On representing chemical environments

Cited by (1,001)

Items 301–400 of 1,001

1. Uncertainty quantification in molecular simulations with dropout neural network potentials
   Mingjian Wen, Ellad B. Tadmor · 2020 · npj Computational Materials
2. Wavelet Scattering Regression of Quantum Chemical Energies
   Matthew Hirn, Stéphane Mallat, Nicolas Poilvert · 2017 · Multiscale Modeling and Simulation
3. Physically Motivated Recursively Embedded Atom Neural Networks: Incorporating Local Completeness and Nonlocality
   Yaolong Zhang, Junfan Xia, Bin Jiang · 2021 · Physical Review Letters
4. Computational Approaches for Organic Semiconductors: From Chemical and Physical Understanding to Predicting New Materials
   Vinayak Bhat, Connor P. Callaway, Chad M. Risko · 2023 · Chemical Reviews
5. Updates to the DScribe library: New descriptors and derivatives
   Jarno Laakso, Lauri Himanen, Henrietta Homm, Eiaki V. Morooka, Marc O. J. Jäger, Milica Todorović, Patrick Rinke · 2023 · The Journal of Chemical Physics
6. The role of dynamics in heterogeneous catalysis: Surface diffusivity and N 2 decomposition on Fe(111)
   Luigi Bonati, Daniela Polino, Cristina Pizzolitto, Pierdomenico Biasi, René Eckert, Stephan J. Reitmeier, Robert Schlögl, Michele Parrinello · 2023 · Proceedings of the National Academy of Sciences
7. First-principles study of alkali-metal intercalation in disordered carbon anode materials
   Jianxing Huang, Gábor Cśanyi, Jinbao Zhao, Jun Jie Cheng, Volker L. Deringer · 2019 · Journal of Materials Chemistry A
8. Thermal conductivity modeling using machine learning potentials: application to crystalline and amorphous silicon
   Xin Qian, Shiyu Peng, Xiaobo Li, Yujie Wei, Ronggui Yang · 2019 · Materials Today Physics
9. Evolving the Materials Genome: How Machine Learning Is Fueling the Next Generation of Materials Discovery
   Changwon Suh, Clyde Fare, James A. Warren, Edward O. Pyzer‐Knapp · 2020 · Annual Review of Materials Research
10. Molecular representations for machine learning applications in chemistry
    Shampa Raghunathan, U. Deva Priyakumar · 2021 · International Journal of Quantum Chemistry
11. Latent Ewald summation for machine learning of long-range interactions
    Bingqing Cheng · 2025 · npj Computational Materials
12. Sparse representation for a potential energy surface
    Atsuto Seko, Akira Takahashi, Isao Tanaka · 2014 · Physical Review B
13. Assessing Gaussian Process Regression and Permutationally Invariant Polynomial Approaches To Represent High-Dimensional Potential Energy Surfaces
    Chen Qu, Qi Yu, Brian L. Van Hoozen, Joel M. Bowman, Rodrigo A. Vargas–Hernández · 2018 · Journal of Chemical Theory and Computation
14. Understanding X-ray Spectroscopy of Carbonaceous Materials by Combining Experiments, Density Functional Theory, and Machine Learning. Part I: Fingerprint Spectra
    Anja Aarva, Volker L. Deringer, Sami Sainio, Tomi T. Laurila, A. Miguel · 2019 · Chemistry of Materials
15. From Data to Discovery: Recent Trends of Machine Learning in Metal–Organic Frameworks
    Junkil Park, Honghui Kim, Yeonghun Kang, Yunsung Lim, Jihan Kim · 2024 · JACS Au
16. Machine learning determination of atomic dynamics at grain boundaries
    Tristan A. Sharp, Spencer L. Thomas, Ekin D. Cubuk, Samuel S. Schoenholz, David J. Srolovitz, Andrea J. Liu · 2018 · Proceedings of the National Academy of Sciences
