On representing chemical environments

Cited by (1,001)

Items 201–300 of 1,001

1. Modeling the Phase-Change Memory Material, Ge2Sb2Te5, with a Machine-Learned Interatomic Potential
   Felix C. Mocanu, Konstantinos Konstantinou, Tae Hoon Lee, Noam Bernstein, Volker L. Deringer, Gábor Cśanyi, Stephen R. Elliott · 2018 · The Journal of Physical Chemistry B
2. Thermal transport and phase transitions of zirconia by on-the-fly machine-learned interatomic potentials
   Carla Verdi, Ferenc Karsai, Peitao Liu, Ryosuke Jinnouchi, Georg Kresse · 2021 · npj Computational Materials
3. Fast and Accurate Uncertainty Estimation in Chemical Machine Learning
   Félix Musil, Michael J. Willatt, Mikhail Langovoy, Michele Ceriotti · 2019 · Journal of Chemical Theory and Computation
4. Machine learning and artificial neural network accelerated computational discoveries in materials science
   Yang Hong, Bo Hou, Hengle Jiang, Jingchao Zhang · 2019 · Wiley Interdisciplinary Reviews Computational Molecular Science
5. A critical examination of robustness and generalizability of machine learning prediction of materials properties
   Kangming Li, Brian DeCost, Kamal Kumar Choudhary, Michael Greenwood, Jason R. Hattrick-Simpers · 2023 · npj Computational Materials
6. Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations
   Guanjie Wang, Changrui Wang, Xuanguang Zhang, Zefeng Li, Jian Zhou, Zhimei Sun · 2024 · iScience
7. DPA-2: a large atomic model as a multi-task learner
   Zhang, Duo, Liu, Xinzijian, Zhang, Xiangyu, Zhang, Chengqian, Cai, Chun, Bi, Hangrui, Du, Yiming, Qin, Xuejian, Peng, Anyang, Huang, Jiameng et al. · 2023 · arXiv
8. Orbital graph convolutional neural network for material property prediction
   Mohammadreza Karamad, Rishikesh Magar, Yuting Shi, Samira Siahrostami, Ian Donald Gates, Amir Barati Farimani · 2020 · Physical Review Materials
9. Molecular Dynamics Simulations with Quantum Mechanics/Molecular Mechanics and Adaptive Neural Networks
   Lin Fang Shen, Weitao Yang · 2018 · Journal of Chemical Theory and Computation
10. Optimizing many-body atomic descriptors for enhanced computational performance of machine learning based interatomic potentials
    A. Miguel · 2019 · Physical review. B./Physical review. B
11. Machine-learned interatomic potentials for alloys and alloy phase diagrams
    Conrad W. Rosenbrock, Konstantin Gubaev, Alexander V. Shapeev, Lívia B. Pártay, Noam Bernstein, Gábor Cśanyi, Gus L. W. Hart · 2021 · npj Computational Materials
12. Structure-based sampling and self-correcting machine learning for accurate calculations of potential energy surfaces and vibrational levels
    Pavlo O. Dral, Alec Owens, Sergei N. Yurchenko, Walter Thiel · 2017 · The Journal of Chemical Physics
13. Data‐Driven Machine Learning for Understanding Surface Structures of Heterogeneous Catalysts
    Haobo Li, Yan Jiao, Kenneth R. Davey, Shi‐Zhang Qiao · 2022 · Angewandte Chemie International Edition
14. Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning
    Marcel F. Langer, Alex Goeßmann, Matthias Rupp · 2022 · npj Computational Materials
15. High-energy and durable aqueous magnesium batteries: Recent advances and perspectives
    Min Deng, Linqian Wang, Bahram Vaghefinazari, Wen Lin Xu, Christian Feiler, Sviatlana V. Lamaka, Daniel Höche, Mikhail L. Zheludkevich, Darya Snihirova · 2021 · Energy storage materials
