On representing chemical environments

Cited by (1,002)

Items 401–500 of 1,002

1. Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks
   Daniel Schwalbe‐Koda, Aik Rui Tan, Rafael Gómez‐Bombarelli · 2021 · Nature Communications
2. The Future of Material Scientists in an Age of Artificial Intelligence
   Ayman Maqsood, Chen Chen, T. Jesper Jacobsson · 2024 · Advanced Science
3. The seventh blind test of crystal structure prediction: structure ranking methods
   Lily M. Hunnisett, Nicholas F. Francia, Jonas Nyman, Nathan S. Abraham, Srinivasulu Aitipamula, Tamador Alkhidir, Mubarak Almehairbi, Andrea Anelli, Dylan M. Anstine, John E. Anthony et al. · 2024 · Acta Crystallographica Section B Structural Science Crystal Engineering and Materials
4. Graph similarity drives zeolite diffusionless transformations and intergrowth
   Daniel Schwalbe‐Koda, Zach Jensen, Elsa Olivetti, Rafael Gómez‐Bombarelli · 2019 · Nature Materials
5. Chemist versus Machine: Traditional Knowledge versus Machine Learning Techniques
   Janine George, Geoffroy Hautier · 2020 · Trends in Chemistry
6. The evolution of machine learning potentials for molecules, reactions and materials
   Junfan Xia, Yaolong Zhang, Bin Jiang · 2025 · Chemical Society Reviews
7. Error-Controlled Exploration of Chemical Reaction Networks with Gaussian Processes
   Gregor N. C. Simm, Markus Reiher · 2018 · Journal of Chemical Theory and Computation
8. The Limit of Spin Lifetime in Solid-State Electronic Spins
   Alessandro Lunghi, Stefano Sanvito · 2020 · The Journal of Physical Chemistry Letters
9. Machine Learning for Accurate Force Calculations in Molecular Dynamics Simulations
   Punyaslok Pattnaik, Shampa Raghunathan, Tarun Kalluri, Prabhakar Bhimalapuram, C. V. Jawahar, U. Deva Priyakumar · 2020 · The Journal of Physical Chemistry A
10. Predicting Density Functional Theory-Quality Nuclear Magnetic Resonance Chemical Shifts via Δ-Machine Learning
    Pablo A. Unzueta, Chandler Greenwell, Gregory J. O. Beran · 2021 · Journal of Chemical Theory and Computation
11. REANN: A PyTorch-based end-to-end multi-functional deep neural network package for molecular, reactive, and periodic systems
    Yaolong Zhang, Junfan Xia, Bin Jiang · 2022 · The Journal of Chemical Physics
12. A Machine Learning Model of Chemical Shifts for Chemically and Structurally Diverse Molecular Solids
    Manuel Cordova, Edgar A. Engel, Artur Stefaniuk, Federico M. Paruzzo, Albert Hofstetter, Michele Ceriotti, Lyndon Emsley · 2022 · The Journal of Physical Chemistry C
13. Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine learning molecular dynamics simulations
    Yanzhou Wang, Zheyong Fan, Ping Qian, A. Miguel, Tapio Ala-Nissilä · 2023 · Physical review. B./Physical review. B
14. Machine Learning in Chemistry
    Jon Paul Janet, Heather J. Kulik · 2020 · ACS in focus
15. Global optimization of atomic structure enhanced by machine learning
    Malthe Kjær Bisbo, Bjørk Hammer · 2022 · Physical review. B./Physical review. B
16. Frustration in Super‐Ionic Conductors Unraveled by the Density of Atomistic States
    Shuo Wang, Yunsheng Liu, Yifei Mo · 2023 · Angewandte Chemie International Edition
17. Complimentary Computational Cues for Water Electrocatalysis: A DFT and ML Perspective
    Ahmed Badreldin, O. Bouhali, Ahmed Abdel‐Wahab · 2023 · Advanced Functional Materials
18. Multiscale approach for simulations of Kelvin probe force microscopy with atomic resolution
