On representing chemical environments

Cited by (1,001)

Items 801–900 of 1,001

1. Advances in modeling complex materials: The rise of neuroevolution potentials
   Penghua Ying, Cheng Qian, Rui Zhao, Yanzhou Wang, Ke Wei Xu, Feng Ding, Shunda Chen, Zheyong Fan · 2025 · Chemical Physics Reviews
2. Benchmarking machine learning models for predicting lithium ion migration
   Artem D. Dembitskiy, Innokentiy S. Humonen, Roman A. Eremin, Dmitry A. Aksyonov, Stanislav S. Fedotov, Semen Budennyy · 2025 · npj Computational Materials
3. Fast and scalable prediction of local energy at grain boundaries: machine-learning based modeling of first-principles calculations
   Tomoyuki Tamura, Masayuki Karasuyama, Ryo Kobayashi, Ryuichi Arakawa, Yoshinori Shiihara, Ichiro Takeuchi · 2017 · Modelling and Simulation in Materials Science and Engineering
4. Significantly Improving the Prediction of Molecular Atomization Energies by an Ensemble of Machine Learning Algorithms and Rescanning Input Space: A Stacked Generalization Approach
   Ruobing Wang · 2018 · The Journal of Physical Chemistry C
5. Artificial Neural Networks as Trial Wave Functions for Quantum Monte Carlo
   Jan Kessler, Francesco Calcavecchia, Thomas D. Kühne · 2021 · Advanced Theory and Simulations
6. Machine Learning in Computational Chemistry: An Evaluation of Method Performance for Nudged Elastic Band Calculations
   Ralf Meyer, Klemens Schmuck, Andreas W. Hauser · 2019 · Journal of Chemical Theory and Computation
7. BAND NN: A Deep Learning Framework for Energy Prediction and Geometry Optimization of Organic Small Molecules
   Siddhartha Laghuvarapu, Yashaswi Pathak, U. Deva Priyakumar · 2019 · Journal of Computational Chemistry
8. Hydrogen evolution on non-metal oxide catalysts
   Stephen Rhatigan, Marie-Clara Michel, Michael Nolan · 2020 · Journal of Physics Energy
9. The role of feature space in atomistic learning
   Alexander Goscinski, Guillaume Fraux, Giulio Imbalzano, Michele Ceriotti · 2021 · Machine Learning Science and Technology
10. Empowering engineering with data, machine learning and artificial intelligence: a short introductive review
    Francisco Chinesta, Elías Cueto · 2022 · Advanced Modeling and Simulation in Engineering Sciences
11. Machine learning in energy chemistry: introduction, challenges and perspectives
    Yuzhi Xu, Jiankai Ge, Cheng‐Wei Ju · 2023 · Energy Advances
12. Dynamic Distortions of Quasi-2D Ruddlesden–Popper Perovskites at Elevated Temperatures: Influence on Thermal and Electronic Properties
    Raisa-Ioana Biega, Menno Bokdam, Kai Herrmann, John Mohanraj, Dominik Skrybeck, Mukundan Thelakkat, Markus Retsch, Linn Leppert · 2023 · The Journal of Physical Chemistry C
13. Robustness of Local Predictions in Atomistic Machine Learning Models
    Sanggyu Chong, Federico Grasselli, Chiheb Ben Mahmoud, Joe D. Morrow, Volker L. Deringer, Michele Ceriotti · 2023 · Journal of Chemical Theory and Computation
14. Computational Tools for Handling Molecular Clusters: Configurational Sampling, Storage, Analysis, and Machine Learning
    Jakub Kubečka, Vitus Besel, Ivo Neefjes, Yosef Knattrup, Theo Kurtén, Hanna Vehkamäki, Jonas Elm · 2023 · ACS Omega
15. Permutation-invariant distance between atomic configurations
    Grégoire Ferré, Jean‐Bernard Maillet, Gabriel Stoltz · 2015 · The Journal of Chemical Physics
16. A compact and accurate semi-global potential energy surface for malonaldehyde from constrained least squares regression
