On representing chemical environments

Cited by (1,001)

Items 501–600 of 1,001

1. Discrepancies and error evaluation metrics for machine learning interatomic potentials
   Yunsheng Liu, Xingfeng He, Yifei Mo · 2023 · npj Computational Materials
2. An assessment of the structural resolution of various fingerprints commonly used in machine learning
   Behnam Parsaeifard, Deb Sankar De, Anders S Christensen, Felix A. Faber, Emir Kocer, Sandip De, Jörg Behler, O Anatole von Lilienfeld, Stefan Goedecker · 2020 · Machine Learning Science and Technology
3. TSNet: predicting transition state structures with tensor field networks and transfer learning
   Riley Jackson, Wenyuan Zhang, Jason K. Pearson · 2021 · Chemical Science
4. The development of nucleic acids force fields: From an unchallenged past to a competitive future
   Korbinian Liebl, Martin Zacharias · 2022 · Biophysical Journal
5. Unraveling Thermal Transport Correlated with Atomistic Structures in Amorphous Gallium Oxide via Machine Learning Combined with Experiments
   Yuanbin Liu, Huili Liang, Lei Yang, Guang Yang, Hong-Ao Yang, Shuang Song, Zengxia Mei, Gábor Cśanyi, Bing Cao · 2023 · Advanced Materials
6. Data-efficient fine-tuning of foundational models for first-principles quality sublimation enthalpies
   Harveen Kaur, Flaviano Della Pia, Ilyes Batatia, Xavier R. Advincula, Benjamin X. Shi, Jinggang Lan, Gábor Cśanyi, Angelos Michaelides, Venkat Kapil · 2024 · Faraday Discussions
7. Learning molecular energies using localized graph kernels
   Grégoire Ferré, Terry Scot Haut, Kipton M. Barros · 2017 · The Journal of Chemical Physics
8. Machine learning force fields based on local parametrization of dispersion interactions: Application to the phase diagram of C 60
   Heikki Muhli, Xi Chen, Albert P. Bartók, Patricia Hernández-León, Gábor Cśanyi, Tapio Ala-Nissilä, A. Miguel · 2021 · Physical review. B./Physical review. B
9. A Differentiable Neural-Network Force Field for Ionic Liquids
   Hadrián Montes‐Campos, Jesús Carrete, Sebastian Bichelmaier, Luis M. Varela, Georg K. H. Madsen · 2021 · Journal of Chemical Information and Modeling
10. CEGANN: Crystal Edge Graph Attention Neural Network for multiscale classification of materials environment
    Suvo Banik, Debdas Dhabal, Henry Chan, Sukriti Manna, Mathew J. Cherukara, Valeria Molinero, Subramanian K. R. S. Sankaranarayanan · 2023 · npj Computational Materials
11. Gaussian process regression to accelerate geometry optimizations relying on numerical differentiation
    Gunnar Alexander Schmitz, Ove Christiansen A · 2018 · The Journal of Chemical Physics
12. Equation of State of Fluid Methane from First Principles with Machine Learning Potentials
    Max Veit, Sandeep Kumar Jain, Satyanarayana Bonakala, Indranil Rudra, Detlef Hohl, Gábor Cśanyi · 2019 · Journal of Chemical Theory and Computation
13. Heterogeneous catalysis from structure to activity via SSW-NN method
    Sicong Ma, Cheng Shang, Zhi‐Pan Liu · 2019 · The Journal of Chemical Physics
14. Computing RPA Adsorption Enthalpies by Machine Learning Thermodynamic Perturbation Theory
    Bilal Chehaibou, Michaël Badawi, Tomáš Bučko, Timur Bazhirov, Dario Rocca · 2019 · Journal of Chemical Theory and Computation
15. Kernel-Based Machine Learning for Efficient Simulations of Molecular Liquids
    Christoph Scherer, René Scheid, Denis Andrienko, Tristan Bereau · 2020 · Journal of Chemical Theory and Computation
16. Multi-scale approach for the prediction of atomic scale properties
