On representing chemical environments

Cited by (1,001)

Items 701–800 of 1,001

1. A Hybrid Machine Learning Approach for Structure Stability Prediction in Molecular Co-crystal Screenings
   Simon Wengert, Gábor Cśanyi, Karsten Reuter, Johannes T. Margraf · 2022 · Journal of Chemical Theory and Computation
2. Machine-learning potentials for crystal defects
   Rodrigo Moura Freitas, Yifan Cao · 2022 · MRS Communications
3. Atomistic global optimization X: A Python package for optimization of atomistic structures
   Mads-Peter Verner Christiansen, Nikolaj Rønne, Bjørk Hammer · 2022 · The Journal of Chemical Physics
4. Challenges and opportunities in atomistic simulations of glasses: a review
   Han Liu, Zhangji Zhao, Qi Zhou, Ruoxia Chen, Kai Yang, Zhe Wang, Longwen Tang, Mathieu Bauchy · 2022 · Comptes Rendus Géoscience
5. Machine learning for semiconductors
   Duanyang Liu, Liming Xu, Xu-Min Lin, Xing Wei, Wenjie Yu, Yan Wang, Zhongming Wei · 2022 · Chip
6. Developments and further applications of ephemeral data derived potentials
   Pascal T. Salzbrenner, Se Hun Joo, Lewis J. Conway, Peter I. C. Cooke, Bonan Zhu, Milosz P. Matraszek, William C. Witt, Chris J. Pickard · 2023 · The Journal of Chemical Physics
7. Neural network kinetics for exploring diffusion multiplicity and chemical ordering in compositionally complex materials
   Bin Xing, Timothy J. Rupert, Xiaoqing Q. Pan, Penghui Cao · 2024 · Nature Communications
8. Applications of machine‐learning interatomic potentials for modeling ceramics, glass, and electrolytes: A review
   Shingo Urata, Marco Bertani, Alfonso Pedone · 2024 · Journal of the American Ceramic Society
9. Uncertainty quantification by direct propagation of shallow ensembles
   Matthias Kellner, Michele Ceriotti · 2024 · Machine Learning Science and Technology
10. Unsupervised landmark analysis for jump detection in molecular dynamics simulations
    Leonid Kahle, Albert Musaelian, Nicola Marzari, Boris Kozinsky · 2019 · Physical Review Materials
11. Partitioning of sulfur between solid and liquid iron under Earth’s core conditions: Constraints from atomistic simulations with machine learning potentials
    Zhigang Zhang, Gábor Cśanyi, Dario Alfè · 2020 · Geochimica et Cosmochimica Acta
12. Controlling Exchange Pathways in Dynamic Supramolecular Polymers by Controlling Defects
    Anna Lucia de Marco, Davide Bochicchio, Andrea Gardin, Giovanni Doni, Giovanni Maria Pavan · 2021 · ACS Nano
13. All-Atom Nonadiabatic Dynamics Simulation of Hybrid Graphene Nanoribbons Based on Wannier Analysis and Machine Learning
    Zedong Wang, Jiawei Dong, Jing Qiu, Linjun Wang · 2022 · ACS Applied Materials & Interfaces
14. Big Data-Driven Materials Science and Its FAIR Data Infrastructure
    Claudia Draxl, Matthias Scheffler · 2020 · Springer eBooks
15. Atomistic neural network representations for chemical dynamics simulations of molecular, condensed phase, and interfacial systems: Efficiency, representability, and generalization
    Yaolong Zhang, Qidong Lin, Bin Jiang · 2022 · Wiley Interdisciplinary Reviews Computational Molecular Science
16. Concurrent prediction of metallic glasses’ global energy and internal structural heterogeneity by interpretable machine learning
    Chaoyi Liu, Yuchu Wang, Yuchi Wang, Minhazul Islam, Jinwoo Hwang, Yunzhi Wang, Yue Fan · 2023 · Acta Materialia
17. Atomistic Simulations and Machine Learning of Solute Grain Boundary Segregation in Mg Alloys at Finite Temperatures
    Vaidehi Menon, Sambit Kumar Das, Vikram Gavini, Liang Qi · 2023 · Acta Materialia