16. Machine-learning interatomic potential for radiation damage and defects in tungsten
    Jesper Byggmästar, A. Hamedani, K. Nordlund, Flyura Djurabekova · 2019 · Physical review. B./Physical review. B
17. Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt
    Vandermause, Jonathan, Xie, Yu, Lim, Jin Soo, Owen, Cameron J., Kozinsky, Boris · 2022 · Nature Communications
18. SIMPLE-NN: An efficient package for training and executing neural-network interatomic potentials
    Kyuhyun Lee, Dongsun Yoo, Wonseok Jeong, Seungwu Han · 2019 · Computer Physics Communications
19. Real-time prediction of 1 H and 13 C chemical shifts with DFT accuracy using a 3D graph neural network
    Yanfei Guan, Shree Sowndarya S. V., Liliana C. Gallegos, Peter C. St. John, Robert S. Paton · 2021 · Chemical Science
20. Coupling a Crystal Graph Multilayer Descriptor to Active Learning for Rapid Discovery of 2D Ferromagnetic Semiconductors/Half‐Metals/Metals
    Shuaihua Lu, Qionghua Zhou, Yilv Guo, Yehui Zhang, Yilei Wu, Jinlan Wang · 2020 · Advanced Materials
21. Efficient nonparametric n -body force fields from machine learning
    Aldo Glielmo, Claudio Zeni, Alessandro De Vita · 2018 · Physical review. B./Physical review. B
22. Learning the electronic density of states in condensed matter
    Chiheb Ben Mahmoud, Andrea Anelli, Gábor Cśanyi, Michele Ceriotti · 2020 · Physical review. B./Physical review. B
23. Human- and machine-centred designs of molecules and materials for sustainability and decarbonization
    Jiayu Peng, Daniel Schwalbe‐Koda, Karthik Akkiraju, Tian Xie, Livia Giordano, Yang Yu, C. John Eom, Jaclyn R. Lunger, Daniel Jia Zheng, Reshma R. Rao et al. · 2022 · Nature Reviews Materials
24. Modeling refractory high-entropy alloys with efficient machine-learned interatomic potentials: Defects and segregation
    Jesper Byggmästar, K. Nordlund, Flyura Djurabekova · 2021 · Physical review. B./Physical review. B
25. MAGUS: machine learning and graph theory assisted universal structure searcher
    Junjie Wang, Hao Gao, Yu Han, Chi Ding, Shuning Pan, Yong Wang, Qiuhan Jia, Hui‐Tian Wang, Dingyu Xing, Jian Sun · 2023 · National Science Review
26. Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements
    Willatt, Michael J., Musil, Félix, Ceriotti, Michele · 2018 · Physical Chemistry Chemical Physics
27. Metrics for measuring distances in configuration spaces
    Sadeghi, Ali, Ghasemi, S. Alireza, Schaefer, Bastian, Mohr, Stephan, Lill, Markus A., Goedecker, Stefan · 2013 · The Journal of Chemical Physics
28. Critical review of machine learning applications in perovskite solar research
    Beyza Yılmaz, Ramazan Yıldırım · 2020 · Nano Energy
29. Representations of Materials for Machine Learning
    James Damewood, Jessica Karaguesian, Jaclyn R. Lunger, Aik Rui Tan, Mingrou Xie, Jiayu Peng, Rafael Gómez‐Bombarelli · 2023 · Annual Review of Materials Research
30. Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials
    Tian Xie, Arthur France‐Lanord, Yanming Wang, Yang Shao‐Horn, Jeffrey C. Grossman · 2019 · Nature Communications
31. Atomic cluster expansion: Completeness, efficiency and stability
    Geneviève Dusson, Markus Bachmayr, Gábor Cśanyi, Ralf Drautz, Simon Etter, Cas van der Oord, Christoph Ortner · 2022 · Journal of Computational Physics
32. Accelerating materials science with high-throughput computations and machine learning