    Andrea Grisafi, Jigyasa Nigam, Michele Ceriotti · 2020 · Chemical Science
17. Ranking the information content of distance measures
    Aldo Glielmo, Claudio Zeni, Bingqing Cheng, Gábor Cśanyi, Alessandro Laio · 2022 · PNAS Nexus
18. High-entropy alloy catalysts: high-throughput and machine learning-driven design
    Lixin Chen, Zhiwen Chen, Xue Yao, Baoxian Su, Weijian Chen, Xin Pang, Keun-Su Kim, Chandra Veer Singh, Yu Zou · 2022 · Journal of Materials Informatics
19. Spin-dependent graph neural network potential for magnetic materials
    Hongyu Yu, Yang Zhong, Liangliang Hong, Changsong Xu, Wei Ren, Xin-Gao Gong, Hongjun Xiang · 2024 · Physical review. B./Physical review. B
20. Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry
    Austin M. Mroz, Annabel R. Basford, Friedrich Hastedt, Isuru Shavindra Jayasekera, Irea Mosquera‐Lois, Ruby Sedgwick, Pedro J. Ballester, Joshua D. Bocarsly, Ehecatl Antonio del Rio‐Chanona, Matthew L. Evans et al. · 2025 · Chemical Society Reviews
21. libmolgrid: Graphics Processing Unit Accelerated Molecular Gridding for Deep Learning Applications
    Jocelyn Sunseri, David Ryan Koes · 2020 · Journal of Chemical Information and Modeling
22. Computational discovery of energy materials in the era of big data and machine learning: A critical review
    Ziheng Lu · 2021 · Materials Reports Energy
23. Equivariant representations for molecular Hamiltonians and N -center atomic-scale properties
    Jigyasa Nigam, Michael J. Willatt, Michele Ceriotti · 2021 · The Journal of Chemical Physics
24. Accurate large-scale simulations of siliceous zeolites by neural network potentials
    Erlebach, Andreas, Nachtigall, Petr, Grajciar, Lukáš · 2022 · npj Computational Materials
25. Short-range order and its impacts on the BCC MoNbTaW multi-principal element alloy by the machine-learning potential
    Pedro Antonio Santos-Flórez, Shi-Cheng Dai, Yi Yao, Howard Yanxon, Lin Li, Yun-Jiang Wang, Qiang Zhu, Xiao-Xiang Yu · 2023 · Acta Materialia
26. Unraveling the Potential of Solid-State Hydrogen Storage Materials: Insights from First Principle Calculations
    Yaohui Xu, Yang Min Zhou, Chaoqun Li, Shuai Dong, Hao Liu, Weijie Yang, Yuting Li, Han Jiang, Zhao Ding, Hao Li et al. · 2024 · Fuel
27. Deep Learning for Optoelectronic Properties of Organic Semiconductors
    Chengqiang Lu, Qi Liu, Qiming Sun, Chang‐Yu Hsieh, Shengyu Zhang, Liang Shi, Chee‐Kong Lee · 2020 · The Journal of Physical Chemistry C
28. Machine learning potential era of zeolite simulation
    Ma, Sicong, Liu, Zhi-Pan · 2022 · Chemical Science
29. A Look Inside the Black Box of Machine Learning Photodynamics Simulations
    Jingbai Li, Steven A. Lopez · 2022 · Accounts of Chemical Research
30. Training data selection for accuracy and transferability of interatomic potentials
    David Montes de Oca Zapiain, Mitchell A. Wood, Nicholas Lubbers, Carlos Z. Pereyra, Aidan P. Thompson, Danny Pérez · 2022 · npj Computational Materials
31. A machine learning interatomic potential for high entropy alloys
    Lianping Wu, Teng Li · 2024 · Journal of the Mechanics and Physics of Solids
32. Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials
    Viktor Zaverkin, David Holzmüller, Henrik Christiansen, Federico Errica, Francesco Alesiani, Makoto Takamoto, Mathias Niepert, Johannes Kästner · 2024 · npj Computational Materials
33. Rise of machine learning potentials in heterogeneous catalysis: Developments, applications, and prospects
    Seokhyun Choung, Wongyu Park, Jinuk Moon, Jeong Woo Han · 2024 · Chemical Engineering Journal
