201
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Sun G, Sautet P. Toward Fast and Reliable Potential Energy Surfaces for Metallic Pt Clusters by Hierarchical Delta Neural Networks. J Chem Theory Comput 2019; 15:5614-5627. [DOI: 10.1021/acs.jctc.9b00465] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Geng Sun
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Philippe Sautet
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, California 90095, United States
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California 90095, United States
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202
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Nandi A, Qu C, Bowman JM. Full and fragmented permutationally invariant polynomial potential energy surfaces for trans and cis N-methyl acetamide and isomerization saddle points. J Chem Phys 2019; 151:084306. [DOI: 10.1063/1.5119348] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Apurba Nandi
- Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, USA
| | - Chen Qu
- Department of Chemistry and Biochemistry, University of Maryland, College Park, Maryland 20742, USA
| | - Joel M. Bowman
- Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, USA
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203
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Herr JE, Koh K, Yao K, Parkhill J. Compressing physics with an autoencoder: Creating an atomic species representation to improve machine learning models in the chemical sciences. J Chem Phys 2019; 151:084103. [DOI: 10.1063/1.5108803] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Affiliation(s)
- John E. Herr
- Department of Chemistry and Biochemistry, The University of Notre Dame du Lac, 251 Nieuwland Science Hall, Notre Dame, Indiana 46556, USA
| | - Kevin Koh
- Department of Chemistry and Biochemistry, The University of Notre Dame du Lac, 251 Nieuwland Science Hall, Notre Dame, Indiana 46556, USA
| | - Kun Yao
- Department of Chemistry and Biochemistry, The University of Notre Dame du Lac, 251 Nieuwland Science Hall, Notre Dame, Indiana 46556, USA
| | - John Parkhill
- Department of Chemistry and Biochemistry, The University of Notre Dame du Lac, 251 Nieuwland Science Hall, Notre Dame, Indiana 46556, USA
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204
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DeFever RS, Targonski C, Hall SW, Smith MC, Sarupria S. A generalized deep learning approach for local structure identification in molecular simulations. Chem Sci 2019; 10:7503-7515. [PMID: 31768235 PMCID: PMC6839808 DOI: 10.1039/c9sc02097g] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2019] [Accepted: 07/04/2019] [Indexed: 12/20/2022] Open
Abstract
Identifying local structure in molecular simulations is of utmost importance. The most common existing approach to identify local structure is to calculate some geometrical quantity referred to as an order parameter. In simple cases order parameters are physically intuitive and trivial to develop (e.g., ion-pair distance), however in most cases, order parameter development becomes a much more difficult endeavor (e.g., crystal structure identification). Using ideas from computer vision, we adapt a specific type of neural network called a PointNet to identify local structural environments in molecular simulations. A primary challenge in applying machine learning techniques to simulation is selecting the appropriate input features. This challenge is system-specific and requires significant human input and intuition. In contrast, our approach is a generic framework that requires no system-specific feature engineering and operates on the raw output of the simulations, i.e., atomic positions. We demonstrate the method on crystal structure identification in Lennard-Jones (four different phases), water (eight different phases), and mesophase (six different phases) systems. The method achieves as high as 99.5% accuracy in crystal structure identification. The method is applicable to heterogeneous nucleation and it can even predict the crystal phases of atoms near external interfaces. We demonstrate the versatility of our approach by using our method to identify surface hydrophobicity based solely upon positions and orientations of surrounding water molecules. Our results suggest the approach will be broadly applicable to many types of local structure in simulations.
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Affiliation(s)
- Ryan S DeFever
- Department of Chemical & Biomolecular Engineering , Clemson University , Clemson , SC 29634 , USA
| | - Colin Targonski
- Department of Electrical & Computer Engineering , Clemson University , Clemson , SC 29634 , USA .
| | - Steven W Hall
- Department of Chemical & Biomolecular Engineering , Clemson University , Clemson , SC 29634 , USA
| | - Melissa C Smith
- Department of Electrical & Computer Engineering , Clemson University , Clemson , SC 29634 , USA .
| | - Sapna Sarupria
- Department of Chemical & Biomolecular Engineering , Clemson University , Clemson , SC 29634 , USA
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205
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Ma S, Shang C, Liu ZP. Heterogeneous catalysis from structure to activity via SSW-NN method. J Chem Phys 2019. [DOI: 10.1063/1.5113673] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Sicong Ma
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, China
| | - Cheng Shang
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, China
| | - Zhi-Pan Liu
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, China
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206
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Xu M, Zhu T, Zhang JZH. Molecular Dynamics Simulation of Zinc Ion in Water with an ab Initio Based Neural Network Potential. J Phys Chem A 2019; 123:6587-6595. [DOI: 10.1021/acs.jpca.9b04087] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Mingyuan Xu
- State Key Lab of Precision Spectroscopy, Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, Shanghai Key Laboratory of Green Chemistry & Chemical Process, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China
| | - Tong Zhu
- State Key Lab of Precision Spectroscopy, Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, Shanghai Key Laboratory of Green Chemistry & Chemical Process, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
| | - John Z. H. Zhang
- State Key Lab of Precision Spectroscopy, Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, Shanghai Key Laboratory of Green Chemistry & Chemical Process, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
- Department of Chemistry, New York University, New York City, New York 10003, United States
- Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, Shanxi 030006, China
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207
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Activation of the electronic excitation state of reactions in the complex region using microwave irradiation. Chem Phys Lett 2019. [DOI: 10.1016/j.cplett.2019.04.029] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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208
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Schmitz G, Godtliebsen IH, Christiansen O. Machine learning for potential energy surfaces: An extensive database and assessment of methods. J Chem Phys 2019; 150:244113. [DOI: 10.1063/1.5100141] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Affiliation(s)
- Gunnar Schmitz
- Department of Chemistry, Aarhus Universitet, DK-8000 Aarhus, Denmark
| | | | - Ove Christiansen
- Department of Chemistry, Aarhus Universitet, DK-8000 Aarhus, Denmark
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209
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Schlexer Lamoureux P, Winther KT, Garrido Torres JA, Streibel V, Zhao M, Bajdich M, Abild‐Pedersen F, Bligaard T. Machine Learning for Computational Heterogeneous Catalysis. ChemCatChem 2019. [DOI: 10.1002/cctc.201900595] [Citation(s) in RCA: 144] [Impact Index Per Article: 28.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Philomena Schlexer Lamoureux
- SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory 2575 Sand Hill Road, Menlo Park California 94025 United States
- Department of Chemical Engineering Stanford University 443 Via Ortega Stanford CA 94305 United States
| | - Kirsten T. Winther
- SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory 2575 Sand Hill Road, Menlo Park California 94025 United States
- Department of Chemical Engineering Stanford University 443 Via Ortega Stanford CA 94305 United States
| | - Jose Antonio Garrido Torres
- SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory 2575 Sand Hill Road, Menlo Park California 94025 United States
- Department of Chemical Engineering Stanford University 443 Via Ortega Stanford CA 94305 United States
| | - Verena Streibel
- SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory 2575 Sand Hill Road, Menlo Park California 94025 United States
- Department of Chemical Engineering Stanford University 443 Via Ortega Stanford CA 94305 United States
| | - Meng Zhao
- SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory 2575 Sand Hill Road, Menlo Park California 94025 United States
- Department of Chemical Engineering Stanford University 443 Via Ortega Stanford CA 94305 United States
| | - Michal Bajdich
- SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory 2575 Sand Hill Road, Menlo Park California 94025 United States
- Department of Chemical Engineering Stanford University 443 Via Ortega Stanford CA 94305 United States
| | - Frank Abild‐Pedersen
- SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory 2575 Sand Hill Road, Menlo Park California 94025 United States
- Department of Chemical Engineering Stanford University 443 Via Ortega Stanford CA 94305 United States
| | - Thomas Bligaard
- SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory 2575 Sand Hill Road, Menlo Park California 94025 United States
- Department of Chemical Engineering Stanford University 443 Via Ortega Stanford CA 94305 United States
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210
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Abstract
Machine learning enables computers to address problems by learning from data. Deep learning is a type of machine learning that uses a hierarchical recombination of features to extract pertinent information and then learn the patterns represented in the data. Over the last eight years, its abilities have increasingly been applied to a wide variety of chemical challenges, from improving computational chemistry to drug and materials design and even synthesis planning. This review aims to explain the concepts of deep learning to chemists from any background and follows this with an overview of the diverse applications demonstrated in the literature. We hope that this will empower the broader chemical community to engage with this burgeoning field and foster the growing movement of deep learning accelerated chemistry.
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Affiliation(s)
- Adam C Mater
- ARC Centre of Excellence for Electromaterials Science, Research School of Chemistry , Australian National University , Canberra , Australian Capital Territory 2601 , Australia
| | - Michelle L Coote
- ARC Centre of Excellence for Electromaterials Science, Research School of Chemistry , Australian National University , Canberra , Australian Capital Territory 2601 , Australia
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211
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Guan Y, Guo H, Yarkony DR. Neural network based quasi-diabatic Hamiltonians with symmetry adaptation and a correct description of conical intersections. J Chem Phys 2019; 150:214101. [DOI: 10.1063/1.5099106] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Yafu Guan
- Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Hua Guo
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, USA
| | - David R. Yarkony
- Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, USA
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212
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Atahan-Evrenk S, Atalay FB. Prediction of Intramolecular Reorganization Energy Using Machine Learning. J Phys Chem A 2019; 123:7855-7863. [DOI: 10.1021/acs.jpca.9b02733] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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213
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Eckhoff M, Behler J. From Molecular Fragments to the Bulk: Development of a Neural Network Potential for MOF-5. J Chem Theory Comput 2019; 15:3793-3809. [PMID: 31091097 DOI: 10.1021/acs.jctc.8b01288] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The development of first-principles-quality reactive atomistic potentials for organic-inorganic hybrid materials is still a substantial challenge because of the very different physics of the atomic interactions-from covalent via ionic bonding to dispersion-that have to be described in an accurate and balanced way. In this work we used a prototypical metal-organic framework, MOF-5, as a benchmark case to investigate the applicability of high-dimensional neural network potentials (HDNNPs) to this class of materials. In HDNNPs, which belong to the class of machine learning potentials, the energy is constructed as a sum of environment-dependent atomic energy contributions. We demonstrate that by the use of this approach it is possible to obtain a high-quality potential for the periodic MOF-5 crystal using density functional theory (DFT) reference calculations of small molecular fragments only. The resulting HDNNP, which has a root-mean-square error (RMSE) of 1.6 meV/atom for the energies of molecular fragments not included in the training set, is able to provide the equilibrium lattice constant of the bulk MOF-5 structure with an error of about 0.1% relative to DFT, and also, the negative thermal expansion behavior is accurately predicted. The total energy RMSE of periodic structures that are completely absent in the training set is about 6.5 meV/atom, with errors on the order of 2 meV/atom for energy differences. We show that in contrast to energy differences, achieving a high accuracy for total energies requires careful variation of the stoichiometries of the training structures to avoid energy offsets, as atomic energies are not physical observables. The forces, which have RMSEs of about 94 meV/ a0 for the molecular fragments and 130 meV/ a0 for bulk structures not included in the training set, are insensitive to such offsets. Therefore, forces, which are the relevant properties for molecular dynamics simulations, provide a realistic estimate of the accuracy of atomistic potentials.
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Affiliation(s)
- Marco Eckhoff
- Universität Göttingen , Institut für Physikalische Chemie, Theoretische Chemie , Tammannstraße 6 , D-37077 Göttingen , Germany
| | - Jörg Behler
- Universität Göttingen , Institut für Physikalische Chemie, Theoretische Chemie , Tammannstraße 6 , D-37077 Göttingen , Germany
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214
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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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215
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Pun GPP, Batra R, Ramprasad R, Mishin Y. Physically informed artificial neural networks for atomistic modeling of materials. Nat Commun 2019; 10:2339. [PMID: 31138813 PMCID: PMC6538760 DOI: 10.1038/s41467-019-10343-5] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Accepted: 04/26/2019] [Indexed: 11/30/2022] Open
Abstract
Large-scale atomistic computer simulations of materials heavily rely on interatomic potentials predicting the energy and Newtonian forces on atoms. Traditional interatomic potentials are based on physical intuition but contain few adjustable parameters and are usually not accurate. The emerging machine-learning (ML) potentials achieve highly accurate interpolation within a large DFT database but, being purely mathematical constructions, suffer from poor transferability to unknown structures. We propose a new approach that can drastically improve the transferability of ML potentials by informing them of the physical nature of interatomic bonding. This is achieved by combining a rather general physics-based model (analytical bond-order potential) with a neural-network regression. This approach, called the physically informed neural network (PINN) potential, is demonstrated by developing a general-purpose PINN potential for Al. We suggest that the development of physics-based ML potentials is the most effective way forward in the field of atomistic simulations.