    Shyue Ping Ong · 2019 · Computational Materials Science
33. A general-purpose machine-learning force field for bulk and nanostructured phosphorus
    Volker L. Deringer, A. Miguel, Gábor Cśanyi · 2020 · Nature Communications
34. Recent advances in knowledge discovery for heterogeneous catalysis using machine learning
    Mehmet Erdem Günay, Ramazan Yıldırım · 2020 · Catalysis Reviews
35. How to validate machine-learned interatomic potentials
    Joe D. Morrow, John L. A. Gardner, Volker L. Deringer · 2023 · The Journal of Chemical Physics
36. Large-Scale Atomic Simulation via Machine Learning Potentials Constructed by Global Potential Energy Surface Exploration
    Pei‐Lin Kang, Cheng Shang, Zhi‐Pan Liu · 2020 · Accounts of Chemical Research
37. Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces
    Huziel E. Sauceda, Stefan Chmiela, Igor Poltavsky, Klaus-Robert Müller, Alexandre Tkatchenko · 2019 · The Journal of Chemical Physics
38. Ab initio quantum chemistry with neural-network wavefunctions
    Jan Hermann, JAMES M. SPENCER, Kenny Choo, Antonio Mezzacapo, W. M. C. Foulkes, David Pfau, Giuseppe Carleo, Frank Noé · 2023 · Nature Reviews Chemistry
39. First principles view on chemical compound space: Gaining rigorous atomistic control of molecular properties
    O. Anatole von Lilienfeld · 2013 · International Journal of Quantum Chemistry
40. Advances of machine learning in molecular modeling and simulation
    Mojtaba Haghighatlari, Johannes Hachmann · 2019 · Current Opinion in Chemical Engineering
41. Dataset’s chemical diversity limits the generalizability of machine learning predictions
    Marta Glavatskikh, Jules Leguy, Gilles Hunault, Thomas Cauchy, Benoit Da Mota · 2019 · Journal of Cheminformatics
42. Gaussian process regression for geometry optimization
    Alexander Denzel, Johannes Kästner · 2018 · The Journal of Chemical Physics
43. Neural network potentials for chemistry: concepts, applications and prospects
    Silvan Käser, Luis Itza Vazquez-Salazar, Markus Meuwly, Kai Töpfer · 2022 · Digital Discovery
44. Device-scale atomistic modelling of phase-change memory materials
    Yuxing Zhou, Wei Zhang, Evan Ma, Volker L. Deringer · 2023 · Nature Electronics
45. Machine learning for materials design and discovery
    Rama Krishnan Vasudevan, Ghanshyam Pilania, Prasanna V. Balachandran · 2021 · Journal of Applied Physics
46. High-Dimensional Neural Network Potentials for Organic Reactions and an Improved Training Algorithm
    Michael Gastegger, Philipp Marquetand · 2015 · Journal of Chemical Theory and Computation
47. Boosting Quantum Machine Learning Models with a Multilevel Combination Technique: Pople Diagrams Revisited
    Peter Zaspel, Bing Huang, Helmut Harbrecht, O. Anatole von Lilienfeld · 2018 · Journal of Chemical Theory and Computation
48. A fingerprint based metric for measuring similarities of crystalline structures
    Li Zhu, Maximilian Amsler, Tobias Fuhrer, Bastian Schaefer, Somayeh Faraji, Samare Rostami, S. Alireza Ghasemi, Ali Sadeghi, Migle Grauzinyte, Chris Wolverton et al. · 2016 · The Journal of Chemical Physics
49. Automated structure discovery in atomic force microscopy
    Benjamin Alldritt, Prokop Hapala, Niko Oinonen, Fedor Urtev, Ondřej Krejčí, Filippo Federici Canova, Juho Kannala, Fabian Schulz, Peter Liljeroth, Adam Stuart Foster · 2020 · Science Advances
50. Deep potential generation scheme and simulation protocol for the Li10GeP2S12-type superionic conductors