34. Atomistic modeling of nanoscale plasticity in high-entropy alloys
    Zachary H. Aitken, V. Sorkin, Yong‐Wei Zhang · 2019 · Journal of materials research/Pratt's guide to venture capital sources
35. Extensive deep neural networks for transferring small scale learning to large scale systems
    Kyle Mills, Kevin Ryczko, Iryna Luchak, Adam Domurad, Chris Beeler, Isaac Tamblyn · 2019 · Chemical Science
36. Gaussian Process-Based Refinement of Dispersion Corrections
    Jonny Proppe, Stefan Gugler, Markus Reiher · 2019 · Journal of Chemical Theory and Computation
37. Machine learning scheme for fast extraction of chemically interpretable interatomic potentials
    Pavel E. Dolgirev, Ivan A. Kruglov, Артем Ромаевич Оганов · 2016 · AIP Advances
38. Machine learning-inspired battery material innovation
    Man‐Fai Ng, Yongming Sun, Zhi Wei Seh · 2023 · Energy Advances
39. Temperature effect on the phonon dispersion stability of zirconium by machine learning driven atomistic simulations
    Xin Qian, Ronggui Yang · 2018 · Physical review. B./Physical review. B
40. Machine learning for potential energy surfaces: An extensive database and assessment of methods
    Gunnar Alexander Schmitz, Ian H. Godtliebsen, Ove Christiansen A · 2019 · The Journal of Chemical Physics
41. PANNA: Properties from Artificial Neural Network Architectures
    Ruggero Lot, Franco Pellegrini, Yusuf Shaidu, Emine Küçükbenli · 2020 · Computer Physics Communications
42. Detection of topological materials with machine learning
    Nikolas H Claussen, Bogdan Andrei Bernevig, Nicolas Regnault · 2020 · Physical review. B./Physical review. B
43. An Ecosystem for Digital Reticular Chemistry
    Kevin Maik Jablonka, Andrew Rosen, Aditi S. Krishnapriyan, Berend Smit · 2023 · ACS Central Science
44. A Perspective on Deep Learning for Molecular Modeling and Simulations
    Jun Zhang, Yao-Kun Lei, Zhen Zhang, Junhan Chang, Maodong Li, Xu Han, Lijiang Yang, Yi Yang, Yi Qin Gao · 2020 · The Journal of Physical Chemistry A
45. Machine Learning Prediction of Superconducting Critical Temperature through the Structural Descriptor
    Jingzi Zhang, Zhuoxuan Zhu, X.‐D. Xiang, Ke Zhang, Shangchao Huang, Chengquan Zhong, Hua‐Jun Qiu, Kailong Hu, Xi Lin · 2022 · The Journal of Physical Chemistry C
46. Machine Learning Potential for Electrochemical Interfaces with Hybrid Representation of Dielectric Response
    Jia-Xin Zhu, Jun Cheng · 2025 · Physical Review Letters
47. Towards better efficiency of interatomic linear machine learning potentials
    Alexandra M. Goryaeva, Jean‐Bernard Maillet, Mihai‐Cosmin Marinica · 2019 · Computational Materials Science
48. Challenges, Opportunities, and Prospects in Metal Halide Perovskites from Theoretical and Machine Learning Perspectives
    Myung, Chang Woo, Hajibabaei, Amir, Cha, Ji‐Hyun, Ha, Miran, Kim, Junu, Kim, Kwang S. · 2022 · Advanced Energy Materials
49. Structure prediction of crystals, surfaces and nanoparticles
    Scott M. Woodley, Graeme Matthew Day, C. Richard A. Catlow · 2020 · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences
50. Unraveling Crystallization Mechanisms and Electronic Structure of Phase‐Change Materials by Large‐Scale Ab Initio Simulations
    Yazhi Xu, Yuxing Zhou, Xudong Wang, Wei Zhang, Evan Ma, Volker L. Deringer, Riccardo Mazzarello · 2022 · Advanced Materials
51. Examination of computed aluminum grain boundary structures and energies that span the 5D space of crystallographic character