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Affiliation(s)
- G P Purja Pun
- Department of Physics and Astronomy, MSN 3F3, George Mason University, Fairfax, VA, 22030, USA
| | - R Batra
- School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - R Ramprasad
- School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
| | - Y Mishin
- Department of Physics and Astronomy, MSN 3F3, George Mason University, Fairfax, VA, 22030, USA.
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216
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Molina J, Laroche A, Richard JV, Schuller AS, Rolando C. Neural Networks Are Promising Tools for the Prediction of the Viscosity of Unsaturated Polyester Resins. Front Chem 2019; 7:375. [PMID: 31192194 PMCID: PMC6545879 DOI: 10.3389/fchem.2019.00375] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Accepted: 05/07/2019] [Indexed: 11/13/2022] Open
Abstract
Unsaturated polyester resins are widely used for the preparation of composite materials and fulfill the majority of practical requirements for industrial and domestic applications at low cost. These resins consist of a highly viscous polyester oligomer and a reactive diluent, which allows its process ability and its crosslinking. The viscosity of the initial polyester and the reactive diluent mixture is critical for practical applications. So far, these viscosities were determined by trial and error which implies a time-consuming succession of manipulations, to achieve the targeted viscosities. In this work, we developed a strategy for predicting the viscosities of unsaturated polyesters formulation based on neural networks. In a first step 15 unsaturated polyesters have been synthesized through high-temperature polycondensation using usual monomers. Experimental Hansen solubility parameters (HSP) were determined from solubility experiment with HSPiP software and glass transition temperatures (Tg) were measured by Differential Scanning Calorimetry (DSC). Quantitative Structure—Property Relationship (QSPR) coupled to multiple linear regressions have been used to get a prediction of Hansen solubility parameters δd, δp, and δh from structural composition. A second QSPR regression has been done on glass transition temperature (prediction vs. experimental coefficient of determination R2 = 0.93) of these unsaturated polyesters. These unsaturated polyesters were next diluted in several solvents with different natures (ethers, esters, alcohol, aromatics for example) at different concentrations. Viscosities at room temperature of these polyesters in solution were finally measured in order to create a database of 220 entries with 7 descriptors (polyester molecular weight, Tg, dispersity index Ð, polyester-solvent HSP RED, molar volume of the solvent, δh of the solvent, concentration of polyester in solvent). The QSPR method for predicting the viscosity from these 6 descriptors proved to be ineffective (R2 = 0.56) as viscosities exhibit non-linear phenomena. A Neural Network with an optimized number of 12 hidden neurons has been trained with 179 entries to predict the viscosity. A correlation between experimental and predicted viscosities based on 41 testing instances gave a correlation coefficient R2 of 0.88 and a predicted vs. measured slope of 0.98. Thanks to Neural Networks, new developments with eco-friendly reactive diluents can be accelerated.
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Affiliation(s)
- Julien Molina
- Mäder Research, Mulhouse, France.,Faculté des Sciences et Technologies, Université de Lille, USR 3290 MSAP, Miniaturisation pour l'Analyse, la Synthèse et la Protéomique, Villeneuve d'Ascq, France
| | - Aurélie Laroche
- Mäder Research, Mulhouse, France.,Faculté des Sciences et Technologies, Université de Lille, USR 3290 MSAP, Miniaturisation pour l'Analyse, la Synthèse et la Protéomique, Villeneuve d'Ascq, France
| | | | | | - Christian Rolando
- Faculté des Sciences et Technologies, Université de Lille, USR 3290 MSAP, Miniaturisation pour l'Analyse, la Synthèse et la Protéomique, Villeneuve d'Ascq, France
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217
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Kocer E, Mason JK, Erturk H. A novel approach to describe chemical environments in high-dimensional neural network potentials. J Chem Phys 2019; 150:154102. [PMID: 31005106 DOI: 10.1063/1.5086167] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
A central concern of molecular dynamics simulations is the potential energy surfaces that govern atomic interactions. These hypersurfaces define the potential energy of the system and have generally been calculated using either predefined analytical formulas (classical) or quantum mechanical simulations (ab initio). The former can accurately reproduce only a selection of material properties, whereas the latter is restricted to short simulation times and small systems. Machine learning potentials have recently emerged as a third approach to model atomic interactions, and are purported to offer the accuracy of ab initio simulations with the speed of classical potentials. However, the performance of machine learning potentials depends crucially on the description of a local atomic environment. A set of invariant, orthogonal, and differentiable descriptors for an atomic environment is proposed, implemented in a neural network potential for solid-state silicon, and tested in molecular dynamics simulations. Neural networks using the proposed descriptors are found to outperform ones using the Behler-Parinello and smooth overlap of atomic position descriptors in the literature.
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Affiliation(s)
- Emir Kocer
- Department of Mechanical Engineering, Bogazici University, Istanbul, Turkey
| | - Jeremy K Mason
- Department of Materials Science and Engineering, University of California Davis, Davis, California 95616, USA
| | - Hakan Erturk
- Department of Mechanical Engineering, Bogazici University, Istanbul, Turkey
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218
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Ceriotti M. Unsupervised machine learning in atomistic simulations, between predictions and understanding. J Chem Phys 2019; 150:150901. [PMID: 31005087 DOI: 10.1063/1.5091842] [Citation(s) in RCA: 75] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Automated analyses of the outcome of a simulation have been an important part of atomistic modeling since the early days, addressing the need of linking the behavior of individual atoms and the collective properties that are usually the final quantity of interest. Methods such as clustering and dimensionality reduction have been used to provide a simplified, coarse-grained representation of the structure and dynamics of complex systems from proteins to nanoparticles. In recent years, the rise of machine learning has led to an even more widespread use of these algorithms in atomistic modeling and to consider different classification and inference techniques as part of a coherent toolbox of data-driven approaches. This perspective briefly reviews some of the unsupervised machine-learning methods-that are geared toward classification and coarse-graining of molecular simulations-seen in relation to the fundamental mathematical concepts that underlie all machine-learning techniques. It discusses the importance of using concise yet complete representations of atomic structures as the starting point of the analyses and highlights the risk of introducing preconceived biases when using machine learning to rationalize and understand structure-property relations. Supervised machine-learning techniques that explicitly attempt to predict the properties of a material given its structure are less susceptible to such biases. Current developments in the field suggest that using these two classes of approaches side-by-side and in a fully integrated mode, while keeping in mind the relations between the data analysis framework and the fundamental physical principles, will be key to realizing the full potential of machine learning to help understand the behavior of complex molecules and materials.