    Ali Sadeghi, A. Baratoff, S. Alireza Ghasemi, Stefan Goedecker, Thilo Glatzel, Shigeki Kawai, Ernst Meyer · 2012 · Physical Review B
19. Perspective on computational reaction prediction using machine learning methods in heterogeneous catalysis
    Jiayan Xu, Xiaoming Cao, P. Hu · 2021 · Physical Chemistry Chemical Physics
20. Efficient and transferable machine learning potentials for the simulation of crystal defects in bcc Fe and W
    Alexandra M. Goryaeva, Julien Dérès, Clovis Lapointe, Petr Grigorev, Thomas D. Swinburne, James R. Kermode, Lisa Ventelon, Jacopo Baima, Mihai‐Cosmin Marinica · 2021 · Physical Review Materials
21. MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows
    Pavlo O. Dral, Fuchun Ge, Yi-Fan Hou, Peikun Zheng, Yuxinxin Chen, Mario Barbatti, Olexandr Isayev, Cheng Wang, Bao-Xin Xue, Max Pinheiro et al. · 2024 · Journal of Chemical Theory and Computation
22. Next generation interatomic potentials for condensed systems
    Christopher M. Handley, Jörg Behler · 2014 · The European Physical Journal B
23. Mapping uncharted territory in ice from zeolite networks to ice structures
    Edgar A. Engel, Andrea Anelli, Michele Ceriotti, Chris J. Pickard, Richard J. Needs · 2018 · Nature Communications
24. Gaussian Process Regression for Transition State Search
    Alexander Denzel, Johannes Kästner · 2018 · Journal of Chemical Theory and Computation
25. Using Dimensionality Reduction to Analyze Protein Trajectories
    Gareth A. Tribello, Piero Gasparotto · 2019 · Frontiers in Molecular Biosciences
26. Machine Learning for Vibrational Spectroscopic Maps
    Alexei A. Kananenka, Kun Yao, Steven A. Corcelli, James L. Skinner · 2019 · Journal of Chemical Theory and Computation
27. Carbons with Regular Pore Geometry Yield Fundamental Insights into Supercapacitor Charge Storage
    Yifei Michelle Liu, Céline Merlet, Berend Smit · 2019 · ACS Central Science
28. Message Passing Neural Networks for Partial Charge Assignment to Metal–Organic Frameworks
    Ali Raza, Árni Sturluson, Cory M. Simon, Xiaoli Z. Fern · 2020 · The Journal of Physical Chemistry C
29. Development of robust neural-network interatomic potential for molten salt
    Ju Li, Emine Küçükbenli, Stephen Lam, Boris Khaykovich, Efthimios Kaxiras, Ju Li · 2021 · Cell Reports Physical Science
30. BIGDML—Towards accurate quantum machine learning force fields for materials
    Huziel E. Sauceda, Luis E. Gálvez-González, Stefan Chmiela, Lauro Oliver Paz‐Borbón, Klaus‐Robert Müller, Alexandre Tkatchenko · 2022 · Nature Communications
31. MatGPT: A Vane of Materials Informatics from Past, Present, to Future
    Zhilong Wang, An Chen, Kehao Tao, Yanqiang Han, Jinjin Li · 2023 · Advanced Materials
32. Recursive evaluation and iterative contraction of N-body equivariant features
    Jigyasa Nigam, Sergey N. Pozdnyakov, Michele Ceriotti · 2020 · The Journal of Chemical Physics
33. DFT Accurate Interatomic Potential for Molten NaCl from Machine Learning
    Samuel Tovey, Anand Narayanan Krishnamoorthy, Ganesh Sivaraman, Jicheng Guo, Chris J. Benmore, Andreas Heuer, Christian Holm · 2020 · The Journal of Physical Chemistry C
34. High-throughput property-driven generative design of functional organic molecules
    Julia Westermayr, Joe Gilkes, Rhyan Barrett, Reinhard J. Maurer · 2023 · Nature Computational Science
35. Switching of Redox Levels Leads to High Reductive Stability in Water-in-Salt Electrolytes
    Feng Wang, Yan Sun, Jun Jie Cheng · 2023 · Journal of the American Chemical Society