    Eric R. Homer, Gus L. W. Hart, C. Braxton Owens, Derek M. Hensley, Jay C. Spendlove, Lydia Harris Serafin · 2022 · Acta Materialia
52. Reaction dynamics of Diels–Alder reactions from machine learned potentials
    Tom A. Young, Tristan Johnston-Wood, Hanwen Zhang, Fernanda Duarte · 2022 · Physical Chemistry Chemical Physics
53. Neural network potentials for accelerated metadynamics of oxygen reduction kinetics at Au–water interfaces
    Xin Li Yang, Arghya Bhowmik, Tejs Vegge, Heine Anton Hansen · 2023 · Chemical Science
54. Machine Learning-Based Computational Design Methods for High-Entropy Alloys
    Yan Ming Zhao, J. Y. Zhang, Peter Kaehuei Liaw, Tao Yang · 2025 · High Entropy Alloys & Materials
55. Atomate2: modular workflows for materials science
    Alex M. Ganose, Hrushikesh P. Sahasrabuddhe, Mark Asta, Kevin Beck, Tathagata Biswas, Alexander Bonkowski, Joana Bustamante, Xin Chen, Yuan Chiang, D. C. Chrzan et al. · 2025 · Digital Discovery
56. Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications
    Kai Zhu, Enrico Trizio, Jintu Zhang, Renling Hu, Linlong Jiang, Tingjun Hou, Luigi Bonati · 2025 · Chemical Reviews
57. Application of machine learning potentials to predict grain boundary properties in fcc elemental metals
    Takayuki Nishiyama, Atsuto Seko, Isao Tanaka · 2020 · Physical Review Materials
58. Predicting the propensity for thermally activated β events in metallic glasses via interpretable machine learning
    Qi S. Wang, Jun Cai Ding, Zhang Longfei, Evgeny V. Podryabinkin, Alexander V. Shapeev, Evan Ma · 2020 · npj Computational Materials
59. Digital navigation of energy–structure–function maps for hydrogen-bonded porous molecular crystals
    Chengxi Zhao, Linjiang Chen, Yu Che, Zhongfu Pang, Xiaofeng Wu, Yunxiang Lu, Honglai Liu, Graeme Matthew Day, Andrew Ian Cooper · 2021 · Nature Communications
60. Accuracy of Machine Learning Potential for Predictions of Multiple-Target Physical Properties*
    Yulou Ouyang, Zhongwei Zhang, Cuiqian Yu, Jia He, Gang Feng Yan, Jie Chen · 2020 · Chinese Physics Letters
61. Equivariant analytical mapping of first principles Hamiltonians to accurate and transferable materials models
    Liwei Zhang, Berk Onat, Geneviève Dusson, Adam McSloy, G. Anand, Reinhard J. Maurer, Christoph Ortner, James R. Kermode · 2022 · npj Computational Materials
62. Local-order metric for condensed-phase environments
    Fausto Martelli, Hsin-Yu Ko, Erdal C. Oğuz, Roberto Car · 2018 · Physical review. B./Physical review. B
63. Analysis of the Geometrical Evolution in On-the-Fly Surface-Hopping Nonadiabatic Dynamics with Machine Learning Dimensionality Reduction Approaches: Classical Multidimensional Scaling and Isometric Feature Mapping
    Xusong Li, Yu Yang Xie, Deping Hu, Zhenggang Lan · 2017 · Journal of Chemical Theory and Computation
64. Weighted Averaging Scheme and Local Atomic Descriptor for pKa Prediction Based on Density Functional Theory
    Haoyu S. Yu, Mark A. Watson, Arteum D. Bochevarov · 2018 · Journal of Chemical Information and Modeling
65. Force Field for Water Based on Neural Network
    Hao Wang, Weitao Yang · 2018 · The Journal of Physical Chemistry Letters
66. Monte Carlo Simulations of Au 38 (SCH 3 ) 24 Nanocluster Using Distance-Based Machine Learning Methods
    Antti Pihlajamäki, Joonas Hämäläinen, Joakim Linja, P. Nieminen, Sami Malola, Tommi Kärkkäinen, Hannu Häkkinen · 2020 · The Journal of Physical Chemistry A
67. Dynamical strengthening of covalent and non-covalent molecular interactions by nuclear quantum effects at finite temperature