    Wataru Mizukami, Scott Habershon, David P. Tew · 2014 · The Journal of Chemical Physics
17. Machine learning accelerated photodynamics simulations
    Jingbai Li, Steven A. Lopez · 2023 · Chemical Physics Reviews
18. Durable high-entropy non-noble metal anodes for neutral seawater electrolysis
    Fumiya Shiokawa, Aimi Asilah Haji Tajuddin, Tatsuhiko Ohto, Yue Yu, Takeshi Fujita, H. Tanimoto, Zeyu Xi, Samuel Jeong, Yoshikazu Ito · 2023 · Chemical Engineering Journal
19. Sparse Gaussian process based machine learning first principles potentials for materials simulations: Application to batteries, solar cells, catalysts, and macromolecular systems
    Soohaeng Yoo Willow, Amir Hajibabaei, Miran Ha, David ChangMo Yang, Chang Woo Myung, Seung Kyu Min, Geunsik Lee, Kwang S. Kim · 2024 · Chemical Physics Reviews
20. Descriptor-Free Collective Variables from Geometric Graph Neural Networks
    Jintu Zhang, Luigi Bonati, Enrico Trizio, Odin Zhang, Yu Kang, Tingjun Hou, Michele Parrinello · 2024 · Journal of Chemical Theory and Computation
21. Applications of machine learning in surfaces and interfaces
    Shaofeng Xu, Jing‐Yuan Wu, Ying Guo, Qing Zhang, Xiaoxia Zhong, Jinjin Li, Wei Ren · 2025 · Chemical Physics Reviews
22. The Future of Catalysis: Applying Graph Neural Networks for Intelligent Catalyst Design
    Zhihao Wang, Wentao Li, Siying Wang, Xiaonan Wang · 2025 · Wiley Interdisciplinary Reviews Computational Molecular Science
23. Machine Learning Assisted Material Discovery: A Small Data Approach
    Qionghua Zhou, Xinyu Chen, Jinlan Wang · 2025 · Accounts of Materials Research
24. Domain oriented universal machine learning potential enables fast exploration of chemical space of battery electrolytes
    Feng Wang, Yuhang Tang, Ze-Bing Ma, Yucheng Jin, Jun Jie Cheng · 2025 · Nature Communications
25. Molecular dynamics study on β -phase vanadium monohydride with machine learning potential
    Kazutoshi Miwa, Hiroshi Ohno · 2016 · Physical review. B./Physical review. B
26. Globally Optimal Catalytic Fields – Inverse Design of Abstract Embeddings for Maximum Reaction Rate Acceleration
    Mark Dittner, Bernd Hartke · 2018 · Journal of Chemical Theory and Computation
27. Transferable MP2-Based Machine Learning for Accurate Coupled-Cluster Energies
    Jacob Townsend, Konstantinos D. Vogiatzis · 2020 · Journal of Chemical Theory and Computation
28. Deep spatial representation learning of polyamide nanofiltration membranes
    Ziyang Zhang, Yingtao Luo, Huawen Peng, Yu Chen, Rong‐Zhen Liao, Qiang Zhao · 2020 · Journal of Membrane Science
29. Exploring Librational Pathways with on-the-Fly Machine-Learning Force Fields: Methylammonium Molecules in MAPbX 3 (X = I, Br, Cl) Perovskites
    Menno Bokdam, Jonathan Lahnsteiner, D. D. Sarma · 2021 · The Journal of Physical Chemistry C
30. Phase diagram of the TIP4P/Ice water model by enhanced sampling simulations
    Sigbjørn Løland Bore, Pablo M. Piaggi, Roberto Car, Francesco Paesani · 2022 · The Journal of Chemical Physics
31. Physics-based representations for machine learning properties of chemical reactions
    Puck van Gerwen, Alberto Fabrizio, Matthew D. Wodrich, Clémence Corminbœuf · 2022 · Machine Learning Science and Technology
32. Machine Learning Full NMR Chemical Shift Tensors of Silicon Oxides with Equivariant Graph Neural Networks
    Maxwell C. Venetos, Mingjian Wen, Kristin Aslaug Persson · 2023 · The Journal of Physical Chemistry A