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Affiliation(s)
- Michele Ceriotti
- Laboratory of Computational Science and Modeling, Institute des Materiaux, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
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219
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Huang J, Yang D, Zhou Y, Xie D. A new full-dimensional ab initio intermolecular potential energy surface and vibrational states for (HF)2 and (DF)2. J Chem Phys 2019; 150:154302. [DOI: 10.1063/1.5090225] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Jing Huang
- Institute of Theoretical and Computational Chemistry, Key Laboratory of Mesoscopic Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
| | - Dongzheng Yang
- Institute of Theoretical and Computational Chemistry, Key Laboratory of Mesoscopic Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
| | - Yanzi Zhou
- Institute of Theoretical and Computational Chemistry, Key Laboratory of Mesoscopic Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
| | - Daiqian Xie
- Institute of Theoretical and Computational Chemistry, Key Laboratory of Mesoscopic Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
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220
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Schmitz G, Artiukhin DG, Christiansen O. Approximate high mode coupling potentials using Gaussian process regression and adaptive density guided sampling. J Chem Phys 2019; 150:131102. [DOI: 10.1063/1.5092228] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Gunnar Schmitz
- Department of Chemistry, Aarhus Universitet, DK-8000 Aarhus, Denmark
| | | | - Ove Christiansen
- Department of Chemistry, Aarhus Universitet, DK-8000 Aarhus, Denmark
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221
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Pilati S, Pieri P. Supervised machine learning of ultracold atoms with speckle disorder. Sci Rep 2019; 9:5613. [PMID: 30948777 PMCID: PMC6449337 DOI: 10.1038/s41598-019-42125-w] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Accepted: 03/21/2019] [Indexed: 11/29/2022] Open
Abstract
We analyze how accurately supervised machine learning techniques can predict the lowest energy levels of one-dimensional noninteracting ultracold atoms subject to the correlated disorder due to an optical speckle field. Deep neural networks with different numbers of hidden layers and neurons per layer are trained on large sets of instances of the speckle field, whose energy levels have been preventively determined via a high-order finite difference technique. The Fourier components of the speckle field are used as the feature vector to represent the speckle-field instances. A comprehensive analysis of the details that determine the possible success of supervised machine learning tasks, namely the depth and the width of the neural network, the size of the training set, and the magnitude of the regularization parameter, is presented. It is found that ground state energies of previously unseen instances can be predicted with an essentially negligible error given a computationally feasible number of training instances. First and second excited state energies can be predicted too, albeit with slightly lower accuracy and using more layers of hidden neurons. We also find that a three-layer neural network is remarkably resilient to Gaussian noise added to the training-set data (up to 10% noise level), suggesting that cold-atom quantum simulators could be used to train artificial neural networks.
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Affiliation(s)
- S Pilati
- School of Science and Technology, Physics Division, Università di Camerino, 62032, Camerino, (MC), Italy.
| | - P Pieri
- School of Science and Technology, Physics Division, Università di Camerino, 62032, Camerino, (MC), Italy
- INFN, Sezione di Perugia, 06123, Perugia, (PG), Italy
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222
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Sauceda HE, Chmiela S, Poltavsky I, Müller KR, Tkatchenko A. Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces. J Chem Phys 2019; 150:114102. [DOI: 10.1063/1.5078687] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Huziel E. Sauceda
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, 14195 Berlin, Germany
| | - Stefan Chmiela
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany
| | - Igor Poltavsky
- Physics and Materials Science Research Unit, University of Luxembourg, L-1511 Luxembourg, Luxembourg
| | - Klaus-Robert Müller
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany
- Department of Brain and Cognitive Engineering, Korea University, Anam-dong, Seongbuk-gu, Seoul 02841, South Korea
- Max Planck Institute for Informatics, Stuhlsatzenhausweg, 66123 Saarbrücken, Germany
| | - Alexandre Tkatchenko
- Physics and Materials Science Research Unit, University of Luxembourg, L-1511 Luxembourg, Luxembourg
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223
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Nandi A, Qu C, Bowman JM. Using Gradients in Permutationally Invariant Polynomial Potential Fitting: A Demonstration for CH4 Using as Few as 100 Configurations. J Chem Theory Comput 2019; 15:2826-2835. [DOI: 10.1021/acs.jctc.9b00043] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Apurba Nandi
- Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, United States
| | - Chen Qu
- Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, United States
| | - Joel M. Bowman
- Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, United States
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224
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Fulford M, Salvalaglio M, Molteni C. DeepIce: A Deep Neural Network Approach To Identify Ice and Water Molecules. J Chem Inf Model 2019; 59:2141-2149. [DOI: 10.1021/acs.jcim.9b00005] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Maxwell Fulford
- Department of Physics, King’s College London, Strand, London WC2R 2LS, United Kingdom
| | - Matteo Salvalaglio
- Department of Chemical Engineering, University College London, Torrington Place, London WC1E 7JE, United Kingdom
| | - Carla Molteni
- Department of Physics, King’s College London, Strand, London WC2R 2LS, United Kingdom
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225
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Singraber A, Behler J, Dellago C. Library-Based LAMMPS Implementation of High-Dimensional Neural Network Potentials. J Chem Theory Comput 2019; 15:1827-1840. [DOI: 10.1021/acs.jctc.8b00770] [Citation(s) in RCA: 114] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Andreas Singraber
- Faculty of Physics, University of Vienna, Boltzmanngasse 5, 1090 Vienna, Austria
| | - Jörg Behler
- Universität Göttingen, Institut für Physikalische Chemie, Theoretische Chemie, Tammannstraße 6, 37077 Göttingen, Germany
| | - Christoph Dellago
- Faculty of Physics, University of Vienna, Boltzmanngasse 5, 1090 Vienna, Austria
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226
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Walton JR, Rivera-Rivera LA, Lucchese RR, Bevan JW. Canonical Approach To Generate Multidimensional Potential Energy Surfaces. J Phys Chem A 2019; 123:537-543. [DOI: 10.1021/acs.jpca.8b10329] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Jay R. Walton
- Department of Mathematics, Texas A&M University, College Station, Texas 77843-3368, United States
| | - Luis A. Rivera-Rivera
- Department of Physical Sciences, Ferris State University, Big Rapids, Michigan 49307, United States
| | - Robert R. Lucchese
- Department of Chemistry, Texas A&M University, College Station, Texas 77843-3255, United States
| | - John W. Bevan
- Department of Chemistry, Texas A&M University, College Station, Texas 77843-3255, United States
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227
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Li J, Song K, Behler J. A critical comparison of neural network potentials for molecular reaction dynamics with exact permutation symmetry. Phys Chem Chem Phys 2019; 21:9672-9682. [DOI: 10.1039/c8cp06919k] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Several symmetry strategies have been compared in fitting full dimensional accurate potentials for reactive systems based on a neural network approach.