17. graphDelta: MPNN Scoring Function for the Affinity Prediction of Protein–Ligand Complexes
    Dmitry S. Karlov, Sergey Sosnin, Maxim V. Fedorov, Petr A. Popov · 2020 · ACS Omega
18. Universal Machine Learning Interatomic Potentials: Surveying Solid Electrolytes
    Amir Hajibabaei, Kwang S. Kim · 2021 · The Journal of Physical Chemistry Letters
19. Machine-learned approximations to Density Functional Theory Hamiltonians
    Ganesh Shridhar Hegde, R. Chris Bowen · 2017 · Scientific Reports
20. A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses
    Qi S. Wang, Anubhav Jain · 2019 · Nature Communications
21. Machine learning interatomic potential developed for molecular simulations on thermal properties of β-Ga2O3
    Yuanbin Liu, Jia‐Yue Yang, Gongming Xin, Linhua Liu, Gábor Cśanyi, Bing Cao · 2020 · The Journal of Chemical Physics
22. Graph neural network predictions of metal organic framework CO 2 adsorption properties
    Kamal Kumar Choudhary, Taner Yildirim, Daniel W. Siderius, Aaron Gilad Kusne, Austin McDannald, Diana L. Ortiz‐Montalvo · 2022 · Computational Materials Science
23. A machine-learned interatomic potential for silica and its relation to empirical models
    Linus C. Erhard, Jochen Rohrer, Karsten Albe, Volker L. Deringer · 2022 · npj Computational Materials
24. Understanding the thermal properties of amorphous solids using machine-learning-based interatomic potentials
    Gabriele Cesare Sosso, Volker L. Deringer, Stephen R. Elliott, Gábor Cśanyi · 2018 · Molecular Simulation
25. Transfer learning for materials informatics using crystal graph convolutional neural network
    Joohwi Lee, Ryoji Asahi · 2021 · Computational Materials Science
26. Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials
    Viktor Zaverkin, Johannes Kästner · 2020 · Journal of Chemical Theory and Computation
27. Structure and Pore Size Distribution in Nanoporous Carbon
    Yanzhou Wang, Zheyong Fan, Ping Qian, Tapio Ala-Nissilä, A. Miguel · 2022 · Chemistry of Materials
28. ChIMES: A Force Matched Potential with Explicit Three-Body Interactions for Molten Carbon
    Rebecca Lindsey, Laurence E. Fried, Nir Goldman · 2017 · Journal of Chemical Theory and Computation
29. Computational Surface Chemistry of Tetrahedral Amorphous Carbon by Combining Machine Learning and Density Functional Theory
    Volker L. Deringer, A. Miguel, Richard Jana, Anja Aarva, Stephen R. Elliott, Tomi T. Laurila, Gábor Cśanyi, Lars Pastewka · 2018 · Chemistry of Materials
30. Regularised atomic body-ordered permutation-invariant polynomials for the construction of interatomic potentials
    Cas van der Oord, Geneviève Dusson, Gábor Cśanyi, Christoph Ortner · 2020 · Machine Learning Science and Technology
31. Gaussian approximation potentials for body-centered-cubic transition metals
    J. Byggmästar, K. Nordlund, Flyura Djurabekova · 2020 · Physical Review Materials
32. Transferable equivariant graph neural networks for the Hamiltonians of molecules and solids
    Yang Zhong, Hongyu Yu, Mao Su, Xin-Gao Gong, Hongjun Xiang · 2023 · npj Computational Materials
33. Descriptors for Machine Learning of Materials Data
    Atsuto Seko, Atsushi Togo, Isao Tanaka · 2018 · Springer eBooks
34. Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures
    So Fujikake, Volker L. Deringer, Tae Hoon Lee, Marcin Krynski, Stephen R. Elliott, Gábor Cśanyi · 2018 · The Journal of Chemical Physics