36. Machine-learning driven global optimization of surface adsorbate geometries
    Hyunwook Jung, Lena Sauerland, Sina Stocker, Karsten Reuter, Johannes T. Margraf · 2023 · npj Computational Materials
37. Modeling of Nanomaterials for Supercapacitors: Beyond Carbon Electrodes
    Sheng Bi, Lisanne Knijff, Xiliang Lian, Alicia van Hees, Chao Zhang, Mathieu Salanne · 2024 · ACS Nano
38. i-PI 3.0: A flexible and efficient framework for advanced atomistic simulations
    Yair E. Litman, Venkat Kapil, Yotam M. Y. Feldman, Davide Tisi, Tomislav Begušić, Karen Fidanyan, Guillaume Fraux, Jacob Higer, Matthias Kellner, Tao E. Li et al. · 2024 · The Journal of Chemical Physics
39. Advances and New Challenges to Bimolecular Reaction Dynamics Theory
    Jun Li, Bin Zhao, Daiqian Xie, Hua Guo · 2020 · The Journal of Physical Chemistry Letters
40. Pure non-local machine-learned density functional theory for electron correlation
    Johannes T. Margraf, Karsten Reuter · 2021 · Nature Communications
41. Transferable Neural Network Potential Energy Surfaces for Closed-Shell Organic Molecules: Extension to Ions
    Leif D. Jacobson, James Stevenson, Farhad Ramezanghorbani, Delaram Ghoreishi, Karl Leswing, Edward D. Harder, Robert Abel · 2022 · Journal of Chemical Theory and Computation
42. Integrated Molecular Modeling and Machine Learning for Drug Design
    Song Xia, Eric Chen, Yingkai Zhang · 2023 · Journal of Chemical Theory and Computation
43. Hybrid neural network potential for multilayer graphene
    Mingjian Wen, Ellad B. Tadmor · 2019 · Physical review. B./Physical review. B
44. Applied Machine Learning for Developing Next‐Generation Functional Materials
    Filip Dinic, Kamalpreet Singh, Tony Dong, Milad Rezazadeh, Zhibo Wang, Ali Khosrozadeh, Tiange Yuan, Oleksandr Voznyy · 2021 · Advanced Functional Materials
45. Machine learning force fields for molecular liquids: Ethylene Carbonate/Ethyl Methyl Carbonate binary solvent
    Ioan-Bogdan Magdău, Daniel J. Arismendi‐Arrieta, Holly E. Smith, Clare P. Grey, Kersti Hermansson, Gábor Cśanyi · 2023 · npj Computational Materials
46. Atomic-scale identification of active sites of oxygen reduction nanocatalysts
    Yao Yang, Jihan Zhou, Zipeng Zhao, Geng Sun, Saman Moniri, Colin Ophus, Yongsoo Yang, Ziyang Wei, Yakun Yuan, Cheng Y. Zhu et al. · 2024 · Nature Catalysis
47. Spherical harmonics based descriptor for neural network potentials: Structure and dynamics of Au147 nanocluster
    Shweta Jindal, Siva Chiriki, Satya S. Bulusu · 2017 · The Journal of Chemical Physics
48. Understanding X-ray Spectroscopy of Carbonaceous Materials by Combining Experiments, Density Functional Theory, and Machine Learning. Part II: Quantitative Fitting of Spectra
    Anja Aarva, Volker L. Deringer, Sami Sainio, Tomi T. Laurila, A. Miguel · 2019 · Chemistry of Materials
49. Machine Learning for Molecular Dynamics on Long Timescales
    Frank Noé · 2020 · Lecture notes in physics
50. Learning protein-ligand binding affinity with atomic environment vectors
    Rocco Meli, Andrew Anighoro, Mike J. Bodkin, Garrett M. Morris, Philip Charles Biggin · 2021 · Journal of Cheminformatics
51. Active machine learning model for the dynamic simulation and growth mechanisms of carbon on metal surface
    Di Zhang, Peiyun Yi, Xinmin Lai, Linfa Peng, Hao Li · 2024 · Nature Communications
52. Cartesian atomic cluster expansion for machine learning interatomic potentials
