to strong interaction between the molecular species and metal substrate. Furthermore, most of these reactive processes are impacted by energy exchange with surface phonons and/or electron-hole pair excitations. These theoretical studies help to interpret and rationalize experimental observations and, in some cases, guide experimental explorations. Knowledge acquired in these fundamental studies is expected to impact many practical problems in a wide range of interfacial processes.
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Affiliation(s)
- 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 230026, China
| | - Hua Guo
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, USA
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18
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Ghassemi E, Somers MF, Kroes GJ. Assessment of Two Problems of Specific Reaction Parameter Density Functional Theory: Sticking and Diffraction of H 2 on Pt(111). THE JOURNAL OF PHYSICAL CHEMISTRY. C, NANOMATERIALS AND INTERFACES 2019; 123:10406-10418. [PMID: 31049122 PMCID: PMC6488140 DOI: 10.1021/acs.jpcc.9b00981] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Revised: 03/26/2019] [Indexed: 06/09/2023]
Abstract
It is important that theory is able to accurately describe dissociative chemisorption reactions on metal surfaces, as such reactions are often rate-controlling in heterogeneously catalyzed processes. Chemically accurate theoretical descriptions have recently been obtained on the basis of the specific reaction parameter (SRP) approach to density functional (DF) theory (DFT), allowing reaction barriers to be obtained with chemical accuracy. However, being semiempirical, this approach suffers from two basic problems. The first is that sticking probabilities (to which SRP density functionals (DFs) are usually fitted) might show differences across experiments, of which the origins are not always clear. The second is that it has proven hard to use experiments on diffractive scattering of H2 from metals for validation purposes, as dynamics calculations using a SRP-DF may yield a rather poor description of the measured data, especially if the potential used contains a van der Waals well. We address the first problem by performing dynamics calculations on three sets of molecular beam experiments on D2 + Pt(111), using four sets of molecular beam parameters to obtain sticking probabilities, and the SRP-DF recently fitted to one set of experiments on D2 + Pt(111). It is possible to reproduce all three sets of experiments with chemical accuracy with the aid of two sets of molecular beam parameters. The theoretical simulations with the four different sets of beam parameters allow one to determine for which range of incidence conditions the experiments should agree well and for which conditions they should show specific differences. This allows one to arrive at conclusions about the quality of the experiments and about problems that might affect the experiments. Our calculations on diffraction of H2 scattering from Pt(111) show both quantitative and qualitative differences with previously measured diffraction probabilities, which were Debye-Waller (DW)-extrapolated to 0 K. We suggest that DW extrapolation, which is appropriate for direct scattering, might fail if the scattering is affected by the presence of a van der Waals well and that theory should attempt to model surface atom motion for reproducing diffraction experiments performed for surface temperatures of 500 K and higher.
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19
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Núñez M, Vlachos DG. Multiscale Modeling Combined with Active Learning for Microstructure Optimization of Bifunctional Catalysts. Ind Eng Chem Res 2018. [DOI: 10.1021/acs.iecr.8b04801] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Affiliation(s)
- Marcel Núñez
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States
| | - Dionisios G. Vlachos
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States
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20
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Bose S, Dhawan D, Nandi S, Sarkar RR, Ghosh D. Machine learning prediction of interaction energies in rigid water clusters. Phys Chem Chem Phys 2018; 20:22987-22996. [PMID: 30156235 DOI: 10.1039/c8cp03138j] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Classical force fields form a computationally efficient avenue for calculating the energetics of large systems. However, due to the constraints of the underlying analytical form, it is sometimes not accurate enough. Quantum mechanical (QM) methods, although accurate, are computationally prohibitive for large systems. In order to circumvent the bottle-neck of interaction energy estimation of large systems, data driven approaches based on machine learning (ML) have been employed in recent years. In most of these studies, the method of choice is artificial neural networks (ANN). In this work, we have shown an alternative ML method, support vector regression (SVR), that provides comparable accuracy with better computational efficiency. We have further used many body expansion (MBE) along with SVR to predict interaction energies in water clusters (decamers). In the case of dimer and trimer interaction energies, the root mean square errors (RMSEs) of the SVR based scheme are 0.12 kcal mol-1 and 0.34 kcal mol-1, respectively. We show that the SVR and MBE based scheme has a RMSE of 2.78% in the estimation of decamer interaction energy against the parent QM method in a computationally efficient way.