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Affiliation(s)
- Jun Li
- School of Chemistry and Chemical Engineering, Chongqing University
- Chongqing 401331
- China
- Universität Göttingen, Institut für Physikalische Chemie, Theoretische Chemie
- 37077 Göttingen
| | - Kaisheng Song
- School of Chemistry and Chemical Engineering, Chongqing University
- Chongqing 401331
- China
| | - Jörg Behler
- Universität Göttingen, Institut für Physikalische Chemie, Theoretische Chemie
- 37077 Göttingen
- Germany
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228
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Guan Y, Zhang DH, Guo H, Yarkony DR. Representation of coupled adiabatic potential energy surfaces using neural network based quasi-diabatic Hamiltonians: 1,2 2A′ states of LiFH. Phys Chem Chem Phys 2019; 21:14205-14213. [DOI: 10.1039/c8cp06598e] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
A general algorithm for determining diabatic representations from adiabatic energies, energy gradients and derivative couplings using neural networks is introduced.
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Affiliation(s)
- Yafu Guan
- Department of Chemistry
- Johns Hopkins University
- Baltimore
- USA
| | - Dong H. Zhang
- State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical Computational Chemistry
- Dalian Institute of Chemical Physics
- Chinese Academy of Sciences
- Dalian 116023
- People's Republic of China
| | - Hua Guo
- Department of Chemistry and Chemical Biology
- University of New Mexico
- Albuquerque
- USA
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229
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Abstract
This article discusses applications of Bayesian machine learning for quantum molecular dynamics.
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Affiliation(s)
- R. V. Krems
- Department of Chemistry
- University of British Columbia
- Vancouver
- Canada
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230
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Sturluson A, Huynh MT, Kaija AR, Laird C, Yoon S, Hou F, Feng Z, Wilmer CE, Colón YJ, Chung YG, Siderius DW, Simon CM. The role of molecular modelling and simulation in the discovery and deployment of metal-organic frameworks for gas storage and separation. MOLECULAR SIMULATION 2019; 45:10.1080/08927022.2019.1648809. [PMID: 31579352 PMCID: PMC6774364 DOI: 10.1080/08927022.2019.1648809] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Accepted: 07/15/2019] [Indexed: 01/10/2023]
Abstract
Metal-organic frameworks (MOFs) are highly tuneable, extended-network, crystalline, nanoporous materials with applications in gas storage, separations, and sensing. We review how molecular models and simulations of gas adsorption in MOFs have informed the discovery of performant MOFs for methane, hydrogen, and oxygen storage, xenon, carbon dioxide, and chemical warfare agent capture, and xylene enrichment. Particularly, we highlight how large, open databases of MOF crystal structures, post-processed to enable molecular simulations, are a platform for computational materials discovery. We discuss how to orient research efforts to routinise the computational discovery of MOFs for adsorption-based engineering applications.
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Affiliation(s)
- Arni Sturluson
- School of Chemical, Biological, and Environmental Engineering, Oregon State University. Corvallis, OR, USA
| | - Melanie T. Huynh
- School of Chemical, Biological, and Environmental Engineering, Oregon State University. Corvallis, OR, USA
| | - Alec R. Kaija
- Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Caleb Laird
- School of Chemical, Biological, and Environmental Engineering, Oregon State University. Corvallis, OR, USA
| | - Sunghyun Yoon
- School of Chemical and Biomolecular Engineering, Pusan National University, Busan, Korea (South)
| | - Feier Hou
- Western Oregon University. Department of Chemistry, Monmouth, OR, USA
| | - Zhenxing Feng
- School of Chemical, Biological, and Environmental Engineering, Oregon State University. Corvallis, OR, USA
| | - Christopher E. Wilmer
- Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Yamil J. Colón
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN, USA
| | - Yongchul G. Chung
- School of Chemical and Biomolecular Engineering, Pusan National University, Busan, Korea (South)
| | - Daniel W. Siderius
- Chemical Sciences Division, National Institute of Standards and Technology. Gaithersburg, MD, USA
| | - Cory M. Simon
- School of Chemical, Biological, and Environmental Engineering, Oregon State University. Corvallis, OR, USA
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231
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Samanta A. Representing local atomic environment using descriptors based on local correlations. J Chem Phys 2018; 149:244102. [PMID: 30599737 DOI: 10.1063/1.5055772] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Statistical learning of material properties is an emerging topic of research and has been tremendously successful in areas such as representing complex energy landscapes as well as in technologically relevant areas, like identification of better catalysts and electronic materials. However, analysis of large data sets to efficiently learn characteristic features of a complex energy landscape, for example, depends on the ability of descriptors to effectively screen different local atomic environments. Thus, discovering appropriate descriptors of bulk or defect properties and the functional dependence of such properties on these descriptors remains a difficult and tedious process. To this end, we develop a framework to generate descriptors based on many-body correlations that can effectively capture intrinsic geometric features of the local environment of an atom. These descriptors are based on the spectrum of two-body, three-body, four-body, and higher order correlations between an atom and its neighbors and are evaluated by calculating the corresponding two-body, three-body, and four-body overlap integrals. They are invariant to global translation, global rotation, reflection, and permutations of atomic indices. By systematically testing the ability to capture the local atomic environment, it is shown that the local correlation descriptors are able to successfully reconstruct structures containing 10-25 atoms which was previously not possible.