    Bingqing Cheng · 2024 · npj Computational Materials
53. A Universal 3D Voxel Descriptor for Solid-State Material Informatics with Deep Convolutional Neural Networks
    Seiji Kajita, Nobuko Ohba, Ryosuke Jinnouchi, Ryoji Asahi · 2017 · Scientific Reports
54. Atomic Energies from a Convolutional Neural Network
    Xin Chen, Mathias Jo̷rgensen, Jun Li, Bjørk Hammer · 2018 · Journal of Chemical Theory and Computation
55. Machine Learning-Guided Approach for Studying Solvation Environments
    Yasemin Basdogan, Mitchell C. Groenenboom, Ethan Henderson, Sandip De, Susan B. Rempe, John A. Keith · 2019 · Journal of Chemical Theory and Computation
56. Ab initio molecular dynamics and materials design for embedded phase-change memory
    Liang Sun, Yuxing Zhou, Xudong Wang, Yuhan Chen, Volker L. Deringer, Riccardo Mazzarello, Wei Zhang · 2021 · npj Computational Materials
57. Bayesian force fields from active learning for simulation of inter-dimensional transformation of stanene
    Yu Xie, Jonathan Vandermause, Lixin Sun, Andrea Cepellotti, Boris Kozinsky · 2021 · npj Computational Materials
58. Accelerated Atomistic Modeling of Solid-State Battery Materials With Machine Learning
    Haoyue Guo, Qian Wang, Annika Stuke, Alexander Urban, Nongnuch Artrith · 2021 · Frontiers in Energy Research
59. Simple machine-learned interatomic potentials for complex alloys
    Jesper Byggmästar, K. Nordlund, Flyura Djurabekova · 2022 · Physical Review Materials
60. Organic crystal structure prediction and its application to materials design
    Qiang Zhu, Shinnosuke Hattori · 2022 · Journal of materials research/Pratt's guide to venture capital sources
61. JARVIS-Leaderboard: a large scale benchmark of materials design methods
    Kamal Kumar Choudhary, Daniel Wines, Kangming Li, Kevin F. Garrity, Vishu Gupta, Aldo Humberto Romero, Jaron T. Krogel, Kayahan Saritas, Addis Fuhr, Panchapakesan Ganesh et al. · 2024 · npj Computational Materials
62. Impact of inhibition mechanisms, automation, and computational models on the discovery of organic corrosion inhibitors
    David Alan Winkler, Anthony ewart Hughes, Can Özkan, J.M.C. Mol, Tim Würger, Christian Feiler, Dawei Zhang, Sviatlana V. Lamaka · 2024 · Progress in Materials Science
63. Crowd-sourcing materials-science challenges with the NOMAD 2018 Kaggle competition
    Christopher A. Sutton, Luca M. Ghiringhelli, Takenori Yamamoto, Yury V. Lysogorskiy, Lars Blumenthal, Thomas Hammerschmidt, Jacek R. Gołębiowski, Xiang‐Yue Liu, Angelo Ziletti, Matthias Scheffler · 2019 · npj Computational Materials
64. Accurate Computational Prediction of Core-Electron Binding Energies in Carbon-Based Materials: A Machine-Learning Model Combining Density-Functional Theory and GW
    Dorothea Golze, Markus Hirvensalo, Patricia Hernández-León, Anja Aarva, Jarkko Etula, Toma Susi, Patrick Rinke, Tomi T. Laurila, A. Miguel · 2022 · Chemistry of Materials
65. High-Throughput Screening of Gas Sensor Materials for Decomposition Products of Eco-Friendly Insulation Medium by Machine Learning
    Xuhao Wan, Wei Yu, Anyang Wang, Xiting Wang, John Robertson, Zhaofu Zhang, Yuzheng Guo · 2023 · ACS Sensors
66. Navigating at Will on the Water Phase Diagram
    Silvio Pipolo, Mathieu Salanne, Guillaume Ferlat, Stefan Klotz, Antonino Marco Saitta, Fabio Pietrucci · 2017 · Physical Review Letters
67. Frontiers in atomistic simulations of high entropy alloys