18. E(n)-Equivariant cartesian tensor message passing interatomic potential
    Junjie Wang, Yong Wang, Haoting Zhang, Ziyang Yang, Zhixin Liang, Jiuyang Shi, Hui‐Tian Wang, Dingyu Xing, Jian Sun · 2024 · Nature Communications
19. Recent machine learning-driven investigations into high entropy alloys: A comprehensive review
    Yonggang Yan, Xunxiang Hu, Yalin Liao, Yanyao Zhou, Wenhao He, Ting Zhou · 2024 · Journal of Alloys and Compounds
20. Artificial intelligence in materials science and engineering: Current landscape, key challenges, and future trajectories
    Iman Peivaste, Salim Belouettar, Francesco Mercuri, Nicholas Fantuzzi, Hamidreza Dehghani, Razie Izadi, Halliru Ibrahim, Jakub Lengiewicz, Mael Belouettar-Mathis, Kouider Bendine et al. · 2025 · Composite Structures
21. Thermally-nucleated self-assembly of water and alcohol into stable structures at hydrophobic interfaces
    Kislon Voı̈tchovsky, Daniele Giofré, Juan José Segura, Francesco Stellacci, Michele Ceriotti · 2016 · Nature Communications
22. Understanding grain boundaries – The role of crystallography, structural descriptors and machine learning
    Srikanth Patala · 2019 · Computational Materials Science
23. Machine learning-aided analysis for complex local structure of liquid crystal polymers
    Hideo Doi, Kazuaki Z. Takahashi, Kenji Tagashira, Jun‐ichi Fukuda, Takeshi Aoyagi · 2019 · Scientific Reports
24. Permutationally invariant polynomial potential energy surfaces for tropolone and H and D atom tunneling dynamics
    Paul L. Houston, Riccardo Conte, Chen Qu, Joel M. Bowman · 2020 · The Journal of Chemical Physics
25. Machine learning potentials for multicomponent systems: The Ti-Al binary system
    Atsuto Seko · 2020 · Physical review. B./Physical review. B
26. Global optimization of atomic structures with gradient-enhanced Gaussian process regression
    Sami Kaappa, Estefanía Garijo del Río, Karsten W. Jacobsen · 2021 · Physical review. B./Physical review. B
27. Ab initio random structure searching for battery cathode materials
    Ziheng Lu, Bonan Zhu, Benjamin W. B. Shires, David O. Scanlon, Chris J. Pickard · 2021 · The Journal of Chemical Physics
28. Machine learning for metallurgy III: A neural network potential for Al-Mg-Si
    Abhinav Jain, Daniel Marchand, Albert Glensk, Michele Ceriotti, W. A. Curtin · 2021 · Physical Review Materials
29. Predicting Molecular Energy Using Force-Field Optimized Geometries and Atomic Vector Representations Learned from an Improved Deep Tensor Neural Network
    Jianing Lu, Cheng Wang, Yingkai Zhang · 2019 · Journal of Chemical Theory and Computation
30. Accelerated prediction of atomically precise cluster structures using on-the-fly machine learning
    Yunzhe Wang, Shanping Liu, Peter Lile, Sam Walton Norwood, Alberto Hernández, Sukriti Manna, Tim Mueller · 2022 · npj Computational Materials
31. Symmetry-adapted modeling for molecules and crystals
    Hiroaki Kusunose, Rikuto Oiwa, Satoru Hayami · 2023 · Physical review. B./Physical review. B
32. Physics-Inspired Equivariant Descriptors of Nonbonded Interactions
    Kevin K. Huguenin-Dumittan, Philip Loche, Ni Haoran, Michele Ceriotti · 2023 · The Journal of Physical Chemistry Letters
33. Machine learning-aided first-principles calculations of redox potentials
    Ryosuke Jinnouchi, Ferenc Karsai, Georg Kresse · 2024 · npj Computational Materials
34. Quantum-accurate machine learning potentials for metal-organic frameworks using temperature driven active learning