33. Machine learning properties of binary wurtzite superlattices
    Ghanshyam Pilania, Xiang-Yang Liu · 2018 · Journal of Materials Science
34. Machine learning characterization of structural defects in amorphous packings of dimers and ellipses
    Matt Harrington, Andrea J. Liu, Douglas J. Durian · 2019 · Physical review. E
35. Generating equilibrium molecules with deep neural networks
    Niklas W. A. Gebauer, Michael Gastegger, Kristof T. Schütt · 2018 · arXiv (Cornell University)
36. Density-of-states similarity descriptor for unsupervised learning from materials data
    Martin Kubáň, Santiago Rigamonti, Markus Scheidgen, Claudia Draxl · 2022 · Scientific Data
37. Arrested Phase Separation in Double-Exchange Models: Large-Scale Simulation Enabled by Machine Learning
    Puhan Zhang, Gia-Wei Chern · 2021 · Physical Review Letters
38. Room-temperature stacking disorder in layered covalent-organic frameworks from machine-learning force fields
    Ju Hua Huang, Seung‐Jae Shin, Kasper Tolborg, Alex M. Ganose, Gabriel Krenzer, Aron Walsh · 2023 · Materials Horizons
39. Influence of near-surface oxide layers on TiFe hydrogenation: mechanistic insights and implications for hydrogen storage applications
    Archa Santhosh, ShinYoung Kang, Nathan Daniel Keilbart, Brandon C. Wood, Thomas Klassen, Paul Jerabek, Martin Dornheim · 2023 · Journal of Materials Chemistry A
40. Global Neural Network Potential with Explicit Many-Body Functions for Improved Descriptions of Complex Potential Energy Surface
    Pei‐Lin Kang, Zhengxin Yang, Cheng Shang, Zhi‐Pan Liu · 2023 · Journal of Chemical Theory and Computation
41. Highly reliable and large-scale simulations of promising argyrodite solid-state electrolytes using a machine-learned moment tensor potential
    Ji Hoon Kim, Byeongsun Jun, Yong Jun Jang, Sun Ho Choi, Seong Hyeon Choi, Sung Man Cho, Yong-Gu Kim, Byung‐Hyun Kim, Sang Uck Lee · 2024 · Nano Energy
42. Crystallization Instead of Amorphization in Collision Cascades in Gallium Oxide
    J. Zhao, Javier García‐Fernández, Alexander Yu. Azarov, Ru He, Øystein Prytz, K. Nordlund, Mengyuan Hua, Flyura Djurabekova, Andrej Yu Kuznetsov · 2025 · Physical Review Letters
43. Machine Learning Force Fields in Electrochemistry: From Fundamentals to Applications
    Ryosuke Jinnouchi, Saori Minami · 2025 · ACS Nano
44. Toward Building Protein Force Fields by Residue-Based Systematic Molecular Fragmentation and Neural Network
    Hao Wang, Weitao Yang · 2018 · Journal of Chemical Theory and Computation
45. Multitribe evolutionary search for stable Cu–Pd–Ag nanoparticles using neural network models
    Samad Hajinazar, Ernesto D. Sandoval, Aiden Cullo, Aleksey N. Kolmogorov · 2019 · Physical Chemistry Chemical Physics
46. In operando active learning of interatomic interaction during large-scale simulations
    M Hodapp, A Shapeev · 2020 · Machine Learning Science and Technology
47. Atomistic structure learning algorithm with surrogate energy model relaxation
    Henrik Lund Mortensen, Søren Ager Meldgaard, Malthe Kjær Bisbo, Mads-Peter Verner Christiansen, Bjørk Hammer · 2020 · Physical review. B./Physical review. B
48. Adversarial reverse mapping of condensed-phase molecular structures: Chemical transferability
    Marc Stieffenhofer, Tristan Bereau, Michael Wand · 2021 · APL Materials
49. Group and Period-Based Representations for Improved Machine Learning Prediction of Heterogeneous Alloy Catalysts