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Affiliation(s)
- Samik Bose
- School of Mathematical and Computational Sciences, Indian Association for the Cultivation of Science, Kolkata-700032, West Bengal, India.
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21
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Medford AJ, Kunz MR, Ewing SM, Borders T, Fushimi R. Extracting Knowledge from Data through Catalysis Informatics. ACS Catal 2018. [DOI: 10.1021/acscatal.8b01708] [Citation(s) in RCA: 128] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Andrew J. Medford
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30318 United States
| | - M. Ross Kunz
- Biological and Chemical Processing Department, Energy and Environmental Science and Technology, Idaho National Laboratory, P.O. Box 1625, Idaho Falls, Idaho 83415, United States
| | - Sarah M. Ewing
- Biological and Chemical Processing Department, Energy and Environmental Science and Technology, Idaho National Laboratory, P.O. Box 1625, Idaho Falls, Idaho 83415, United States
| | - Tammie Borders
- Biological and Chemical Processing Department, Energy and Environmental Science and Technology, Idaho National Laboratory, P.O. Box 1625, Idaho Falls, Idaho 83415, United States
| | - Rebecca Fushimi
- Biological and Chemical Processing Department, Energy and Environmental Science and Technology, Idaho National Laboratory, P.O. Box 1625, Idaho Falls, Idaho 83415, United States
- Center for Advanced Energy Studies, 995 University Boulevard, Idaho Falls, Idaho 83401, United States
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22
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Li W, Ando Y, Minamitani E, Watanabe S. Study of Li atom diffusion in amorphous Li 3PO 4 with neural network potential. J Chem Phys 2018; 147:214106. [PMID: 29221381 DOI: 10.1063/1.4997242] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
To clarify atomic diffusion in amorphous materials, which is important in novel information and energy devices, theoretical methods having both reliability and computational speed are eagerly anticipated. In the present study, we applied neural network (NN) potentials, a recently developed machine learning technique, to the study of atom diffusion in amorphous materials, using Li3PO4 as a benchmark material. The NN potential was used together with the nudged elastic band, kinetic Monte Carlo, and molecular dynamics methods to characterize Li vacancy diffusion behavior in the amorphous Li3PO4 model. By comparing these results with corresponding DFT calculations, we found that the average error of the NN potential is 0.048 eV in calculating energy barriers of diffusion paths, and 0.041 eV in diffusion activation energy. Moreover, the diffusion coefficients obtained from molecular dynamics are always consistent with those from ab initio molecular dynamics simulation, while the computation speed of the NN potential is 3-4 orders of magnitude faster than DFT. Lastly, the structure of amorphous Li3PO4 and the ion transport properties in it were studied with the NN potential using a large supercell model containing more than 1000 atoms. The formation of P2O7 units was observed, which is consistent with the experimental characterization. The Li diffusion activation energy was estimated to be 0.55 eV, which agrees well with the experimental measurements.
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Affiliation(s)
- Wenwen Li
- Department of Materials Engineering, The University of Tokyo, Bunkyo, Tokyo 113-8656, Japan
| | - Yasunobu Ando
- Research Center for Computational Design of Advanced Functional Materials, National Institute of Advanced Industrial Science and Technology, Tsukuba, Ibaraki 305-8568, Japan
| | - Emi Minamitani
- Department of Materials Engineering, The University of Tokyo, Bunkyo, Tokyo 113-8656, Japan
| | - Satoshi Watanabe
- Department of Materials Engineering, The University of Tokyo, Bunkyo, Tokyo 113-8656, Japan
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23
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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
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24
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Nour Ghassemi E, Wijzenbroek M, Somers MF, Kroes GJ. Chemically accurate simulation of dissociative chemisorption of D2 on Pt(1 1 1). Chem Phys Lett 2017. [DOI: 10.1016/j.cplett.2016.12.059] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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25
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Hu X, Zhou Y, Jiang B, Guo H, Xie D. Dynamics of carbon monoxide dissociation on Co(112̄0). Phys Chem Chem Phys 2017; 19:12826-12837. [DOI: 10.1039/c7cp01697b] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The dissociative chemisorption dynamics of CO on rigid Co(112̄0) is investigated using a quasi-classical trajectory method on a new global six-dimensional potential energy surface.