35. Strategies for the construction of machine-learning potentials for accurate and efficient atomic-scale simulations
    April M Miksch, Tobias Morawietz, Johannes Kästner, Alexander Urban, Nongnuch Artrith · 2021 · Machine Learning Science and Technology
36. Oxidation of metallic Cu by supercritical CO2 and control synthesis of amorphous nano-metal catalysts for CO2 electroreduction
    Chunjun Chen, Xupeng Yan, Yahui Wu, Xiudong Zhang, Shoujie Liu, Fanyu Zhang, Xiaofu Sun, Qinggong Zhu, Lirong Zheng, Jing Zhang et al. · 2023 · Nature Communications
37. Graph Atomic Cluster Expansion for Semilocal Interactions beyond Equivariant Message Passing
    Anton S. Bochkarev, Yury V. Lysogorskiy, Ralf Drautz · 2024 · Physical Review X
38. Application of machine learning methods for predicting new superhard materials
    Efim A. Mazhnik, Артем Ромаевич Оганов · 2020 · Journal of Applied Physics
39. Deep learning of accurate force field of ferroelectric HfO 2
    Jing Wu, Yuzhi Zhang, Linfeng Zhang, Shi Liu · 2021 · Physical review. B./Physical review. B
40. Revealing the Oxygen Reduction/Evolution Reaction Activity Origin of Carbon-Nitride-Related Single-Atom Catalysts: Quantum Chemistry in Artificial Intelligence
    Xuhao Wan, Wei Yu, Huan Niu, Xiting Wang, Zhaofu Zhang, Yuzheng Guo · 2022 · Chemical Engineering Journal
41. The seventh blind test of crystal structure prediction: structure generation methods
    Lily M. Hunnisett, Jonas Nyman, Nicholas F. Francia, Nathan S. Abraham, Claire S. Adjiman, Srinivasulu Aitipamula, Tamador Alkhidir, Mubarak Almehairbi, Andrea Anelli, Dylan M. Anstine et al. · 2024 · Acta Crystallographica Section B Structural Science Crystal Engineering and Materials
42. Analyzing machine learning models to accelerate generation of fundamental materials insights
    Umehara, Mitsutaro, Stein, Helge S., Guevarra, Dan, Newhouse, Paul F., Boyd, David A., Gregoire, John M. · 2019 · npj Computational Materials
43. Liquid water contains the building blocks of diverse ice phases
    Bartomeu Monserrat, Jan Gerit Brandenburg, Edgar A. Engel, Bingqing Cheng · 2020 · Nature Communications
44. High-dimensional neural network potentials for magnetic systems using spin-dependent atom-centered symmetry functions
    Marco Eckhoff, Jörg Behler · 2021 · npj Computational Materials
45. Perspectives on multiscale modelling and experiments to accelerate materials development for fusion
    Mark R. Gilbert, Kazuto Arakawa, Zachary Bergstrom, M.J. Caturla, S. L. Dudarev, Fei Gao, Alexandra M. Goryaeva, Shenyang Hu, Xunxiang Hu, Richard J. Kurtz et al. · 2021 · Journal of Nuclear Materials
46. Modelling of Environmental Ageing of Polymers and Polymer Composites—Modular and Multiscale Methods
    Andrey E. Krauklis, Christian W. Karl, I.B.C.M. Rocha, Juris Burlakovs, Rūta Ozola-Davidāne, Abedin I. Gagani, Olesja Starkova · 2022 · Polymers
47. Deep machine learning unravels the structural origin of mid‐gap states in chalcogenide glass for high‐density memory integration
    Meng Kai Xu, Ming Xu, Xiangshui Miao · 2022 · InfoMat
48. Atomic Cluster Expansion for Quantum-Accurate Large-Scale Simulations of Carbon
    Minaam Qamar, Matous Mrovec, Yury V. Lysogorskiy, Anton S. Bochkarev, Ralf Drautz · 2023 · Journal of Chemical Theory and Computation
49. Machine Learning of Reactive Potentials
    Yinuo Yang, Shuhao Zhang, Kavindri D. Ranasinghe, Olexandr Isayev, Adrián E. Roitberg · 2024 · Annual Review of Physical Chemistry