    Huziel E. Sauceda, Valentín Vassilev-Galindo, Stefan Chmiela, Klaus-Robert Müller, Alexandre Tkatchenko · 2021 · Nature Communications
68. Machine Learning in Nano-Scale Biomedical Engineering
    Alexandros–Apostolos A. Boulogeorgos, Stylianos E. Trevlakis, Sotiris A. Tegos, Vasilis K. Papanikolaou, George K. Karagiannidis · 2020 · IEEE Transactions on Molecular Biological and Multi-Scale Communications
69. Microscopic (Dis)order and Dynamics of Cations in Mixed FA/MA Lead Halide Perovskites
    Helen Grüninger, Menno Bokdam, Nico Leupold, Paul Tinnemans, Ralf Moos, G. A. de Wijs, Fabian Panzer, Arno P. M. Kentgens · 2021 · The Journal of Physical Chemistry C
70. Application of deep learning to inverse design of phase separation structure in polymer alloy
    Kazuya Hiraide, Kenta Hirayama, Katsuhiro Endo, Mayu Muramatsu · 2021 · Computational Materials Science
71. Metastable materials discovery in the age of large-scale computation
    Félix Therrien, Eric B. Jones, Vladan Stevanović · 2021 · Applied Physics Reviews
72. Quantifying Chemical Structure and Machine‐Learned Atomic Energies in Amorphous and Liquid Silicon
    Noam Bernstein, Bishal Bhattarai, Gábor Cśanyi, David A. Drabold, Stephen R. Elliott, Volker L. Deringer · 2019 · Angewandte Chemie International Edition
73. Identifying and Tracking Defects in Dynamic Supramolecular Polymers
    Piero Gasparotto, Davide Bochicchio, Michele Ceriotti, Giovanni Maria Pavan · 2019 · The Journal of Physical Chemistry B
74. MAISE: Construction of neural network interatomic models and evolutionary structure optimization
    Samad Hajinazar, Aidan Thorn, Ernesto D. Sandoval, Saba Kharabadze, Aleksey N. Kolmogorov · 2020 · Computer Physics Communications
75. Machine Learning Potential for Hexagonal Boron Nitride Applied to Thermally and Mechanically Induced Rippling
    Fabian L. Thiemann, Patrick Rowe, Erich A. Müller, Angelos Michaelides · 2020 · The Journal of Physical Chemistry C
76. Reinforcing materials modelling by encoding the structures of defects in crystalline solids into distortion scores
    Alexandra M. Goryaeva, Clovis Lapointe, Chendi Dai, Julien Dérès, Jean‐Bernard Maillet, Mihai‐Cosmin Marinica · 2020 · Nature Communications
77. Anharmonic Correction to Adsorption Free Energy from DFT-Based MD Using Thermodynamic Integration
    Jonas Amsler, Philipp N. Pleßow, Felix Studt, Tomáš Bučko · 2021 · Journal of Chemical Theory and Computation
78. Development of a general-purpose machine-learning interatomic potential for aluminum by the physically informed neural network method
    G. P. Purja Pun, V. Yamakov, J. Hickman, E. H. Glaessgen, Y. Mishin · 2020 · Physical Review Materials
79. α − β phase transition of zirconium predicted by on-the-fly machine-learned force field
    Peitao Liu, Carla Verdi, Ferenc Karsai, Georg Kresse · 2021 · Physical Review Materials
80. Learning molecular potentials with neural networks
    Hatice Gökcan, Olexandr Isayev · 2021 · Wiley Interdisciplinary Reviews Computational Molecular Science
81. Electrostatic Embedding of Machine Learning Potentials
    Kirill Zinovjev · 2023 · Journal of Chemical Theory and Computation
82. Modeling high-entropy transition metal alloys with alchemical compression
    Nataliya Lopanitsyna, Guillaume Fraux, Maximilian A. Springer, Sandip De, Michele Ceriotti · 2023 · Physical Review Materials
83. AI Approaches to Homogeneous Catalysis with Transition Metal Complexes
    Lucía Morán‐González, Arron L. Burnage, Ainara Nova, David Balcells · 2025 · ACS Catalysis