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Affiliation(s)
- Amit Samanta
- Physics Division, Lawrence Livermore National Laboratory, Livermore, California 94550, USA
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232
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Chen WK, Liu XY, Fang WH, Dral PO, Cui G. Deep Learning for Nonadiabatic Excited-State Dynamics. J Phys Chem Lett 2018; 9:6702-6708. [PMID: 30403870 DOI: 10.1021/acs.jpclett.8b03026] [Citation(s) in RCA: 97] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
In this work we show that deep learning (DL) can be used for exploring complex and highly nonlinear multistate potential energy surfaces of polyatomic molecules and related nonadiabatic dynamics. Our DL is based on deep neural networks (DNNs), which are used as accurate representations of the CASSCF ground- and excited-state potential energy surfaces (PESs) of CH2NH. After geometries near conical intersection are included in the training set, the DNN models accurately reproduce excited-state topological structures; photoisomerization paths; and, importantly, conical intersections. We have also demonstrated that the results from nonadiabatic dynamics run with the DNN models are very close to those from the dynamics run with the pure ab initio method. The present work should encourage further studies of using machine learning methods to explore excited-state potential energy surfaces and nonadiabatic dynamics of polyatomic molecules.
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Affiliation(s)
- Wen-Kai Chen
- Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry , Beijing Normal University , Beijing 100875 , China
| | - Xiang-Yang Liu
- Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry , Beijing Normal University , Beijing 100875 , China
| | - Wei-Hai Fang
- Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry , Beijing Normal University , Beijing 100875 , China
| | - Pavlo O Dral
- Max-Planck-Institut für Kohlenforschung , Kaiser-Wilhelm-Platz 1 , 45470 Mülheim an der Ruhr , Germany
| | - Ganglong Cui
- Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry , Beijing Normal University , Beijing 100875 , China
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233
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Tran K, Palizhati A, Back S, Ulissi ZW. Dynamic Workflows for Routine Materials Discovery in Surface Science. J Chem Inf Model 2018; 58:2392-2400. [DOI: 10.1021/acs.jcim.8b00386] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Kevin Tran
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15217, United States
| | - Aini Palizhati
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15217, United States
| | - Seoin Back
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15217, United States
| | - Zachary W. Ulissi
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15217, United States
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234
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Eisenbrandt P, Ruckenbauer M, Burghardt I. Gaussian-based multiconfiguration time-dependent Hartree: A two-layer approach. III. Application to nonadiabatic dynamics in a charge transfer complex. J Chem Phys 2018; 149:174102. [DOI: 10.1063/1.5053417] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Affiliation(s)
- P. Eisenbrandt
- Institute of Physical and Theoretical Chemistry, Goethe University Frankfurt, Max-von-Laue-Str. 7, 60438 Frankfurt, Germany
| | - M. Ruckenbauer
- Institute of Physical and Theoretical Chemistry, Goethe University Frankfurt, Max-von-Laue-Str. 7, 60438 Frankfurt, Germany
| | - I. Burghardt
- Institute of Physical and Theoretical Chemistry, Goethe University Frankfurt, Max-von-Laue-Str. 7, 60438 Frankfurt, Germany
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235
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Sun Y, Hamada I. Insight into the Solvation Structure of Tetraglyme-Based Electrolytes via First-Principles Molecular Dynamics Simulation. J Phys Chem B 2018; 122:10014-10022. [PMID: 30299952 DOI: 10.1021/acs.jpcb.8b07098] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Glyme-lithium salt equimolar mixtures, as solvate ionic liquid electrolytes for rechargeable lithium secondary batteries, are of great interest, due to the desirable properties such as high oxidative stability, low vapor pressure, and nonflammability. However, the fundamental understanding of the solvation shell structure in glyme electrolytes has not been clearly established. Herein, we employ first-principles molecular dynamics (FPMD) simulation to study the lithium bis(trifluoromethylsulfonyl)-amide (LiTFSA) and tetraglyme (G4) electrolyte system. For the case of equimolar ratio, a positive correlation between the total coordination number of Li+ ions and the phase stability is clearly established. At the ground state of equimolar LiTFSA-G4 electrolyte, most of the Li+ ions are coordinated to four O atoms of a curled G4 molecule and one O atom of a TFSA- anion, equivalent to the second most stable contact ion pair in gas-phase cluster calculations. By contrast, Li+ ions prefer to be coordinated by two G4 molecules and not in direct contact with TFSA- anions at a low concentration of Li salt. The significantly increased probability of pairing between the Li-G4 complexes and TFSA- anions at the equimolar ratio could be highly relevant to its ionic-liquid-like properties.
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Affiliation(s)
- Yang Sun
- Global Research Center for Environment and Energy based on Nanomaterials Science (GREEN) , National Institute for Materials Science , 1-1 Namiki , Tsukuba 305-0044 , Japan
| | - Ikutaro Hamada
- Global Research Center for Environment and Energy based on Nanomaterials Science (GREEN) , National Institute for Materials Science , 1-1 Namiki , Tsukuba 305-0044 , Japan.,Department of Precision Science and Technology, Graduate School of Engineering , Osaka University , 2-1 Yamada-oka , Suita , Osaka 565-0871 , Japan
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236
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Ziegler B, Rauhut G. Rigorous use of symmetry within the construction of multidimensional potential energy surfaces. J Chem Phys 2018; 149:164110. [DOI: 10.1063/1.5047912] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Affiliation(s)
- Benjamin Ziegler
- Institut für Theoretische Chemie, Universität Stuttgart, Pfaffenwaldring 55, 70569 Stuttgart, Germany
| | - Guntram Rauhut
- Institut für Theoretische Chemie, Universität Stuttgart, Pfaffenwaldring 55, 70569 Stuttgart, Germany
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237
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Ma D, Tian X, Guo L, Mou J, Lin S, Ma J. Activation of Reactions in the Complex Region Using Microwave Irradiation. J Phys Chem A 2018; 122:7540-7547. [PMID: 30160492 DOI: 10.1021/acs.jpca.8b06442] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Many mode-specific behaviors in the gas phase and at the gas-surface interface have been reported in the past decades. Infrared activation of a reagent vibrational mode is often used to study these reactions. In this work, an inexpensive and easily applied scheme using microwave irradiation is proposed for activating complex-forming reactions by transferring populations between closely spaced resonances. The important combustion reaction of H + O2 ↔ O + OH is used as a model system to demonstrate the feasibility of the proposed approach. The existence of a nonzero transition dipole moment matrix element between two highly excited resonance states separated by a small energy gap in the model system may allow one to use microwave irradiation to intervene and control the model reaction. The high energy resonance states of the model reaction can also release their energy by photon emission, which is in agreement with the experimentally observed chemiluminescence process.