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Affiliation(s)
- Xixi Hu
- Institute of Theoretical and Computational Chemistry
- Key Laboratory of Mesoscopic Chemistry
- School of Chemistry and Chemical Engineering
- Nanjing University
- Nanjing 210093
| | - Yipeng Zhou
- Institute of Theoretical and Computational Chemistry
- Key Laboratory of Mesoscopic Chemistry
- School of Chemistry and Chemical Engineering
- Nanjing University
- Nanjing 210093
| | - Bin Jiang
- Department of Chemical Physics
- University of Science and Technology of China
- Hefei 230026
- China
| | - Hua Guo
- Department of Chemistry and Chemical Biology
- University of New Mexico
- Albuquerque
- USA
| | - Daiqian Xie
- Institute of Theoretical and Computational Chemistry
- Key Laboratory of Mesoscopic Chemistry
- School of Chemistry and Chemical Engineering
- Nanjing University
- Nanjing 210093
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26
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Zhou X, Nattino F, Zhang Y, Chen J, Kroes GJ, Guo H, Jiang B. Dissociative chemisorption of methane on Ni(111) using a chemically accurate fifteen dimensional potential energy surface. Phys Chem Chem Phys 2017; 19:30540-30550. [DOI: 10.1039/c7cp05993k] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
A new chemically accurate potential energy surface for the dissociative chemisorption of methane on the rigid Ni(111) surface.
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Affiliation(s)
- Xueyao Zhou
- Department of Chemical Physics
- University of Science and Technology of China
- Hefei
- China
| | - Francesco Nattino
- Leiden Institute of Chemistry
- Leiden University
- Gorlaeus Laboratories
- P.O. Box 9502
- 2300 RA Leiden
| | - Yaolong Zhang
- Department of Chemical Physics
- University of Science and Technology of China
- Hefei
- China
| | - Jun Chen
- Collaborative Innovation Center of Chemistry for Energy Materials (iChEM)
- College of Chemistry and Chemical Engineering
- Xiamen University
- Xiamen
- Fujian 361005
| | - Geert-Jan Kroes
- Leiden Institute of Chemistry
- Leiden University
- Gorlaeus Laboratories
- P.O. Box 9502
- 2300 RA Leiden
| | - Hua Guo
- Department of Chemistry and Chemical Biology
- University of New Mexico
- Albuquerque
- USA
| | - Bin Jiang
- Department of Chemical Physics
- University of Science and Technology of China
- Hefei
- China
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27
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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]
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28
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Kroes GJ, Díaz C. Quantum and classical dynamics of reactive scattering of H2 from metal surfaces. Chem Soc Rev 2016; 45:3658-700. [DOI: 10.1039/c5cs00336a] [Citation(s) in RCA: 120] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
State-of-the-art theoretical models allow nowadays an accurate description of H2/metal surface systems and phenomena relative to heterogeneous catalysis. Here we review the most relevant ones investigated during the last 10 years.
