Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations

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Items 301–400 of 781

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2. Toward spectroscopically accurate global ab initio potential energy surface for the acetylene-vinylidene isomerization
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3. A Gaussian process regression adaptive density guided approach for potential energy surface construction
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5. A Full-Dimensional Global Potential Energy Surface of H3O+(ã3A) for the OH+(X3Σ–) + H2(X1Σg+) → H(2S) + H2O+(X2B1) Reaction
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7. Development of a physically-informed neural network interatomic potential for tantalum
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8. H+CH4→ H2+ CH3 initial state-selected reaction probabilities on different potential energy surfaces
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9. Active Learning A Neural Network Model For Gold Clusters & Bulk From Sparse First Principles Training Data
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10. Exclusive Neural Network Representation of the Quasi-Diabatic Hamiltonians Including Conical Intersections
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11. Is Unified Understanding of Vibrational Coupling of Water Possible? Hyper-Raman Measurement and Machine Learning Spectra
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12. Machine learning for atomic forces in a crystalline solid: Transferability to various temperatures
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13. ænet-PyTorch: A GPU-supported implementation for machine learning atomic potentials training
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15. Unified approach to multipolar polarisation and charge transfer for ions: microhydrated Na+
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16. Employing general fit-bases for construction of potential energy surfaces with an adaptive density-guided approach
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17. Predicting HSE band gaps from PBE charge densities via neural network functionals
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18. Combining Quantum Mechanics and Machine-Learning Calculations for Anharmonic Corrections to Vibrational Frequencies
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19. A neural network potential energy surface for the F + CH 4 reaction including multiple channels based on coupled cluster theory
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20. A new full-dimensional ab initio intermolecular potential energy surface and vibrational states for (HF)2 and (DF)2
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21. Tree based machine learning framework for predicting ground state energies of molecules
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22. Isomerization and decomposition reactions of acetaldehyde relevant to atmospheric processes from dynamics simulations on neural network-based potential energy surfaces.
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23. Enabling complete multichannel nonadiabatic dynamics: A global representation of the two-channel coupled, 1,21A and 13A states of NH3 using neural networks
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24. Integration of data-intensive, machine learning and robotic experimental approaches for accelerated discovery of catalysts in renewable energy-related reactions
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25. Grassmann Extrapolation of Density Matrices for Born–Oppenheimer Molecular Dynamics
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26. Generative Adversarial Networks‐Based Synthetic Microstructures for Data‐Driven Materials Design
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27. Vibrational Spectroscopy and Proton Transfer Dynamics in Protonated Oxalate.
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28. Molecular dynamics study on magnesium hydride nanoclusters with machine-learning interatomic potential
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29. Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments
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30. Permutationally invariant fitting of intermolecular potential energy surfaces: A case study of the Ne-C2H2 system
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31. FEREBUS: Highly parallelized engine for kriging training
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32. Finite-temperature materials modeling from the quantum nuclei to the hot electron regime
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33. Neural Network Based Quasi-diabatic Representation for S0 and S1 States of Formaldehyde
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34. Effect of Framework Composition and NH3 on the Diffusion of Cu+ in Cu-CHA Catalysts Predicted by Machine-Learning Accelerated Molecular Dynamics
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35. Charting the energy landscape of metal/organic interfaces via machine learning
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36. State-to-state quantum versus classical dynamics study of the OH + CO → H + CO2 reaction in full dimensions (J = 0): checking the validity of the quasi-classical trajectory method
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37. Molecular Dynamics with Neural Network Potentials
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38. Data-driven many-body potentials from density functional theory for aqueous phase chemistry
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39. The neural network based Δ-machine learning approach efficiently brings the DFT potential energy surface to the CCSD(T) quality: a case for the OH + CH 3 OH reaction
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40. Many-body interactions and deep neural network potentials for water
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41. Ring polymer molecular dynamics fast computation of rate coefficients on accurate potential energy surfaces in local configuration space: Application to the abstraction of hydrogen from methane
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42. Benchmark Quantum Mechanical Calculations of Vibrationally Resolved Cross Sections and Rate Constants on ab Initio Potential Energy Surfaces for the F + HD Reaction: Comparisons with Experiments
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43. A global coupled cluster potential energy surface for HCl + OH ↔ Cl + H 2 O
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44. Rapid freezing of water under dynamic compression
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45. Controlling Heterogeneous Catalysis of Water Dissociation Using Cu–Ni Bimetallic Alloy Surfaces: A Quantum Dynamics Study
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46. Accelerating Variational Transition State Theory via Artificial Neural Networks
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47. A fundamental invariant-neural network representation of quasi-diabatic Hamiltonians for the two lowest states of H 3
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48. ManyHF: A pragmatic automated method of finding lower-energy Hartree–Fock solutions for potential energy surface development
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49. High-dimensional neural network potentials for accurate vibrational frequencies: the formic acid dimer benchmark
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50. Modeling the high-pressure solid and liquid phases of tin from deep potentials with ab initio accuracy
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51. Ab Initio Potential Energy and Dipole Moment Surfaces for CS 2 : Determination of Molecular Vibrational Energies