    Xinyu Li, Raymond Chiong, Alister J. Page · 2021 · The Journal of Physical Chemistry Letters
50. Towards fully ab initio simulation of atmospheric aerosol nucleation
    Shuai Jiang, Yi-Rong Liu, Teng Hsing Huang, Yajuan Feng, Chunyu Wang, Zhongquan Wang, Bin-Jing Ge, Quan-Sheng Liu, Wei-Ran Guang, Wei Huang · 2022 · Nature Communications
51. Development of a physically-informed neural network interatomic potential for tantalum
    Yi-Shen Lin, Ganga P. Purja Pun, Yuri M. Mishin · 2021 · Computational Materials Science
52. Polarizability models for simulations of finite temperature Raman spectra from machine learning molecular dynamics
    Ethan Berger, Hannu‐Pekka Komsa · 2024 · Physical Review Materials
53. Discovery of superionic conductors by ensemble-scope descriptor
    Seiji Kajita, Nobuko Ohba, Akitoshi Suzumura, Shin Tajima, Ryoji Asahi · 2020 · NPG Asia Materials
54. The ANI-1ccx and ANI-1x Data Sets, Coupled-Cluster and Density Functional Theory Properties for Molecules
    Justin S. Smith, R.I. Zubatyuk, Benjamin T. Nebgen, Nicholas Lubbers, Kipton M. Barros, Adrián E. Roitberg, Olexandr Isayev, Sergei Tretiak · 2020 · ChemRxiv
55. The emergence of machine learning force fields in drug design
    Mingan Chen, Xinyu Jiang, Lehan Zhang, Xiaoxu Chen, Yiming Wen, Zhiyong Gu, Xutong Li, Mingyue Zheng · 2024 · Medicinal Research Reviews
56. How Poisoning Is Avoided in a Step of Relevance to the Haber–Bosch Catalysis
    Shivam Tripathi, Luigi Bonati, Simone Perego, Michele Parrinello · 2024 · ACS Catalysis
57. 3DReact: Geometric Deep Learning for Chemical Reactions
    Puck van Gerwen, Ksenia R. Briling, Charlotte Bunne, Vignesh Ram Somnath, Rubén Laplaza, Andreas Krause, Clémence Corminbœuf · 2024 · Journal of Chemical Information and Modeling
58. The taming of the screw: Dislocation cores in BCC metals and alloys
    Rui Wang, Lingyu Zhu, Subrahmanyam Pattamatta, David J. Srolovitz, Zhaoxuan Wu · 2024 · Materials Today
59. Advancing molecular machine learning representations with stereoelectronics-infused molecular graphs
    Daniil A. Boiko, Thiago Reschützegger, Benjamín Sánchez-Lengeling, Samuel M. Blau, Gabe Gomes · 2025 · Nature Machine Intelligence
60. Machine learning prediction of materials properties from chemical composition: Status and prospects
    Mohammed Alghadeer, Nufida D. Aisyah, Mahmoud Hezam, Saad M. Alqahtani, Ahmer A.B. Baloch, Fahhad H. Alharbi · 2024 · Chemical Physics Reviews
61. Dynamic Metal–Support Interaction Dictates Cu Nanoparticle Sintering on Al 2 O 3 Surfaces
    Jiayan Xu, Shreeja Das, Amar Deep Pathak, A. K. Patra, Sharan G. Shetty, Detlef Hohl, Roberto Car · 2025 · ACS Nano
62. Compressing physics with an autoencoder: Creating an atomic species representation to improve machine learning models in the chemical sciences
    John E. Herr, Kevin J. Koh, Kun Yao, John Parkhill · 2019 · The Journal of Chemical Physics
63. The evolving landscape for alloy design
    Tresa M. Pollock, Anton Van der Ven · 2019 · MRS Bulletin
64. A Gaussian process regression adaptive density guided approach for potential energy surface construction
    Gunnar Alexander Schmitz, Emil Lund Klinting, Ove Christiansen A · 2020 · The Journal of Chemical Physics
65. Exploring structure-property relationships in magnesium dissolution modulators
    Tim Würger, Di Mei, Bahram Vaghefinazari, David Alan Winkler, Sviatlana V. Lamaka, Mikhail L. Zheludkevich, Robert H. Meißner, Christian Feiler · 2021 · npj Materials Degradation