    Alberto Ferrari, Biswanath Dutta, Konstantin Gubaev, Yuji Ikeda, Prashanth Srinivasan, Blazej Grabowski, Fritz Körmann · 2020 · Journal of Applied Physics
68. A Perspective on Foundation Models in Chemistry
    Junyoung Choi, Gunwook Nam, Jaesik Choi, Yousung Jung · 2025 · JACS Au
69. Conceptual and practical bases for the high accuracy of machine learning interatomic potentials: Application to elemental titanium
    Akira Takahashi, Atsuto Seko, Isao Tanaka · 2017 · Physical Review Materials
70. Data-driven material models for atomistic simulation
    Mitchell A. Wood, Mary Alice Cusentino, Brian D. Wirth, Aidan P. Thompson · 2019 · Physical review. B./Physical review. B
71. Calculation of phase diagrams in the multithermal-multibaric ensemble
    Pablo M. Piaggi, Michele Parrinello · 2019 · The Journal of Chemical Physics
72. Catalyze Materials Science with Machine Learning
    Jaehyun Kim, Donghoon Kang, Sang‐Bum Kim, Ho Won Jang · 2021 · ACS Materials Letters
73. Thermal conductivity of single-layer MoS2(1−x)Se2x alloys from molecular dynamics simulations with a machine-learning-based interatomic potential
    Xiaokun Gu, Changying Zhao · 2019 · Computational Materials Science
74. Machine learning for the modeling of interfaces in energy storage and conversion materials
    Nongnuch Artrith · 2019 · Journal of Physics Energy
75. Ab initio instanton rate theory made efficient using Gaussian process regression
    Gabriel Laude, Danilo Calderini, David P. Tew, Jeremy O. Richardson · 2018 · Faraday Discussions
76. IrO 2 Surface Complexions Identified through Machine Learning and Surface Investigations
    Jakob Timmermann, Florian Kraushofer, Nikolaus Resch, Peigang Li, Yu Wang, Zhiqiang Q. Mao, Michele Riva, Yonghyuk Lee, Carsten G. Staacke, Michael Schmid et al. · 2020 · Physical Review Letters
77. The Earth Mover’s Distance as a Metric for the Space of Inorganic Compositions
    Cameron J. Hargreaves, Matthew Stephen Dyer, Michael W. Gaultois, Vitaliy A. Kurlin, Matthew J. Rosseinsky · 2020 · Chemistry of Materials
78. Learning Electron Densities in the Condensed Phase
    Alan M. Lewis, Andrea Grisafi, Michele Ceriotti, Mariana Rossi · 2021 · Journal of Chemical Theory and Computation
79. Compressing local atomic neighbourhood descriptors
    James P. Darby, James R. Kermode, Gábor Cśanyi · 2022 · npj Computational Materials
80. Machine-Learning-Based Predictions of Polymer and Postconsumer Recycled Polymer Properties: A Comprehensive Review
    Nagababu Andraju, Greg W. Curtzwiler, Yun Ji, Evguenii I. Kozliak, Prakash Ranganathan · 2022 · ACS Applied Materials & Interfaces
81. Machine learning for perovskite solar cell design
    Zhan Hui, Min Wang, Xiang Qian Yin, Yanan Wang, Yunliang Yue · 2023 · Computational Materials Science
82. Using Gradients in Permutationally Invariant Polynomial Potential Fitting: A Demonstration for CH 4 Using as Few as 100 Configurations
    Apurba Nandi, Chen Qu, Joel M. Bowman · 2019 · Journal of Chemical Theory and Computation
83. Making free-energy calculations routine: Combining first principles with machine learning
    Ryosuke Jinnouchi, Ferenc Karsai, Georg Kresse · 2020 · Physical review. B./Physical review. B
84. Predicting molecular properties with covariant compositional networks
    Truong Son Hy, Shubhendu Trivedi, Horace Pan, Brandon Anderson, Risi Kondor · 2018 · The Journal of Chemical Physics
85. Modelling Bulk Electrolytes and Electrolyte Interfaces with Atomistic Machine Learning