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Affiliation(s)
- Geert-Jan Kroes
- Leiden Institute of Chemistry
- Gorlaeus Laboratories
- Leiden University
- 2300 RA Leiden
- The Netherlands
| | - Cristina Díaz
- Departamento de Química
- Módulo 13
- Universidad Autónoma de Madrid
- 28049 Madrid
- Spain
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29
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Jiang B, Yang M, Xie D, Guo H. Quantum dynamics of polyatomic dissociative chemisorption on transition metal surfaces: mode specificity and bond selectivity. Chem Soc Rev 2016; 45:3621-40. [DOI: 10.1039/c5cs00360a] [Citation(s) in RCA: 122] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Recent advances in quantum dynamical characterization of polyatomic dissociative chemisorption on accurate global potential energy surfaces are critically reviewed.
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Affiliation(s)
- Bin Jiang
- Department of Chemistry and Chemical Biology
- University of New Mexico
- Albuquerque
- USA
- Department of Chemical Physics
| | - Minghui Yang
- Key Laboratory of Magnetic Resonance in Biological Systems
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics
- Wuhan Centre for Magnetic Resonance
- Wuhan Institute of Physics and Mathematics
- Chinese Academy of Sciences
| | - Daiqian Xie
- Institute of Theoretical and Computational Chemistry
- Key Laboratory of Mesoscopic Chemistry
- School of Chemistry and Chemical Engineering
- Nanjing University
- Nanjing 210093
| | - Hua Guo
- Department of Chemistry and Chemical Biology
- University of New Mexico
- Albuquerque
- USA
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30
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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
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31
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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.
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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
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32
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Stamatakis M. Kinetic modelling of heterogeneous catalytic systems. JOURNAL OF PHYSICS. CONDENSED MATTER : AN INSTITUTE OF PHYSICS JOURNAL 2015; 27:013001. [PMID: 25393371 DOI: 10.1088/0953-8984/27/1/013001] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
The importance of heterogeneous catalysis in modern life is evidenced by the fact that numerous products and technologies routinely used nowadays involve catalysts in their synthesis or function. The discovery of catalytic materials is, however, a non-trivial procedure, requiring tedious trial-and-error experimentation. First-principles-based kinetic modelling methods have recently emerged as a promising way to understand catalytic function and aid in materials discovery. In particular, kinetic Monte Carlo (KMC) simulation is increasingly becoming more popular, as it can integrate several sources of complexity encountered in catalytic systems, and has already been used to successfully unravel the underlying physics of several systems of interest. After a short discussion of the different scales involved in catalysis, we summarize the theory behind KMC simulation, and present the latest KMC computational implementations in the field. Early achievements that transformed the way we think about catalysts are subsequently reviewed in connection to latest studies of realistic systems, in an attempt to highlight how the field has evolved over the last few decades. Present challenges and future directions and opportunities in computational catalysis are finally discussed.
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33
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Jiang B, Hu X, Lin S, Xie D, Guo H. Six-dimensional quantum dynamics of dissociative chemisorption of H2 on Co(0001) on an accurate global potential energy surface. Phys Chem Chem Phys 2015; 17:23346-55. [DOI: 10.1039/c5cp03324a] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Six-dimensional quantum dynamics of hydrogen dissociative chemisorption on Co(0001) is investigated on a DFT based potential energy surface.
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Affiliation(s)
- Bin Jiang
- Department of Chemistry and Chemical Biology
- University of New Mexico
- Albuquerque
- USA
| | - Xixi Hu
- Department of Chemistry and Chemical Biology
- University of New Mexico
- Albuquerque
- USA
- Institute of Theoretical and Computational Chemistry
| | - Sen Lin
- State Key Laboratory of Photocatalysis on Energy and Environment
- College of Chemistry
- Fuzhou University
- Fuzhou 350002
- China
| | - Daiqian Xie
- Institute of Theoretical and Computational Chemistry
- Key Laboratory of Mesoscopic Chemistry
- School of Chemistry and Chemical Engineering
- Nanjing University
- Nanjing 210093
| | - Hua Guo
- Department of Chemistry and Chemical Biology
- University of New Mexico
- Albuquerque
- USA
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34
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Jiang B, Guo H. Permutation invariant polynomial neural network approach to fitting potential energy surfaces. III. Molecule-surface interactions. J Chem Phys 2014; 141:034109. [DOI: 10.1063/1.4887363] [Citation(s) in RCA: 106] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Bin Jiang
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, USA
| | - Hua Guo
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, USA
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35
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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.