    Abhishek Sharma, Stefano Sanvito · 2024 · npj Computational Materials
35. AI‐Driven Defect Engineering for Advanced Thermoelectric Materials
    Chenguang Fu, Mouyang Cheng, Nguyen Tuan Hung, Eunbi Rha, Zhantao Chen, Ryotaro Okabe, Denisse Córdova Carrizales, Manasi Mandal, Yongqiang Q. Cheng, Mingda Li · 2025 · Advanced Materials
36. Building and deploying a cyberinfrastructure for the data-driven design of chemical systems and the exploration of chemical space
    Johannes Hachmann, Mohammad Atif Faiz Afzal, Mojtaba Haghighatlari, Yudhajit Pal · 2018 · Molecular Simulation
37. Machine learning enhanced global optimization by clustering local environments to enable bundled atomic energies
    Søren Ager Meldgaard, Esben L. Kolsbjerg, Bjørk Hammer · 2018 · The Journal of Chemical Physics
38. Continuous and optimally complete description of chemical environments using Spherical Bessel descriptors
    Emir Kocer, Jeremy K. Mason, Hakan Ertürk · 2020 · AIP Advances
39. Gaussian Process Regression for Minimum Energy Path Optimization and Transition State Search
    Alexander Denzel, Bernard Haasdonk, Johannes Kästner · 2019 · The Journal of Physical Chemistry A
40. Sensitivity and dimensionality of atomic environment representations used for machine learning interatomic potentials
    Berk Onat, Christoph Ortner, James R. Kermode · 2020 · The Journal of Chemical Physics
41. Building quantum mechanics quality force fields of proteins with the generalized energy-based fragmentation approach and machine learning
    Zheng Cheng, Jiahui Du, Lei Zhang, Jing Ma, Wei Li, Shuhua Li · 2021 · Physical Chemistry Chemical Physics
42. Machine learning for metallurgy IV: A neural network potential for Al-Cu-Mg and Al-Cu-Mg-Zn
    Daniel Marchand, W. A. Curtin · 2022 · Physical Review Materials
43. Tailoring Mid‐Gap States of Chalcogenide Glass by Pressure‐Induced Hypervalent Bonding Towards the Design of Electrical Switching Materials
    Meng Kai Xu, Qundao Xu, Rongchuan Gu, Songyou Wang, Cai‐Zhuang Wang, Kai‐Ming Ho, Zhongrui Wang, Ming Xu, Xiangshui Miao · 2023 · Advanced Functional Materials
44. Layer-by-layer phase transformation in Ti3O5 revealed by machine-learning molecular dynamics simulations
    Mingfeng Liu, Jian-Tao Wang, Junwei Hu, Peitao Liu, Haiyang Niu, Xuexi Yan, Jiangxu Li, Haile Yan, Bo Yang, Yan Sun et al. · 2024 · Nature Communications
45. Designing semiconductor materials and devices in the post-Moore era by tackling computational challenges with data-driven strategies
    Jiahao Xie, Yansong Zhou, Muhammad Faizan, Zewei Li, Tianshu Li, Yuhao Fu, Xinjiang Wang, Lijun Zhang · 2024 · Nature Computational Science
46. Quantifying and connecting atomic and crystallographic grain boundary structure using local environment representation and dimensionality reduction techniques
    Jonathan L. Priedeman, Conrad W. Rosenbrock, Oliver K. Johnson, Eric R. Homer · 2018 · Acta Materialia
47. Comparison of different machine learning models for the prediction of forces in copper and silicon dioxide
    Wenwen Li, Yasunobu Ando · 2018 · Physical Chemistry Chemical Physics
48. Geometric landscapes for material discovery within energy–structure–function maps
    Seyed Mohamad Moosavi, Henglu Xu, Linjiang Chen, Andrew Ian Cooper, Berend Smit · 2020 · Chemical Science
49. Neural-network interatomic potential for grain boundary structures and their energetics in silicon
    Tatsuya Yokoi, Yusuke Noda, Atsutomo Nakamura, Katsuyuki Matsunaga · 2020 · Physical Review Materials