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Affiliation(s)
- Dandan Ma
- Institute of Atomic and Molecular Physics , Sichuan University , Chengdu , Sichuan 610065 , China
| | - Xuefen Tian
- Institute of Atomic and Molecular Physics , Sichuan University , Chengdu , Sichuan 610065 , China
| | - Lifen Guo
- Institute of Atomic and Molecular Physics , Sichuan University , Chengdu , Sichuan 610065 , China
| | - Jie Mou
- Institute of Atomic and Molecular Physics , Sichuan University , Chengdu , Sichuan 610065 , China
| | - Sen Lin
- State Key Laboratory of Photocatalysis on Energy and Environment, College of Chemistry , Fuzhou University , Fuzhou 350002 , People's Republic of China
| | - Jianyi Ma
- Institute of Atomic and Molecular Physics , Sichuan University , Chengdu , Sichuan 610065 , China
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238
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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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239
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Bonfanti M, Petersen J, Eisenbrandt P, Burghardt I, Pollak E. Computation of the S 1 ← S 0 Vibronic Absorption Spectrum of Formaldehyde by Variational Gaussian Wavepacket and Semiclassical IVR Methods. J Chem Theory Comput 2018; 14:5310-5323. [PMID: 30141930 DOI: 10.1021/acs.jctc.8b00355] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The vibronic absorption spectrum of the electric dipole forbidden and vibronically allowed S1(1 A2) ← S0(1 A1) transition of formaldehyde is calculated by Gaussian wavepacket and semiclassical methods, along with numerically exact reference calculations, using the potential energy surface of Fu, Shepler, and Bowman ( J. Am. Chem. Soc. 2011, 133, 7957). Specifically, the variational multiconfigurational Gaussian (vMCG) approach and the Herman-Kluk semiclassical initial value representation (HK-SCIVR) are compared to assess the accuracy and convergence of these methods, benchmarked against numerically exact time-dependent wavepacket propagation (TDWP) on the reference potential energy surface. The vMCG calculation is shown to converge quite well with about 100 variationally evolving Gaussian functions and using a local cubic expansion instead of the conventional local harmonic approximation. By contrast, the HK-SCIVR approach with ∼105 trajectories reproduces the vibrationally structured spectral envelope correctly but yields a strongly broadened spectrum. The comparison of the computed absorption spectrum with experiment shows that the relevant vibronic progressions are reasonably reproduced by all computations, but deviations of the order of 10-100 cm-1 occur, underscoring that both electronic structure calculations and dynamical approaches remain challenging in the calculation of typical small-molecule excited-state spectra by trajectory-based methods.
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Affiliation(s)
- Matteo Bonfanti
- Institute of Physical and Theoretical Chemistry , Goethe University Frankfurt , Max-von-Laue-Str. 7 , D-60438 Frankfurt/Main , Germany
| | - Jakob Petersen
- Chemical and Biological Physics Department , Weizmann Institute of Science , 76000 Rehovot , Israel
| | - Pierre Eisenbrandt
- Institute of Physical and Theoretical Chemistry , Goethe University Frankfurt , Max-von-Laue-Str. 7 , D-60438 Frankfurt/Main , Germany
| | - Irene Burghardt
- Institute of Physical and Theoretical Chemistry , Goethe University Frankfurt , Max-von-Laue-Str. 7 , D-60438 Frankfurt/Main , Germany
| | - Eli Pollak
- Chemical and Biological Physics Department , Weizmann Institute of Science , 76000 Rehovot , Israel
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240
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Huang J, Zhou Y, Xie D. Predicted infrared spectra in the HF stretching band of the H 2-HF complex. J Chem Phys 2018; 149:094307. [PMID: 30195303 DOI: 10.1063/1.5046359] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
The infrared spectra with hydrogen fluoride (HF) and deuterium fluoride (DF) (v2 = 1 ← 0) for eight isotropic species of H2-HF complex are predicted, based on our newly constructed high-accuracy ab initio potential energy surface [D. Yang et al., J. Chem. Phys. 148, 184301 (2018)]. The radial discrete variable representation/angular finite basis representation method and Lanczos algorithm were used to determine the ro-vibrational energy levels and wave functions for eight species of H2-HF complex (para-H2-HF, ortho-H2-HF, para-D2-HF, ortho-D2-HF, para-H2-DF, ortho-H2-DF, para-D2-DF, and ortho-D2-DF) with separating the inter- and intra-molecular vibrations. Bound states properties including their dissociation energies and rotational constants were presented. The calculated band origins are all red shifted to the isolated HF molecule and in good agreement with available experimental values. The frequencies and line intensities of ro-vibrational transitions in the HF stretching band were further calculated, and the predicted infrared spectra are consistent with available observed spectra. Among them, the spectra for three isotopic species of H2-HF (para-H2-DF, para-D2-DF, and ortho-D2-DF) were predicted for the first time.
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Affiliation(s)
- Jing Huang
- Institute of Theoretical and Computational Chemistry, Key Laboratory of Mesoscopic Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
| | - Yanzi Zhou
- Institute of Theoretical and Computational Chemistry, Key Laboratory of Mesoscopic Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
| | - Daiqian Xie
- Institute of Theoretical and Computational Chemistry, Key Laboratory of Mesoscopic Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
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241
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Meuwly M. Reactive molecular dynamics: From small molecules to proteins. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2018. [DOI: 10.1002/wcms.1386] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Affiliation(s)
- Markus Meuwly
- Department of Chemistry University of Basel Basel Switzerland
- Department of Chemistry Brown University Providence Rhode Island
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242
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Chiriki S, Jindal S, Singh P, Bulusu SS. Correlation of structure with UV-visible spectra by varying SH composition in Au-SH nanoclusters. J Chem Phys 2018; 149:074307. [DOI: 10.1063/1.5031478] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Affiliation(s)
- Siva Chiriki
- Discipline of Chemistry, Indian Institute of Technology (IIT) Indore, Indore, Madhya Pradesh 453552, India
| | - Shweta Jindal
- Discipline of Chemistry, Indian Institute of Technology (IIT) Indore, Indore, Madhya Pradesh 453552, India
| | - Priya Singh
- Discipline of Chemistry, Indian Institute of Technology (IIT) Indore, Indore, Madhya Pradesh 453552, India
| | - Satya S. Bulusu
- Discipline of Chemistry, Indian Institute of Technology (IIT) Indore, Indore, Madhya Pradesh 453552, India
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243
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Zhou B, He D, Chen M. A new accurate potential energy surface for HeTiO system and rotational quenching of TiO due to He collisions. Chem Phys Lett 2018. [DOI: 10.1016/j.cplett.2018.06.030] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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244
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Sun P, Chen J, Liu S, Zhang DH. Accurate integral cross sections for the H + CO2 → OH + CO reaction. Chem Phys Lett 2018. [DOI: 10.1016/j.cplett.2018.07.025] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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245
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Schütt O, VandeVondele J. Machine Learning Adaptive Basis Sets for Efficient Large Scale Density Functional Theory Simulation. J Chem Theory Comput 2018; 14:4168-4175. [PMID: 29957943 PMCID: PMC6096449 DOI: 10.1021/acs.jctc.8b00378] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
It is chemically intuitive that an optimal atom centered basis set must adapt to its atomic environment, for example by polarizing toward nearby atoms. Adaptive basis sets of small size can be significantly more accurate than traditional atom centered basis sets of the same size. The small size and well conditioned nature of these basis sets leads to large saving in computational cost, in particular in a linear scaling framework. Here, it is shown that machine learning can be used to predict such adaptive basis sets using local geometrical information only. As a result, various properties of standard DFT calculations can be easily obtained at much lower costs, including nuclear gradients. In our approach, a rotationally invariant parametrization of the basis is obtained by employing a potential anchored on neighboring atoms to ultimately construct a rotation matrix that turns a traditional atom centered basis set into a suitable adaptive basis set. The method is demonstrated using MD simulations of liquid water, where it is shown that minimal basis sets yield structural properties in fair agreement with basis set converged results, while reducing the computational cost in the best case by a factor of 200 and the required flops by 4 orders of magnitude. Already a very small training set yields satisfactory results as the variational nature of the method provides robustness.