84. Extrapolating Energetics on Clusters and Single-Crystal Surfaces to Nanoparticles by Machine-Learning Scheme
    Ryosuke Jinnouchi, Hirohito Hirata, Ryoji Asahi · 2017 · The Journal of Physical Chemistry C
85. An accelerating approach of designing ferromagnetic materials via machine learning modeling of magnetic ground state and Curie temperature
    Teng Long, Nuno M. Fortunato, Yixuan Zhang, Oliver Gutfleisch, Hongbin Zhang · 2021 · Materials Research Letters
86. Molecular Dynamics Simulations of Crystal Nucleation from Solution at Constant Chemical Potential
    Tarak Karmakar, Pablo M. Piaggi, Michele Parrinello · 2019 · Journal of Chemical Theory and Computation
87. Robust recognition and exploratory analysis of crystal structures via Bayesian deep learning
    Andreas Leitherer, Angelo Ziletti, L. Ghiringhelli · 2021 · Nature Communications
88. Efficient implementation of atom-density representations
    Félix Musil, Max Veit, Alexander Goscinski, Guillaume Fraux, Michael J. Willatt, Markus Stricker, Till Junge, Michele Ceriotti · 2021 · The Journal of Chemical Physics
89. Machine learning in computational NMR-aided structural elucidation
    Iván Cortés, Cristina Cuadrado, Antonio Hernández Daranas, Ariel M. Sarotti · 2023 · Frontiers in Natural Products
90. Accurate Fourth-Generation Machine Learning Potentials by Electrostatic Embedding
    Tsz Wai Ko, Jonas A. Finkler, Stefan Goedecker, Jörg Behler · 2023 · Journal of Chemical Theory and Computation
91. Review: Machine learning for advancing low-temperature plasma modeling and simulation
    Jan Trieschmann, Luca Vialetto, Tobias Gergs · 2023 · Journal of Micro/Nanopatterning Materials and Metrology
92. Machine Learning Interatomic Potentials for Heterogeneous Catalysis
    Deqi Tang, Rangsiman Ketkaew, Sandra Luber · 2024 · Chemistry - A European Journal
93. Review: machine learning approaches for diverse alloy systems
    Arafat Rahman, Md Sojib Hossain, Abdullah-Bin Siddique · 2025 · Journal of Materials Science
94. The CALYPSO methodology for structure prediction*
    Qunchao Tong, Jian Lv, Pengyue Gao, Yanchao Wang · 2019 · Chinese Physics B
95. Autobifunctional Mechanism of Jagged Pt Nanowires for Hydrogen Evolution Kinetics via End-to-End Simulation
    Geun Ho Gu, Juhyung Lim, Chengzhang Wan, Tao Cheng, Heting Pu, Sungwon Kim, Juhwan Noh, Changhyeok Choi, Juhwan Kim, William A. Goddard et al. · 2021 · Journal of the American Chemical Society
96. Automated Development of Molten Salt Machine Learning Potentials: Application to LiCl
    Ganesh Sivaraman, Jicheng Guo, Logan T. Ward, Nathaniel C. Hoyt, Mark A. Williamson, Ian T Foster, Chris J. Benmore, Nicholas E. Jackson · 2021 · The Journal of Physical Chemistry Letters
97. On the Role of Long-Range Electrostatics in Machine-Learned Interatomic Potentials for Complex Battery Materials
    Carsten G. Staacke, Hendrik H. Heenen, Christoph Scheurer, Gábor Cśanyi, Karsten Reuter, Johannes T. Margraf · 2021 · ACS Applied Energy Materials
98. Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields
    Huziel E. Sauceda, Michael Gastegger, Stefan Chmiela, Klaus‐Robert Müller, Alexandre Tkatchenko · 2020 · The Journal of Chemical Physics
99. Artificial neural networks for predicting charge transfer coupling
    Chun-I Wang, Ignasius Joanito, Chang‐Feng Lan, Chao‐Ping Hsu · 2020 · The Journal of Chemical Physics
100. Prediction of Thermal Properties of Zeolites through Machine Learning
     Maxime Ducamp, François‐Xavier Coudert · 2022 · The Journal of Physical Chemistry C