50. PyXtal_FF: a python library for automated force field generation
    Howard Yanxon, David Zagaceta, Binh Tang, David S. Matteson, Qiang Zhu · 2020 · Machine Learning Science and Technology
51. Actinide Molten Salts: A Machine-Learning Potential Molecular Dynamics Study
    Manh‐Thuong Nguyen, Roger J. Rousseau, Patricia D. Paviet, Vassiliki‐Alexandra Glezakou · 2021 · ACS Applied Materials & Interfaces
52. Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments
    Viktor Zaverkin, David Holzmüller, Ingo Steinwart, Johannes Kästner · 2021 · Journal of Chemical Theory and Computation
53. Machine Learning of First-Principles Force-Fields for Alkane and Polyene Hydrocarbons
    Amir Hajibabaei, Miran Ha, Saeed Pourasad, Junu Kim, Kwang S. Kim · 2021 · The Journal of Physical Chemistry A
54. Classifying soft self-assembled materials via unsupervised machine learning of defects
    Andrea Gardin, Claudio Perego, Giovanni Doni, Giovanni Maria Pavan · 2022 · Communications Chemistry
55. Catalytic Activity Maps for Alloy Nanoparticles
    Liang Cao, Tim Mueller · 2023 · Journal of the American Chemical Society
56. Improved atomistic Monte Carlo models based onab-initio-trained neural networks: Application to FeCu and FeCr alloys
    Nicolas Castin, Luca Messina, Christophe Domain, Roberto C. Pasianot, Pär Olsson · 2017 · Physical review. B./Physical review. B
57. Machine-Learning Informed Representations for Grain Boundary Structures
    Eric R. Homer, Derek M. Hensley, Conrad W. Rosenbrock, Andrew Huy Nguyen, Gus L. W. Hart · 2019 · Frontiers in Materials
58. Predicting Structural Properties of Pure Silica Zeolites Using Deep Neural Network Potentials
    Sours, Tyler G., Kulkarni, Ambarish R. · 2023 · The Journal of Physical Chemistry C
59. Combining Machine Learning Approaches and Accurate Ab Initio Enhanced Sampling Methods for Prebiotic Chemical Reactions in Solution
    Timothée Devergne, Théo Magrino, Fabio Pietrucci, Antonino Marco Saitta · 2022 · Journal of Chemical Theory and Computation
60. Automatic Prediction of Band Gaps of Inorganic Materials Using a Gradient Boosted and Statistical Feature Selection Workflow
    Son Gyo Jung, Guwon Jung, Jacqueline M. Cole · 2024 · Journal of Chemical Information and Modeling
61. Informative Training Data for Efficient Property Prediction in Metal–Organic Frameworks by Active Learning
    Ashna Jose, Émilie Devijver, N. Jakse, Roberta Poloni · 2024 · Journal of the American Chemical Society
62. Delta Machine Learning for Predicting Dielectric Properties and Raman Spectra
    Manuel Grumet, Clara von Scarpatetti, Tomáš Bučko, David A. Egger · 2024 · The Journal of Physical Chemistry C
63. Machine Learning-Accelerated High-Throughput Computational Screening: Unveiling Bimetallic Nanoparticles with Peroxidase-Like Activity
    Kaiwei Wan, Hui Wang, Xinghua Shi · 2024 · ACS Nano
64. Introduction to machine learning potentials for atomistic simulations
    Fabian L. Thiemann, Niamh O’Neill, Venkat Kapil, Angelos Michaelides, Christoph Schran · 2024 · Journal of Physics Condensed Matter
65. Computational tools for the prediction of site- and regioselectivity of organic reactions
    Lukas M. Sigmund, Michele Assante, Magnus J. Johansson, Per‐Ola Norrby, Kjell Jorner, Mikhail Kabeshov · 2025 · Chemical Science
66. Thermochemistry of gas-phase and surface species via LASSO-assisted subgraph selection
    Geun Ho Gu, Petr Plecháč, Dionisios G. Vlachos · 2018 · Reaction Chemistry & Engineering