66. Chembot: A Machine Learning Approach to Selective Configuration Interaction
    Sergio D. Pineda Flores · 2021 · Journal of Chemical Theory and Computation
67. Modeling Electronic Response Properties with an Explicit-Electron Machine Learning Potential
    Maarten Cools‐Ceuppens, Joni Dambre, Toon Verstraelen · 2022 · Journal of Chemical Theory and Computation
68. CO organization at ambient pressure on stepped Pt surfaces: first principles modeling accelerated by neural networks
    Vaidish Sumaria, Philippe Sautet · 2021 · Chemical Science
69. Autonomous high-throughput computations in catalysis
    Stephan N. Steinmann, Angga Hermawan, Mohammed Bin Jassar, Zhi Wei Seh · 2022 · Chem Catalysis
70. Tackling Structural Complexity in Li2S-P2S5 Solid-State Electrolytes Using Machine Learning Potentials
    Carsten G. Staacke, Tabea Huss, Johannes T. Margraf, Karsten Reuter, Christoph Scheurer · 2022 · Nanomaterials
71. Charge Recombination Dynamics in a Metal Halide Perovskite Simulated by Nonadiabatic Molecular Dynamics Combined with Machine Learning
    Zhaosheng Zhang, Jiazheng Wang, Yingjie Zhang, Jianzhong Xu, Run Long · 2022 · The Journal of Physical Chemistry Letters
72. Collective Variables for Crystallization Simulations─from Early Developments to Recent Advances
    Neha Neha, Vikas Tiwari, Soumya Mondal, Nisha Kumari, Tarak Karmakar · 2022 · ACS Omega
73. Computation of CCSD(T)-Quality NMR Chemical Shifts via Δ-Machine Learning from DFT
    Julius B. Kleine Büning, Stefan Grimme · 2023 · Journal of Chemical Theory and Computation
74. Predicting the charge density response in metal electrodes
    Andrea Grisafi, Augustin Bussy, Mathieu Salanne, Rodolphe Vuilleumier · 2023 · Physical Review Materials
75. Completeness of atomic structure representations
    Jigyasa Nigam, Sergey N. Pozdnyakov, Kevin K. Huguenin-Dumittan, Michele Ceriotti · 2024 · APL Machine Learning
76. Machine learning for atomic forces in a crystalline solid: Transferability to various temperatures
    Teppei Suzuki, Ryo Tamura, Tsuyoshi Miyazaki · 2016 · International Journal of Quantum Chemistry
77. Neural network force fields for simple metals and semiconductors: construction and application to the calculation of phonons and melting temperatures
    Mário R. G. Marques, Jakob Wolff, Conrad Steigemann, Miguel A. L. Marques · 2019 · Physical Chemistry Chemical Physics
78. Frame-independent vector-cloud neural network for nonlocal constitutive modeling on arbitrary grids
    Xu‐Hui Zhou, Jiequn Han, Heng Xiao · 2021 · Computer Methods in Applied Mechanics and Engineering
79. Molecule‐Specific Uncertainty Quantification in Quantum Chemical Studies
    Reiher, Markus · 2022 · Israel Journal of Chemistry
80. Theoretical calculations and simulations power the design of inorganic solid-state electrolytes
    Lirong Xia, Hengzhi Liu, Yong Pei · 2024 · Nanoscale
81. Machine learning Hubbard parameters with equivariant neural networks
    Martin Uhrin, Austin Zadoks, Luca Binci, Nicola Marzari, Iurii Timrov · 2025 · npj Computational Materials
82. Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023
    Igor Poltavsky, Mirela Puleva, Anton Charkin-Gorbulin, Grégory Fonseca, Ilyes Batatia, Nicholas J. Browning, Stefan Chmiela, Mengnan Cui, J. Thorben Frank, Stefan Heinen et al. · 2025 · Chemical Science
83. Accelerating atomic structure search with cluster regularization
    Knud H. Sørensen, Mathias Jo̷rgensen, Albert Bruix, Bjørk Hammer · 2018 · The Journal of Chemical Physics