    Ekadashi Pradhan, José‐Luis Carreón‐Macedo, Javier Eduardo Cuervo, Markus Schröder, Alex Brown · 2012 · The Journal of Physical Chemistry A
52. Genetic algorithm approach to global optimization of the full-dimensional potential energy surface for hydrogen atom at fcc-metal surfaces
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53. A full-dimensional potential energy surface and quantum dynamics of inelastic collision process for H2–HF
    Dongzheng Yang, Jing Huang, Junxiang Zuo, Xixi Hu, Daiqian Xie · 2018 · The Journal of Chemical Physics
54. Theoretical Investigations of Rate Coefficients for H + O3 and HO2 + O Reactions on a Full-Dimensional Potential Energy Surface
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55. Parameterized Bases for Calculating Vibrational Spectra Directly from ab Initio Data Using Rectangular Collocation
    Matthew Chan, Sergei Manzhos, Tucker Carrington, Koichi Yamashita · 2012 · Journal of Chemical Theory and Computation
56. Minimizing Redundancy and Data Requirements of Machine Learning Potential: A Case Study in Interface Combustion
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57. Neural network interpolation of exchange-correlation functional
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58. Modeling Anharmonic Effects in the Vibrational Spectra of High-Frequency Modes
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59. Accelerated Quantum Mechanics/Molecular Mechanics Simulations via Neural Networks Incorporated with Mechanical Embedding Scheme
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60. Machine Learning for Bridging the Gap between Density Functional Theory and Coupled Cluster Energies
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61. A Gaussian Process Based Δ-Machine Learning Approach to Reactive Potential Energy Surfaces
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62. Proton-Transfer-Driven Water Exchange Mechanism in the Na+ Solvation Shell
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63. Direct Learning Hidden Excited State Interaction Patterns from ab initio Dynamics and Its Implication as Alternative Molecular Mechanism Models
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64. Neural Networks Are Promising Tools for the Prediction of the Viscosity of Unsaturated Polyester Resins
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65. Hochdimensionale neuronale Netze für Potentialhyperflächen großer molekularer und kondensierter Systeme
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66. Artificial neural networks for the prediction of solvation energies based on experimental and computational data
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67. Towards a machine learned thermodynamics: exploration of free energy landscapes in molecular fluids, biological systems and for gas storage and separation in metal–organic frameworks
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68. A neural network potential for the IRMOF series and its application for thermal and mechanical behaviors
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69. Gaussian approximation potential for amorphous Si : H
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70. Gaussian-based multiconfiguration time-dependent Hartree: A two-layer approach. III. Application to nonadiabatic dynamics in a charge transfer complex
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71. Accurate description of ion migration in solid-state ion conductors from machine-learning molecular dynamics
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72. Optimal radial basis for density-based atomic representations
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73. Full-dimensional quantum dynamics study of the H2 + C2H → H + C2H2 reaction on an ab initio potential energy surface
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74. Scalable neural networks for the efficient learning of disordered quantum systems
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75. Constructing and Evaluating Machine-Learned Interatomic Potentials for Li-Based Disordered Rocksalts
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76. A Machine-Learning-Assisted Crystalline Structure Prediction Framework To Accelerate Materials Discovery
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77. Properties-to-microstructure-to-processing Inverse Analysis for Steels via Machine Learning
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78. Degeneration of kernel regression with Matern kernels into low-order polynomial regression in high dimension
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79. Adaptive sparse grid expansions of the vibrational Hamiltonian
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80. Constructing high-accuracy intermolecular potential energy surface with multi-dimension Morse/Long-Range model
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81. Revisiting the Gaussian process regression for fitting high-dimensional potential energy surface and its application to the OH + HO2 → O2 + H2O reaction
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82. Computational and training requirements for interatomic potential based on artificial neural network for estimating low thermal conductivity of silver chalcogenides
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83. Machine learning for multi-fidelity scale bridging and dynamical simulations of materials
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84. Prediction of conformationally dependent atomic multipole moments in carbohydrates
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85. Automatically Constructed Neural Network Potentials for Molecular Dynamics Simulation of Zinc Proteins
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86. Introduction
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87. Knowledge extraction and performance improvement of Bi2Te3-based thermoelectric materials by machine learning
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88. RETRACTED ARTICLE: Comparing ANI-2x, ANI-1ccx neural networks, force field, and DFT methods for predicting conformational potential energy of organic molecules
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89. Constructing Grids for Molecular Quantum Dynamics Using an Autoencoder
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90. Efficient and Adaptive Methods for Computing Accurate Potential Surfaces for Quantum Nuclear Effects: Applications to Hydrogen-Transfer Reactions
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91. Application of Clustering Algorithms to Partitioning Configuration Space in Fitting Reactive Potential Energy Surfaces
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92. Energetics and dynamics of CH4 and H2O dissociation on metal surfaces
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93. Optimal radial basis for density-based atomic representations
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94. Machine learning prediction of interaction energies in rigid water clusters
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95. Processing Optimization and Property Predictions of Hot‐Extruded Bi–Te–Se Thermoelectric Materials via Machine Learning
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96. Next-Generation Computational and Experimental Tools for Understanding Nucleation and Growth of Metal–Organic Frameworks
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97. Accurate rovibrational energies of ozone isotopologues up to J = 10 utilizing artificial neural networks
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98. Automated Construction of Neural Network Potential Energy Surface: The Enhanced Self-Organizing Incremental Neural Network Deep Potential Method
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99. Fifty years of amorphous silicon models : the end of the story?
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