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

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Items 101–200 of 781

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2. Neural network potentials for metals and oxides – First applications to copper clusters at zinc oxide
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3. Automated Fitting of Neural Network Potentials at Coupled Cluster Accuracy: Protonated Water Clusters as Testing Ground
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4. Neural network potentials for chemistry: concepts, applications and prospects
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5. Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network
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6. Machine Learning Force Fields: Recent Advances and Remaining Challenges
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7. Global minimization of gold clusters by combining neural network potentials and the basin-hopping method
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8. The role of molecular modelling and simulation in the discovery and deployment of metal-organic frameworks for gas storage and separation
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9. Lithium Ion Conduction in Cathode Coating Materials from On-the-Fly Machine Learning
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10. Deep learning and density-functional theory
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11. Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces
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12. Reversible assembly of nanoparticles: theory, strategies and computational simulations
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13. Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces
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14. Efficient non-parametric fitting of potential energy surfaces for polyatomic molecules with Gaussian processes
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15. Machine Learning Potentials for Heterogeneous Catalysis
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16. Communication: An accurate global potential energy surface for the OH + CO → H + CO2 reaction using neural networks
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17. Discovering charge density functionals and structure-property relationships with PROPhet: A general framework for coupling machine learning and first-principles methods
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18. Constant size descriptors for accurate machine learning models of molecular properties
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19. QM7-X: A comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules
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20. Comparison of dissimilarity measures for cluster analysis of X-ray diffraction data from combinatorial libraries
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21. Neural-network-enhanced evolutionary algorithm applied to supported metal nanoparticles
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22. Hierarchical machine learning of potential energy surfaces
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23. The many-body expansion combined with neural networks
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24. Uncertainty estimation for molecular dynamics and sampling
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25. Improving the accuracy of Møller-Plesset perturbation theory with neural networks
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26. Bayesian machine learning for quantum molecular dynamics
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27. Fitting potential energy surfaces with fundamental invariant neural network. II. Generating fundamental invariants for molecular systems with up to ten atoms
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28. Ab Initio Potential Energy Surfaces and Quantum Dynamics for Polyatomic Bimolecular Reactions
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29. Neural-Network-Based Path Collective Variables for Enhanced Sampling of Phase Transformations
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30. Dynamics of Water Dissociative Chemisorption on Ni(111): Effects of Impact Sites and Incident Angles
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31. Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials
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32. Machine learning interatomic potential developed for molecular simulations on thermal properties of β-Ga2O3
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33. Recent advances in quantum scattering calculations on polyatomic bimolecular reactions
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34. Understanding the thermal properties of amorphous solids using machine-learning-based interatomic potentials
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35. A generalized deep learning approach for local structure identification in molecular simulations
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36. From ab Initio Potential Energy Surfaces to State-Resolved Reactivities: X + H2O ↔ HX + OH [X = F, Cl, and O(3P)] Reactions
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37. A reactive, scalable, and transferable model for molecular energies from a neural network approach based on local information
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38. High-dimensional neural network potentials for magnetic systems using spin-dependent atom-centered symmetry functions
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39. Representing Global Reactive Potential Energy Surfaces Using Gaussian Processes
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40. Learning Invariant Representations of Molecules for Atomization Energy Prediction
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41. Atomic Cluster Expansion for Quantum-Accurate Large-Scale Simulations of Carbon
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42. Representing the potential-energy surface of protonated water clusters by high-dimensional neural network potentials
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43. Automated construction of potential energy surfaces
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44. First-principles interatomic potentials for ten elemental metals via compressed sensing
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45. Structure of aqueous NaOH solutions: insights from neural-network-based molecular dynamics simulations
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46. Neural network and ReaxFF comparison for Au properties
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47. Automating the Development of High-Dimensional Reactive Potential Energy Surfaces with the robosurfer Program System
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48. Stratified construction of neural network based interatomic models for multicomponent materials
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49. A Deep-Learning Potential for Crystalline and Amorphous Li–Si Alloys
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50. Diabatic States of Molecules