50. Modeling the Solid Electrolyte Interphase: Machine Learning as a Game Changer?
    D. Diddens, Williams Agyei Appiah, Y. Mabrouk, A. Heuer, T. Vegge, Arghya Bhowmik · 2022 · Advanced Materials Interfaces
51. Computational design of magnetic molecules and their environment using quantum chemistry, machine learning and multiscale simulations
    Alessandro Lunghi, Stefano Sanvito · 2022 · Nature Reviews Chemistry
52. A reactive, scalable, and transferable model for molecular energies from a neural network approach based on local information
    Oliver T. Unke, Markus Meuwly · 2018 · The Journal of Chemical Physics
53. Data-driven learning and prediction of inorganic crystal structures
    Volker L. Deringer, Davide Maria Proserpio, Gábor Cśanyi, Chris J. Pickard · 2018 · Faraday Discussions
54. Training Neural Nets To Learn Reactive Potential Energy Surfaces Using Interactive Quantum Chemistry in Virtual Reality
    Silvia Amabilino, Lars Andersen Bratholm, Simon J. Bennie, Alain C. Vaucher, Markus Reiher, David R. Glowacki · 2019 · The Journal of Physical Chemistry A
55. The NOMAD Artificial-Intelligence Toolkit: turning materials-science data into knowledge and understanding
    Luigi Sbailò, Ádám Fekete, Luca M. Ghiringhelli, Matthias Scheffler · 2022 · npj Computational Materials
56. A transferable active-learning strategy for reactive molecular force fields
    Tom A. Young, Tristan Johnston-Wood, Volker L. Deringer, Fernanda Duarte · 2021 · Chemical Science
57. Encoding the atomic structure for machine learning in materials science
    Shunning Li, Yuanji Liu, Dong Chen, Yi Jiang, Zhiwei Nie, Feng Pan · 2021 · Wiley Interdisciplinary Reviews Computational Molecular Science
58. Ultra-fast interpretable machine-learning potentials
    Stephen Xie, Matthias Rupp, Richard G. Hennig · 2023 · npj Computational Materials
59. Solid harmonic wavelet scattering for predictions of molecule properties
    Michael Eickenberg, Georgios Exarchakis, Matthew Hirn, Stéphane Mallat, Louis Thiry · 2018 · The Journal of Chemical Physics
60. Machine learning for metallurgy I. A neural-network potential for Al-Cu
    Daniel Marchand, Abhinav Jain, Albert Glensk, W.A. Curtin · 2020 · Physical Review Materials
61. Modelling chemical processes in explicit solvents with machine learning potentials
    Hanwen Zhang, Veronika Jurásková, Fernanda Duarte · 2024 · Nature Communications
62. Chemical diversity in molecular orbital energy predictions with kernel ridge regression
    Annika Stuke, Milica Todorović, Matthias Rupp, Christian Künkel, Kunal Ghosh, Lauri Himanen, Patrick Rinke · 2019 · The Journal of Chemical Physics
63. Band Gap Prediction for Large Organic Crystal Structures with Machine Learning
    Bart Olsthoorn, R. Matthias Geilhufe, Stanislav S. Borysov, Alexander V. Balatsky · 2019 · Advanced Quantum Technologies
64. Density functional theory based neural network force fields from energy decompositions
    Yufeng Huang, Jun Yong Kang, William A. Goddard, Lin‐Wang Wang · 2019 · Physical review. B./Physical review. B
65. Machine-learning-based interatomic potential for phonon transport in perfect crystalline Si and crystalline Si with vacancies
    Hasan Babaei, Ruiqiang Guo, Amirreza Hashemi, Sangyeop Lee · 2019 · Physical Review Materials
66. Incorporating Electronic Information into Machine Learning Potential Energy Surfaces via Approaching the Ground-State Electronic Energy as a Function of Atom-Based Electronic Populations
    Xiaowei Xie, Kristin Aslaug Persson, David W. Small · 2020 · Journal of Chemical Theory and Computation