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Affiliation(s)
- Ole Schütt
- Department of Materials , ETH Zürich , 8093 Zürich , Switzerland
| | - Joost VandeVondele
- Department of Materials , ETH Zürich , 8093 Zürich , Switzerland.,Swiss National Supercomputing Centre (CSCS) , 6900 Lugano , Switzerland
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246
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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
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247
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Herr JE, Yao K, McIntyre R, Toth DW, Parkhill J. Metadynamics for training neural network model chemistries: A competitive assessment. J Chem Phys 2018; 148:241710. [PMID: 29960377 DOI: 10.1063/1.5020067] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Neural network model chemistries (NNMCs) promise to facilitate the accurate exploration of chemical space and simulation of large reactive systems. One important path to improving these models is to add layers of physical detail, especially long-range forces. At short range, however, these models are data driven and data limited. Little is systematically known about how data should be sampled, and "test data" chosen randomly from some sampling techniques can provide poor information about generality. If the sampling method is narrow, "test error" can appear encouragingly tiny while the model fails catastrophically elsewhere. In this manuscript, we competitively evaluate two common sampling methods: molecular dynamics (MD), normal-mode sampling, and one uncommon alternative, Metadynamics (MetaMD), for preparing training geometries. We show that MD is an inefficient sampling method in the sense that additional samples do not improve generality. We also show that MetaMD is easily implemented in any NNMC software package with cost that scales linearly with the number of atoms in a sample molecule. MetaMD is a black-box way to ensure samples always reach out to new regions of chemical space, while remaining relevant to chemistry near kbT. It is a cheap tool to address the issue of generalization.
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Affiliation(s)
- John E Herr
- Department of Chemistry and Biochemistry, The University of Notre Dame du Lac, 251 Nieuwland Science Hall, Notre Dame, Indiana 46556, USA
| | - Kun Yao
- Department of Chemistry and Biochemistry, The University of Notre Dame du Lac, 251 Nieuwland Science Hall, Notre Dame, Indiana 46556, USA
| | - Ryker McIntyre
- Department of Chemistry and Biochemistry, The University of Notre Dame du Lac, 251 Nieuwland Science Hall, Notre Dame, Indiana 46556, USA
| | - David W Toth
- Department of Chemistry and Biochemistry, The University of Notre Dame du Lac, 251 Nieuwland Science Hall, Notre Dame, Indiana 46556, USA
| | - John Parkhill
- Department of Chemistry and Biochemistry, The University of Notre Dame du Lac, 251 Nieuwland Science Hall, Notre Dame, Indiana 46556, USA
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248
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Unke OT, Meuwly M. A reactive, scalable, and transferable model for molecular energies from a neural network approach based on local information. J Chem Phys 2018; 148:241708. [PMID: 29960298 DOI: 10.1063/1.5017898] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Despite the ever-increasing computer power, accurate ab initio calculations for large systems (thousands to millions of atoms) remain infeasible. Instead, approximate empirical energy functions are used. Most current approaches are either transferable between different chemical systems, but not particularly accurate, or they are fine-tuned to a specific application. In this work, a data-driven method to construct a potential energy surface based on neural networks is presented. Since the total energy is decomposed into local atomic contributions, the evaluation is easily parallelizable and scales linearly with system size. With prediction errors below 0.5 kcal mol-1 for both unknown molecules and configurations, the method is accurate across chemical and configurational space, which is demonstrated by applying it to datasets from nonreactive and reactive molecular dynamics simulations and a diverse database of equilibrium structures. The possibility to use small molecules as reference data to predict larger structures is also explored. Since the descriptor only uses local information, high-level ab initio methods, which are computationally too expensive for large molecules, become feasible for generating the necessary reference data used to train the neural network.
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Affiliation(s)
- Oliver T Unke
- Department of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland
| | - Markus Meuwly
- Department of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland
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249
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Affiliation(s)
- T. Serwatka
- Institut für Chemie und Biochemie, Freie Universität Berlin, Berlin, Germany
| | - B. Paulus
- Institut für Chemie und Biochemie, Freie Universität Berlin, Berlin, Germany
| | - J. C. Tremblay
- Institut für Chemie und Biochemie, Freie Universität Berlin, Berlin, Germany
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250
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Gastegger M, Schwiedrzik L, Bittermann M, Berzsenyi F, Marquetand P. wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials. J Chem Phys 2018; 148:241709. [DOI: 10.1063/1.5019667] [Citation(s) in RCA: 145] [Impact Index Per Article: 24.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Affiliation(s)
- M. Gastegger
- Institute of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 17, 1090 Vienna, Austria
| | - L. Schwiedrzik
- Institute of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 17, 1090 Vienna, Austria
| | - M. Bittermann
- Institute of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 17, 1090 Vienna, Austria
| | - F. Berzsenyi
- Institute of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 17, 1090 Vienna, Austria
| | - P. Marquetand
- Institute of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 17, 1090 Vienna, Austria
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