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51. Machine learning activation energies of chemical reactions
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52. Fast, accurate, and transferable many-body interatomic potentials by symbolic regression
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53. Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications
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54. Six-dimensional quantum dynamics for dissociative chemisorption of H 2 and D 2 on Ag(111) on a permutation invariant potential energy surface
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55. Next generation interatomic potentials for condensed systems
    Christopher M. Handley, Jörg Behler · 2014 · The European Physical Journal B
56. Towards Data‐Driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning
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57. How to train a neural network potential
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58. Perspective: Atomistic simulations of water and aqueous systems with machine learning potentials
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59. Machine Learning for Accurate Force Calculations in Molecular Dynamics Simulations
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60. Robust Atomistic Modeling of Materials, Organometallic, and Biochemical Systems
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61. Full-dimensional and reduced-dimensional calculations of initial state-selected reaction probabilities studying the H + CH4 → H2 + CH3 reaction on a neural network PES
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62. Atomic Energies from a Convolutional Neural Network
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63. Microscopic Origins of the Anomalous Melting Behavior of Sodium under High Pressure
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64. Metadynamics for training neural network model chemistries: A competitive assessment
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65. Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks
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66. A “short blanket” dilemma for a state-of-the-art neural network potential for water: Reproducing experimental properties or the physics of the underlying many-body interactions?
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67. Perspectives on point defect thermodynamics
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68. Automatic Generation of Intermolecular Potential Energy Surfaces
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69. Integrated Molecular Modeling and Machine Learning for Drug Design
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70. Report of the Basic Energy Sciences Roundtable on Chemical Upcycling of Polymers
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71. Machine learning and excited-state molecular dynamics
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72. Bayesian force fields from active learning for simulation of inter-dimensional transformation of stanene
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73. A complete description of thermodynamic stabilities of molecular crystals
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74. Methane dissociation on Ni(111): A fifteen-dimensional potential energy surface using neural network method
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75. Permutationally Invariant Fitting of Many-Body, Non-covalent Interactions with Application to Three-Body Methane–Water–Water
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76. Big Data in Drug Discovery
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77. Learning protein-ligand binding affinity with atomic environment vectors
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78. Learning physical descriptors for materials science by compressed sensing
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79. Organic crystal structure prediction and its application to materials design
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80. An assessment of the structural resolution of various fingerprints commonly used in machine learning
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81. An accurate potential energy surface for the F + H2 → HF + H reaction by the coupled-cluster method
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82. Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks
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83. Ab InitioQuality NMR Parameters in Solid-State Materials Using a High-Dimensional Neural-Network Representation
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84. Enigmatic HCl + Au(111) Reaction: A Puzzle for Theory and Experiment
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85. Using Gradients in Permutationally Invariant Polynomial Potential Fitting: A Demonstration for CH 4 Using as Few as 100 Configurations
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86. Machine learning for molecular thermodynamics
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87. Systematic assessment of various universal machine‐learning interatomic potentials
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88. MLatom 2: An Integrative Platform for Atomistic Machine Learning
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89. Comparing the accuracy of high-dimensional neural network potentials and the systematic molecular fragmentation method: A benchmark study for all-trans alkanes
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90. Multi-scale approach for the prediction of atomic scale properties
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91. Six-dimensional potential energy surface of the dissociative chemisorption of HCl on Au(111) using neural networks
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92. Atomic-scale simulations in multi-component alloys and compounds: A review on advances in interatomic potential
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93. A nine-dimensional global potential energy surface for NH 4 (X 2 A 1 ) and kinetics studies on the H + NH 3 ↔ H 2 + NH 2 reaction
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94. Gaussian process regression to accelerate geometry optimizations relying on numerical differentiation
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95. Machine learning scheme for fast extraction of chemically interpretable interatomic potentials
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96. Permutation invariant potential energy surfaces for polyatomic reactions using atomistic neural networks
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97. Heterogeneous catalysis from structure to activity via SSW-NN method
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98. Evolutionary design of molecules based on deep learning and a genetic algorithm
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99. Ab initio neural network MD simulation of thermal decomposition of a high energy material CL-20/TNT
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100. Machine learning electron density in sulfur crosslinked carbon nanotubes
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