67. Predicting Elastic Properties of Materials from Electronic Charge Density Using 3D Deep Convolutional Neural Networks
    Yong Zhao, Kunpeng Yuan, Yinqiao Liu, Steph-Yves Louis, Ming Hu, Jianjun Hu · 2020 · The Journal of Physical Chemistry C
68. Predicting the phase diagram of titanium dioxide with random search and pattern recognition
    Aleks Reinhardt, Chris J. Pickard, Bingqing Cheng · 2020 · Physical Chemistry Chemical Physics
69. Hydration free energies from kernel-based machine learning: Compound-database bias
    Clemens Rauer, Tristan Bereau · 2020 · The Journal of Chemical Physics
70. Predicting path-dependent diffusion barrier spectra in vast compositional space of multi-principal element alloys via convolutional neural networks
    Zhao Fan, Bin Xing, Penghui Cao · 2022 · Acta Materialia
71. Interatomic potentials for oxide glasses: Past, present, and future
    Alfonso Pedone, Marco Bertani, Luca Brugnoli, Annalisa Pallini · 2022 · Journal of Non-Crystalline Solids X
72. Beyond potentials: Integrated machine learning models for materials
    Michele Ceriotti · 2022 · MRS Bulletin
73. Exploring the configuration space of elemental carbon with empirical and machine learned interatomic potentials
    George A. Marchant, A. Miguel, Bora Karasulu, Lívia B. Pártay · 2023 · npj Computational Materials
74. MLatom Software Ecosystem for Surface Hopping Dynamics in Python with Quantum Mechanical and Machine Learning Methods
    Lina Zhang, Sebastian V. Pios, Mikołaj Martyka, Fuchun Ge, Yi-Fan Hou, Yuxinxin Chen, Lipeng Chen, Joanna Jankowska, Mario Barbatti, Pavlo O. Dral · 2024 · Journal of Chemical Theory and Computation
75. Optimal Alignment of Structures for Finite and Periodic Systems
    Matthew Griffiths, Samuel P. Niblett, David J. Wales · 2017 · Journal of Chemical Theory and Computation
76. Size‐Extensive Molecular Machine Learning with Global Representations
    Hyunwook Jung, Sina Stocker, Christian Künkel, Harald Oberhofer, Byungchan Han, Karsten Reuter, Johannes T. Margraf · 2020 · ChemSystemsChem
77. Bonding similarities and differences between Y–Sb–Te and Sc–Sb–Te phase-change memory materials
    Yuxing Zhou, Liang Sun, Getasew Mulualem Zewdie, Riccardo Mazzarello, Volker L. Deringer, Evan Ma, Wei Zhang · 2020 · Journal of Materials Chemistry C
78. An On-the-Fly Approach to Construct Generalized Energy-Based Fragmentation Machine Learning Force Fields of Complex Systems
    Zheng Cheng, Dongbo Zhao, Jing Ma, Wei Li, Shuhua Li · 2020 · The Journal of Physical Chemistry A
79. Hessian Matrix Update Scheme for Transition State Search Based on Gaussian Process Regression
    Alexander Denzel, Johannes Kästner · 2020 · Journal of Chemical Theory and Computation
80. A Data-Driven Dimensionality Reduction Approach to Compare and Classify Lipid Force Fields
    Riccardo Capelli, Andrea Gardin, Charly Empereur‐mot, Giovanni Doni, Giovanni Maria Pavan · 2021 · The Journal of Physical Chemistry B
81. First-Principles-Based Machine-Learning Molecular Dynamics for Crystalline Polymers with van der Waals Interactions
    Sung Jun Hong, Hoje Chun, Je-Hyun Lee, Byung‐Hyun Kim, Min Ho Seo, Joonhee Kang, Byungchan Han · 2021 · The Journal of Physical Chemistry Letters
82. Predicting properties of periodic systems from cluster data: A case study of liquid water
    Viktor Zaverkin, David Holzmüller, Robin Schuldt, Johannes Kästner · 2022 · The Journal of Chemical Physics
83. Successes and challenges in using machine-learned activation energies in kinetic simulations