84. Predicting HSE band gaps from PBE charge densities via neural network functionals
    Levi C. Lentz, Alexie M. Kolpak · 2019 · Journal of Physics Condensed Matter
85. Matching crystal structures atom-to-atom
    Félix Therrien, Peter Graf, Vladan Stevanović · 2020 · The Journal of Chemical Physics
86. Revving up 13C NMR shielding predictions across chemical space: benchmarks for atoms-in-molecules kernel machine learning with new data for 134 kilo molecules
    Amit Kr. Gupta, Sabyasachi Chakraborty, Raghunathan Ramakrishnan · 2021 · Machine Learning Science and Technology
87. Reorganization energies of flexible organic molecules as a challenging target for machine learning enhanced virtual screening
    Ke Chen, Christian Künkel, Karsten Reuter, Johannes T. Margraf · 2022 · Digital Discovery
88. Protein Function Analysis through Machine Learning
    Chris Avery, John Patterson, Tyler J. Grear, Theodore D. Frater, Donald J. Jacobs · 2022 · Biomolecules
89. Unraveling the origin of reductive stability of super-concentrated electrolytes from first principles and unsupervised machine learning
    Feng Wang, Jun Jie Cheng · 2022 · Chemical Science
90. ænet-PyTorch: A GPU-supported implementation for machine learning atomic potentials training
    Jon López-Zorrilla, Xabier M. Aretxabaleta, In Won Yeu, I. Etxebarria, Hegoi Manzano, Nongnuch Artrith · 2023 · The Journal of Chemical Physics
91. Predicting vacancy migration energies in lattice-free environments using artificial neural networks
    Nicolas Castin, J.R. Fernández, Roberto C. Pasianot · 2014 · Computational Materials Science
92. An atomistic fingerprint algorithm for learning ab initio molecular force fields
    Yu-Hang Tang, Dongkun Zhang, George Em Karniadakis · 2018 · The Journal of Chemical Physics
93. Analysis of trajectory similarity and configuration similarity in on-the-fly surface-hopping simulation on multi-channel nonadiabatic photoisomerization dynamics
    Xusong Li, Deping Hu, Yu Yang Xie, Zhenggang Lan · 2018 · The Journal of Chemical Physics
94. Learning from the density to correct total energy and forces in first principle simulations
    Sebastian Dick, Mariví Fernández-Serra · 2019 · The Journal of Chemical Physics
95. Quantum Energy Regression using Scattering Transforms
    Matthew Hirn, Nicolas Poilvert, Stéphane Mallat · 2015 · arXiv (Cornell University)
96. 3DSC - a dataset of superconductors including crystal structures
    Timo Sommer, Roland Willa, Jörg Schmalian, Pascal Friederich · 2023 · Scientific Data
97. Predicting the complex stress-strain curves of polymeric solids by classification-embedded dual neural network
    Han Liu, Feng-Yang Wu, Gan‐Ji Zhong, Zhong‐Ming Li · 2023 · Materials & Design
98. Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure calculations
    Bruno Focassio, Michelangelo Domina, Urvesh Patil, A. Fazzio, Stefano Sanvito · 2023 · npj Computational Materials
99. A Very Deep Graph Convolutional Network for13C NMR Chemical Shift Calculations with Density Functional Theory Level Performance for Structure Assignment
    Wen-Jing Ai, Jing Li, Dongsheng Cao, Shao Liu, Yi-Yun Yuan, Yan Li, Gui‐Shan Tan, Kang-Ping Xu, Xia Yu, Fenghua Kang et al. · 2024 · Journal of Natural Products
100. Tracing Ion Migration in Halide Perovskites with Machine Learned Force Fields
     Viren Tyagi, Mike Pols, Geert Brocks, Shuxia Tao · 2025 · The Journal of Physical Chemistry Letters