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Affiliation(s)
- J Behler
- Lehrstuhl für Theoretische Chemie, Ruhr-Universität Bochum, D-44780 Bochum, Germany
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36
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Jiang B, Guo H. Six-dimensional quantum dynamics for dissociative chemisorption of H2 and D2 on Ag(111) on a permutation invariant potential energy surface. Phys Chem Chem Phys 2014; 16:24704-15. [DOI: 10.1039/c4cp03761h] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Quantum dynamics on a permutation invariant potential energy surface for H2 dissociation on Ag(111) yield satisfactory agreement with experiment.
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Affiliation(s)
- Bin Jiang
- Department of Chemistry and Chemical Biology
- University of New Mexico
- Albuquerque, USA
| | - Hua Guo
- Department of Chemistry and Chemical Biology
- University of New Mexico
- Albuquerque, USA
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37
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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
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38
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39
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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
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40
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Morawietz T, Behler J. A density-functional theory-based neural network potential for water clusters including van der Waals corrections. J Phys Chem A 2013; 117:7356-66. [PMID: 23557541 DOI: 10.1021/jp401225b] [Citation(s) in RCA: 103] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The fundamental importance of water for many chemical processes has motivated the development of countless efficient but approximate water potentials for large-scale molecular dynamics simulations, from simple empirical force fields to very sophisticated flexible water models. Accurate and generally applicable water potentials should fulfill a number of requirements. They should have a quality close to quantum chemical methods, they should explicitly depend on all degrees of freedom including all relevant many-body interactions, and they should be able to describe molecular dissociation and recombination. In this work, we present a high-dimensional neural network (NN) potential for water clusters based on density-functional theory (DFT) calculations, which is constructed using clusters containing up to 10 monomers and is in principle able to meet all these requirements. We investigate the reliability of specific parametrizations employing two frequently used generalized gradient approximation (GGA) exchange-correlation functionals, PBE and RPBE, as reference methods. We find that the binding energy errors of the NN potentials with respect to DFT are significantly lower than the typical uncertainties of DFT calculations arising from the choice of the exchange-correlation functional. Further, we examine the role of van der Waals interactions, which are not properly described by GGA functionals. Specifically, we incorporate the D3 scheme suggested by Grimme (J. Chem. Phys. 2010, 132, 154104) in our potentials and demonstrate that it can be applied to GGA-based NN potentials in the same way as to DFT calculations without modification. Our results show that the description of small water clusters provided by the RPBE functional is significantly improved if van der Waals interactions are included, while in case of the PBE functional, which is well-known to yield stronger binding than RPBE, van der Waals corrections lead to overestimated binding energies.
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Affiliation(s)
- Tobias Morawietz
- Lehrstuhl für Theoretische Chemie, Ruhr-Universität Bochum, 44780 Bochum, Germany
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41
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Stamatakis M, Vlachos DG. Unraveling the Complexity of Catalytic Reactions via Kinetic Monte Carlo Simulation: Current Status and Frontiers. ACS Catal 2012. [DOI: 10.1021/cs3005709] [Citation(s) in RCA: 159] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Michail Stamatakis
- Department of Chemical Engineering, University College London, Torrington Place, London
WC1E 7JE, U.K
| | - Dionisios G. Vlachos
- Department
of Chemical and Biomolecular
Engineering, Center for Catalytic Science and Technology, University of Delaware, 150 Academy Street, Newark,
Delaware 19716, United States
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42
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Jose KVJ, Artrith N, Behler J. Construction of high-dimensional neural network potentials using environment-dependent atom pairs. J Chem Phys 2012; 136:194111. [PMID: 22612084 DOI: 10.1063/1.4712397] [Citation(s) in RCA: 63] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
An accurate determination of the potential energy is the crucial step in computer simulations of chemical processes, but using electronic structure methods on-the-fly in molecular dynamics (MD) is computationally too demanding for many systems. Constructing more efficient interatomic potentials becomes intricate with increasing dimensionality of the potential-energy surface (PES), and for numerous systems the accuracy that can be achieved is still not satisfying and far from the reliability of first-principles calculations. Feed-forward neural networks (NNs) have a very flexible functional form, and in recent years they have been shown to be an accurate tool to construct efficient PESs. High-dimensional NN potentials based on environment-dependent atomic energy contributions have been presented for a number of materials. Still, these potentials may be improved by a more detailed structural description, e.g., in form of atom pairs, which directly reflect the atomic interactions and take the chemical environment into account. We present an implementation of an NN method based on atom pairs, and its accuracy and performance are compared to the atom-based NN approach using two very different systems, the methanol molecule and metallic copper. We find that both types of NN potentials provide an excellent description of both PESs, with the pair-based method yielding a slightly higher accuracy making it a competitive alternative for addressing complex systems in MD simulations.
