Quantum-chemical insights from deep tensor neural networks

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Items 101–200 of 1,001

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5. Hierarchical modeling of molecular energies using a deep neural network
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9. Deep Potential: A General Representation of a Many-Body Potential Energy Surface
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10. Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale
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11. Neural Network Potential Energy Surfaces for Small Molecules and Reactions
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18. Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation
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19. The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules
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20. GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation
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21. Applying machine learning techniques to predict the properties of energetic materials
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22. Embedded Atom Neural Network Potentials: Efficient and Accurate Machine Learning with a Physically Inspired Representation
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23. Deep Learning-Based Weather Prediction: A Survey
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24. Explaining the unique nature of individual gait patterns with deep learning
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25. OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features
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26. "What is relevant in a text document?": An interpretable machine learning approach
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27. Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra
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28. Uncovering Flooding Mechanisms Across the Contiguous United States Through Interpretive Deep Learning on Representative Catchments
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29. A quantitative uncertainty metric controls error in neural network-driven chemical discovery
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30. Scope of machine learning in materials research—A review
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31. A deep potential model with long-range electrostatic interactions
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32. Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation
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33. sGDML: Constructing accurate and data efficient molecular force fields using machine learning
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34. Quantum Machine Learning in Chemical Compound Space
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35. Machine Learning Interatomic Potentials and Long-Range Physics
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36. Quantum machine learning using atom-in-molecule-based fragments selected on the fly
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37. A practical guide to machine learning interatomic potentials – Status and future
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38. Stress Field Prediction in Cantilevered Structures Using Convolutional Neural Networks
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39. 70 years of machine learning in geoscience in review
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40. Building attention and edge message passing neural networks for bioactivity and physical–chemical property prediction
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41. Molecular Imprinting: Materials Nanoarchitectonics with Molecular Information
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42. Deep Learning in Protein Structural Modeling and Design
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43. Learning Molecular Representations for Medicinal Chemistry
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44. 70 years of machine learning in geoscience in review
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45. Machine learning potentials for complex aqueous systems made simple
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46. DeePCG: Constructing coarse-grained models via deep neural networks
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47. Machine Learning: Quantum vs Classical
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48. Transferability in Machine Learning for Electronic Structure via the Molecular Orbital Basis
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49. Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems
    Paraskevi Gkeka, Gabriel Stoltz, Amir Barati Farimani, Zineb Belkacemi, Michele Ceriotti, John D. Chodera, Aaron R. Dinner, Andrew L. Ferguson, Jean‐Bernard Maillet, Hervé Minoux et al. · 2020 · Journal of Chemical Theory and Computation
50. Committee neural network potentials control generalization errors and enable active learning
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51. Perspective on integrating machine learning into computational chemistry and materials science
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52. Coarse graining molecular dynamics with graph neural networks
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53. Molecular Semiconductors for Logic Operations: Dead‐End or Bright Future?
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54. Machine learning in polymer informatics
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55. Ab Initio Machine Learning in Chemical Compound Space
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56. Learning atoms for materials discovery
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57. Conditional Molecular Design with Deep Generative Models
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58. Innovative Materials Science via Machine Learning
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59. Combining SchNet and SHARC: The SchNarc Machine Learning Approach for Excited-State Dynamics
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60. Incompleteness of Atomic Structure Representations
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61. CLEVR-XAI: A benchmark dataset for the ground truth evaluation of neural network explanations
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62. Inverse design of 3d molecular structures with conditional generative neural networks
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63. Data Generation for Machine Learning Interatomic Potentials and Beyond
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64. SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties
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65. Computational prediction of chemical reactions: current status and outlook
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66. Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective
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67. Machine Learning for Predicting the Band Gaps of ABX 3 Perovskites from Elemental Properties
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68. Machine learning for chemical discovery
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69. Choosing the right molecular machine learning potential
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70. Review of Pedestrian Trajectory Prediction Methods: Comparing Deep Learning and Knowledge-Based Approaches
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71. Roadmap on Machine learning in electronic structure
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72. Accurate global machine learning force fields for molecules with hundreds of atoms
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73. DPA-2: a large atomic model as a multi-task learner
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74. Machine-learned multi-system surrogate models for materials prediction
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75. Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning
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76. Combining electronic and structural features in machine learning models to predict organic solar cells properties
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77. Machine learning of solvent effects on molecular spectra and reactions
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78. Molecule Attention Transformer
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79. Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex
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80. Deep learning and the Schrödinger equation
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81. Machine learning of molecular properties: Locality and active learning
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82. End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems
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83. Recent advances in machine learning towards multiscale soft materials design
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84. Molecular design in drug discovery: a comprehensive review of deep generative models
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86. Message Passing Neural Networks
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87. Graph neural networks for an accurate and interpretable prediction of the properties of polycrystalline materials
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88. Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization
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89. Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations
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90. DPA-2: a large atomic model as a multi-task learner
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91. QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules
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92. Development of Multimodal Machine Learning Potentials: Toward a Physics-Aware Artificial Intelligence
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93. Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture
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94. Structure-based sampling and self-correcting machine learning for accurate calculations of potential energy surfaces and vibrational levels
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95. Motif-based Graph Self-Supervised Learning for Molecular Property Prediction
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96. Low rank tensor completion for multiway visual data
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97. Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning
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98. JAX, M.D. A framework for differentiable physics*
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99. Atomistic modeling of the mechanical properties: the rise of machine learning interatomic potentials
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100. Message-passing neural networks for high-throughput polymer screening
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