67. Accurate energy barriers for catalytic reaction pathways: an automatic training protocol for machine learning force fields
    Lars L. Schaaf, Edvin Fako, Sandip De, Ansgar Schäfer, Gábor Cśanyi · 2023 · npj Computational Materials
68. High-throughput and data-driven machine learning techniques for discovering high-entropy alloys
    Lu Zhichao, Dong Ma, Xiongjun Liu, Zhaoping Lü · 2024 · Communications Materials
69. First-principles interatomic potentials for ten elemental metals via compressed sensing
    Atsuto Seko, Akira Takahashi, Isao Tanaka · 2015 · Physical Review B
70. Quantitative prediction of grain boundary thermal conductivities from local atomic environments
    Susumu Fujii, Tatsuya Yokoi, Craig A. J. Fisher, Hiroki Moriwake, Masato Yoshiya · 2020 · Nature Communications
71. A Deep-Learning Potential for Crystalline and Amorphous Li–Si Alloys
    Nan Xu, Yao Shi, Yi He, Qing Shao · 2020 · The Journal of Physical Chemistry C
72. Efficient and Accurate Simulations of Vibrational and Electronic Spectra with Symmetry-Preserving Neural Network Models for Tensorial Properties
    Yaolong Zhang, Sheng Ye, Jinxiao Zhang, Ce Hu, Jun Jiang, Bin Jiang · 2020 · The Journal of Physical Chemistry B
73. Machine learning activation energies of chemical reactions
    Toby Lewis‐Atwell, Piers A. Townsend, Matthew N. Grayson · 2021 · Wiley Interdisciplinary Reviews Computational Molecular Science
74. How robust are modern graph neural network potentials in long and hot molecular dynamics simulations?
    Sina Stocker, Johannes Gasteiger, Florian Becker, Stephan Günnemann, Johannes T. Margraf · 2022 · Machine Learning Science and Technology
75. High‐Throughput Screening of Electrocatalysts for Nitrogen Reduction Reactions Accelerated by Interpretable Intrinsic Descriptor
    Xiaoyun Lin, Yongtao Wang, Xin Chang, Shiyu Zhen, Zhi‐Jian Zhao, Jinlong Gong · 2023 · Angewandte Chemie International Edition
76. Construction of High Accuracy Machine Learning Interatomic Potential for Surface/Interface of Nanomaterials—A Review
    Kaiwei Wan, Jianxin He, Xinghua Shi · 2023 · Advanced Materials
77. In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back
    Abdulrahman Aldossary, Jorge A. Campos-Gonzalez-Angulo, Sergio Pablo‐García, Shi Xuan Leong, Ella Miray Rajaonson, Luca Thiede, Gary Tom, Andrew Wang, Davide Avagliano, Alán Aspuru‐Guzik · 2024 · Advanced Materials
78. Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications
    Tobias Morawietz, Nongnuch Artrith · 2020 · Journal of Computer-Aided Molecular Design
79. Sparse Gaussian process potentials: Application to lithium diffusivity in superionic conducting solid electrolytes
    Amir Hajibabaei, Chang Woo Myung, Kwang S. Kim · 2021 · Physical review. B./Physical review. B
80. Thermodynamics and dielectric response of BaTiO3 by data-driven modeling
    Lorenzo Gigli, Max Veit, Michele Kotiuga, Giovanni Pizzi, Nicola Marzari, Michele Ceriotti · 2022 · npj Computational Materials
81. A multi-fidelity machine learning approach to high throughput materials screening
    Clyde Fare, Peter Fenner, Matt Benatan, Alessandro Varsi, Edward O. Pyzer‐Knapp · 2022 · npj Computational Materials
82. Gaussian approximation potentials: Theory, software implementation and application examples
    Sascha Klawohn, James P. Darby, James R. Kermode, Gábor Cśanyi, A. Miguel, Albert P. Bartók · 2023 · The Journal of Chemical Physics
83. Deep learning generative model for crystal structure prediction
    Xiaoshan Luo, Zhenyu Wang, Pengyue Gao, Jian Lv, Yanchao Wang, Changfeng Chen, Yanming Ma · 2024 · npj Computational Materials