    Idil Ismail, Christopher Robertson, Scott Habershon · 2022 · The Journal of Chemical Physics
84. Dimensionality reduction of local structure in glassy binary mixtures
    Daniele Coslovich, Robert L. Jack, Joris Paret · 2022 · The Journal of Chemical Physics
85. Integrating Machine Learning with Physics-Based Modeling
    E Weinan, Jiequn Han, Linfeng Zhang · 2020 · arXiv (Cornell University)
86. Deep Learning-Based Increment Theory for Formation Enthalpy Predictions
    Lung-Yi Chen, Ting-Wei Hsu, Tsai-Chen Hsiung, Yi‐Pei Li · 2022 · The Journal of Physical Chemistry A
87. Multi-scale computational study of high-temperature corrosion and the design of corrosion-resistant alloys
    Terrence Wenga, Digby D. Macdonald, Wenchao Ma · 2024 · Progress in Materials Science
88. Exploring a potential energy surface by machine learning for characterizing atomic transport
    Kenta Kanamori, Kazuaki Toyoura, Junya Honda, Kazuki Hattori, Atsuto Seko, Masayuki Karasuyama, Kazuki Shitara, M. Shiga, Akihide Kuwabara, Ichiro Takeuchi · 2018 · Physical review. B./Physical review. B
89. Prediction of atomization energy using graph kernel and active learning
    Yuhang Tang, Wibe Albert de Jong · 2019 · The Journal of Chemical Physics
90. High-Precision Atomic Charge Prediction for Protein Systems Using Fragment Molecular Orbital Calculation and Machine Learning
    Koichiro Kato, Tomohide Masuda, Chiduru Watanabe, Naoki Miyagawa, Hideo Mizouchi, Shumpei Nagase, Kikuko Kamisaka, Kanji Oshima, Satoshi Ono, Hiroshi Ueda et al. · 2020 · Journal of Chemical Information and Modeling
91. Machine-guided representation for accurate graph-based molecular machine learning
    Gyoung S. Na, Hyunju Chang, Hyun Woo Kim · 2020 · Physical Chemistry Chemical Physics
92. Toward a Holistic Understanding of the Formation and Growth of Atmospheric Molecular Clusters: A Quantum Machine Learning Perspective
    Jonas Elm · 2020 · The Journal of Physical Chemistry A
93. Smart Materials Prediction: Applying Machine Learning to Lithium Solid-State Electrolyte
    Qianyu Hu, Kunfeng Chen, Fei Liu, Mengying Zhao, Feng Liang, Dongfeng Xue · 2022 · Materials
94. High-Dimensional Fluctuations in Liquid Water: Combining Chemical Intuition with Unsupervised Learning
    Adu Offei-Danso, Ali A. Hassanali, Álex Rodríguez · 2022 · Journal of Chemical Theory and Computation
95. Deep learning approach to genome of two-dimensional materials with flat electronic bands
    Anupam Bhattacharya, Ivan Timokhin, R. Chatterjee, Q. Yang, Artem Mishchenko · 2023 · npj Computational Materials
96. Recent advances in the application of machine-learning algorithms to predict adsorption energies
    Liang Cao · 2022 · Trends in Chemistry
97. Developing Cheap but Useful Machine Learning-Based Models for Investigating High-Entropy Alloy Catalysts
    Chenghan Sun, Rajat Goel, Ambarish R. Kulkarni · 2024 · Langmuir
98. Electronic Excited States from Physically Constrained Machine Learning
    Edoardo Cignoni, Divya Suman, Jigyasa Nigam, Lorenzo Cupellini, Benedetta Mennucci, Michele Ceriotti · 2024 · ACS Central Science
99. Solutions for Lithium Battery Materials Data Issues in Machine Learning: Overview and Future Outlook
    Pengcheng Xue, Rui Qiu, Chuchuan Peng, Zehang Peng, Kui Ding, Rui Long, Liang Ma, Qifeng Zheng · 2024 · Advanced Science
100. Machine-Learning-Accelerated Surface Exploration of Reconstructed BiVO 4 (010) and Characterization of Their Aqueous Interfaces
     Yonghyuk Lee, Taehun Lee · 2025 · Journal of the American Chemical Society