    Yunqi Shao, Lisanne Knijff, Florian M. Dietrich, Kersti Hermansson, Chao Zhang · 2020 · Batteries & Supercaps
86. Machine learning for molecular thermodynamics
    Jiaqi Ding, Nan Xu, Manh Tien Nguyen, Qi Qiao, Yao Shi, Yi He, Qing Shao · 2021 · Chinese Journal of Chemical Engineering
87. Interatomic potentials: achievements and challenges
    Martin H. Müser, Sergey V. Sukhomlinov, Lars Pastewka · 2022 · Advances in Physics X
88. Deep ensembles vs committees for uncertainty estimation in neural-network force fields: Comparison and application to active learning
    Jesús Carrete, Hadrián Montes‐Campos, Ralf Wanzenböck, Esther Heid, Georg K. H. Madsen · 2023 · The Journal of Chemical Physics
89. Decoding Electrochemical Processes of Lithium‐Ion Batteries by Classical Molecular Dynamics Simulations
    Xi Tan, Ming Chen, Jinkai Zhang, Shiqi Li, Huajie Zhang, Long Yang, Tian Shi Sun, Xin Qian, Guang Feng · 2024 · Advanced Energy Materials
90. Explicit Multielement Extension of the Spectral Neighbor Analysis Potential for Chemically Complex Systems
    Mary Alice Cusentino, Mitchell A. Wood, Aidan P. Thompson · 2020 · The Journal of Physical Chemistry A
91. Machine Learning for Predicting Electron Transfer Coupling
    Chun-I Wang, Mac Kevin E. Braza, Gil C. Claudio, Ricky B. Nellas, Chao‐Ping Hsu · 2019 · The Journal of Physical Chemistry A
92. MLatom 2: An Integrative Platform for Atomistic Machine Learning
    Pavlo O. Dral, Fuchun Ge, Bao-Xin Xue, Yi-Fan Hou, Max Pinheiro, Jianxing Huang, Mario Barbatti · 2021 · Topics in Current Chemistry
93. Implications of the BATTERY 2030+ AI‐Assisted Toolkit on Future Low‐TRL Battery Discoveries and Chemistries
    Arghya Bhowmik, Maitane Berecibar, Montse Casas‐Cabanas, Gábor Cśanyi, Robert Dominko, Kersti Hermansson, M. Rosa Palacín, Helge Sören Stein, Tejs Vegge · 2021 · Advanced Energy Materials
94. The design and optimization of heterogeneous catalysts using computational methods
    Shambhawi Shambhawi, Ojus Mohan, Tej Salil Choksi, Alexei A. Lapkin · 2023 · Catalysis Science & Technology
95. Advances in natural fiber polymer and PLA composites through artificial intelligence and machine learning integration
    Md. Helal Uddin, Mohammed Huzaifa Mulla, Tarek Abedin, Abreeza Manap, Boon Kar Yap, Reji Kumar Rajamony, Kiran Shahapurkar, T. M. Yunus Khan, Manzoore Elahi M. Soudagar, Mohammad Nur‐E‐Alam · 2025 · Journal of Polymer Research
96. Machine learning in materials design: Algorithm and application*
    Song, Zhilong, Chen, Xiwen, Meng, Fanbin, Cheng, Guanjian, Wang, Chen, Sun, Zhongti, Yin, Wan-Jian · 2020 · Chinese Physics B
97. Generalized Density-Functional Tight-Binding Repulsive Potentials from Unsupervised Machine Learning
    Kranz, Julian J., Kubillus, Maximilian, Ramakrishnan, Raghunathan, von Lilienfeld, O. Anatole, Elstner, Marcus · 2018 · Journal of Chemical Theory and Computation
98. First-Principles Atomistic Thermodynamics and Configurational Entropy
    Christopher A. Sutton, Sergey V. Levchenko · 2020 · Frontiers in Chemistry
99. Active learning and neural network potentials accelerate molecular screening of ether-based solvate ionic liquids
    Wujie Wang, Tzuhsiung Yang, William H. Harris, Rafael Gómez‐Bombarelli · 2020 · Chemical Communications
100. High-dimensional potential energy surfaces for molecular simulations: from empiricism to machine learning
     Oliver T. Unke, Debasish Koner, Sarbani Patra, Silvan Käser, Markus Meuwly · 2020 · Machine Learning Science and Technology