84. A Euclidean transformer for fast and stable machine learned force fields
    J. Thorben Frank, Oliver T. Unke, Klaus‐Robert Müller, Stefan Chmiela · 2024 · Nature Communications
85. Using Gaussian process regression to simulate the vibrational Raman spectra of molecular crystals
    Nathaniel Raimbault, Andrea Grisafi, Michele Ceriotti, Mariana Rossi · 2019 · New Journal of Physics
86. Deep learning inter-atomic potential model for accurate irradiation damage simulations
    Hao Wang, Xun Guo, Linfeng Zhang, Han Wang, Jianming Xue · 2019 · Applied Physics Letters
87. Living supramolecular polymerization of fluorinated cyclohexanes
    Oleksandr Shyshov, Shyamkumar V. Haridas, Luca Pesce, Haoyuan Qi, Andrea Gardin, Davide Bochicchio, Ute A. Kaiser, Giovanni Maria Pavan, Max von Delius · 2021 · Nature Communications
88. Learning Grain-Boundary Segregation: From First Principles to Polycrystals
    Malik Wagih, Christopher Allan Schuh · 2022 · Physical Review Letters
89. How to train a neural network potential
    Alea Miako Tokita, Jörg Behler · 2023 · The Journal of Chemical Physics
90. MXenes and its composite structures: synthesis, properties, applications, 3D/4D printing, and artificial intelligence; machine learning integration
    Vimukthi Dananjaya, N. Hansika, Sathish Marimuthu, Venkata Chevali, Y. Mishra, A. Grace, Nisa V. Salim, C. Abeykoon · 2025 · Progress in Materials Science
91. Stratified construction of neural network based interatomic models for multicomponent materials
    Samad Hajinazar, Junping Shao, Aleksey N. Kolmogorov · 2017 · Physical review. B./Physical review. B
92. Hierarchical visualization of materials space with graph convolutional neural networks
    Tian Xie, Jeffrey C. Grossman · 2018 · The Journal of Chemical Physics
93. PiNN: A Python Library for Building Atomic Neural Networks of Molecules and Materials
    Yunqi Shao, Matti Hellström, Pavlin Dakev Mitev, Lisanne Knijff, Chao Zhang · 2020 · Journal of Chemical Information and Modeling
94. Machine learning for metallurgy II. A neural-network potential for magnesium
    Markus Stricker, Binglun Yin, Eleanor Mak, W.A. Curtin · 2020 · Physical Review Materials
95. The origin of the lattice thermal conductivity enhancement at the ferroelectric phase transition in GeTe
    Đorđe Dangić, Olle Hellman, Stephen Fahy, Ivana Savić · 2021 · npj Computational Materials
96. Complex Ga2O3 polymorphs explored by accurate and general-purpose machine-learning interatomic potentials
    J. Zhao, Jesper Byggmästar, Huan He, K. Nordlund, Flyura Djurabekova, Mengyuan Hua · 2023 · npj Computational Materials
97. Perspective: Atomistic simulations of water and aqueous systems with machine learning potentials
    Amir Omranpour, Pablo Montero de Hijes, Jörg Behler, Christoph Dellago · 2024 · The Journal of Chemical Physics
98. Universal machine learning interatomic potentials are ready for phonons
    Antoine Loew, D. Y. Sun, Hai‐Chen Wang, Silvana Botti, Miguel A. L. Marques · 2025 · npj Computational Materials
99. The Materials Simulation Toolkit for Machine learning (MAST-ML): An automated open source toolkit to accelerate data-driven materials research
    Ryan M. Jacobs, Tam Mayeshiba, Ben Afflerbach, Luke Harold Miles, Max Williams, Matthew Turner, Raphael A. Finkel, Dane D. Morgan · 2020 · Computational Materials Science
100. Machine learning driven simulated deposition of carbon films: From low-density to diamondlike amorphous carbon
     A. Miguel, Gábor Cśanyi, Tomi T. Laurila, Volker L. Deringer · 2020 · Physical review. B./Physical review. B