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Affiliation(s)
- K V Jovan Jose
- Lehrstuhl für Theoretische Chemie, Ruhr-Universität Bochum, D-44780 Bochum, Germany
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43
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Morawietz T, Sharma V, Behler J. A neural network potential-energy surface for the water dimer based on environment-dependent atomic energies and charges. J Chem Phys 2012; 136:064103. [PMID: 22360165 DOI: 10.1063/1.3682557] [Citation(s) in RCA: 82] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Understanding the unique properties of water still represents a significant challenge for theory and experiment. Computer simulations by molecular dynamics require a reliable description of the atomic interactions, and in recent decades countless water potentials have been reported in the literature. Still, most of these potentials contain significant approximations, for instance a frozen internal structure of the individual water monomers. Artificial neural networks (NNs) offer a promising way for the construction of very accurate potential-energy surfaces taking all degrees of freedom explicitly into account. These potentials are based on electronic structure calculations for representative configurations, which are then interpolated to a continuous energy surface that can be evaluated many orders of magnitude faster. We present a full-dimensional NN potential for the water dimer as a first step towards the construction of a NN potential for liquid water. This many-body potential is based on environment-dependent atomic energy contributions, and long-range electrostatic interactions are incorporated employing environment-dependent atomic charges. We show that the potential and derived properties like vibrational frequencies are in excellent agreement with the underlying reference density-functional theory calculations.
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Affiliation(s)
- Tobias Morawietz
- Lehrstuhl für Theoretische Chemie, Ruhr-Universität Bochum, 44780 Bochum, Germany
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44
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A review of multiscale modeling of metal-catalyzed reactions: Mechanism development for complexity and emergent behavior. Chem Eng Sci 2011. [DOI: 10.1016/j.ces.2011.05.050] [Citation(s) in RCA: 272] [Impact Index Per Article: 20.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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45
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Behler J. Atom-centered symmetry functions for constructing high-dimensional neural network potentials. J Chem Phys 2011; 134:074106. [DOI: 10.1063/1.3553717] [Citation(s) in RCA: 726] [Impact Index Per Article: 55.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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46
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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]
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47
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Frankcombe TJ, Collins MA. Potential energy surfaces for gas-surface reactions. Phys Chem Chem Phys 2011; 13:8379-91. [DOI: 10.1039/c0cp01843k] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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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
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Le HM, Huynh S, Raff LM. Molecular dissociation of hydrogen peroxide (HOOH) on a neural network ab initio potential surface with a new configuration sampling method involving gradient fitting. J Chem Phys 2009; 131:014107. [DOI: 10.1063/1.3159748] [Citation(s) in RCA: 44] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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Pukrittayakamee A, Malshe M, Hagan M, Raff LM, Narulkar R, Bukkapatnum S, Komanduri R. Simultaneous fitting of a potential-energy surface and its corresponding force fields using feedforward neural networks. J Chem Phys 2009; 130:134101. [DOI: 10.1063/1.3095491] [Citation(s) in RCA: 88] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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