    Jianxing Huang, Linfeng Zhang, Han Wang, Jinbao Zhao, Jun Cheng, Weinan E · 2021 · The Journal of Chemical Physics
51. Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network
    Yao, Kun, Herr, John E., Brown, Seth N., Parkhill, John · 2017 · The Journal of Physical Chemistry Letters
52. First principles and machine learning based superior catalytic activities and selectivities for N 2 reduction in MBenes, defective 2D materials and 2D π-conjugated polymer-supported single atom catalysts
    Mohammad Zafari, Arun S. Nissimagoudar, Muhammad Umer, Geunsik Lee, Kwang S. Kim · 2021 · Journal of Materials Chemistry A
53. On-the-Fly Machine Learning of Atomic Potential in Density Functional Theory Structure Optimization
    T. L. Jacobsen, Mathias Jo̷rgensen, Bjørk Hammer · 2018 · Physical Review Letters
54. Advanced graph and sequence neural networks for molecular property prediction and drug discovery
    Zhengyang Wang, Meng Liu, Youzhi Luo, Xu Zhao, Yaochen Xie, Limei Wang, Lei Cai, Qi Qi, Zhuoning Yuan, Tianbao Yang et al. · 2022 · Bioinformatics
55. Quantum-accurate spectral neighbor analysis potential models for Ni-Mo binary alloys and fcc metals
    Xiangguo Li, Chongze Hu, Chi Chen, Zhi Deng, Jian Luo, Shyue Ping Ong · 2018 · Physical review. B./Physical review. B
56. Mapping Materials and Molecules
    Bingqing Cheng, Ryan‐Rhys Griffiths, Simon Wengert, Christian Künkel, Tamás K. Stenczel, Bonan Zhu, Volker L. Deringer, Noam Bernstein, Johannes T. Margraf, Karsten Reuter et al. · 2020 · Accounts of Chemical Research
57. Operators in quantum machine learning: Response properties in chemical space
    Anders S. Christensen, Felix A. Faber, O. Anatole von Lilienfeld · 2019 · The Journal of Chemical Physics
58. Representation of molecular structures with persistent homology for machine learning applications in chemistry
    Jacob Townsend, Cassie Putman Micucci, John H. Hymel, Vasileios Maroulas, Konstantinos D. Vogiatzis · 2020 · Nature Communications
59. Constant size descriptors for accurate machine learning models of molecular properties
    Christopher R. Collins, Geoffrey J. Gordon, O. Anatole von Lilienfeld, David Yaron · 2018 · The Journal of Chemical Physics
60. Applications of Machine Learning in Alloy Catalysts: Rational Selection and Future Development of Descriptors
    Ze kun Yang, Wang Gao · 2022 · Advanced Science
61. Machine learning of material properties: Predictive and interpretable multilinear models
    Alice E. A. Allen, Alexandre Tkatchenko · 2022 · Science Advances
62. Linear Atomic Cluster Expansion Force Fields for Organic Molecules: Beyond RMSE
    Dávid Péter Kovács, Cas van der Oord, Jiri Kucera, Alice E. A. Allen, Daniel J. Cole, Christoph Ortner, Gábor Cśanyi · 2021 · Journal of Chemical Theory and Computation
63. The many-body expansion combined with neural networks
    Yao, Kun, Herr, John E., Parkhill, John · 2017 · The Journal of Chemical Physics
64. Artificial Intelligence and Advanced Materials
    Cefe López · 2022 · Advanced Materials
65. An electrostatic spectral neighbor analysis potential for lithium nitride
    Zhi Deng, Chi Chen, Xiangguo Li, Shyue Ping Ong · 2019 · npj Computational Materials
66. Bayesian optimization of nanoporous materials
    Aryan Deshwal, Cory M. Simon, Janardhan Rao Doppa · 2021 · Molecular Systems Design & Engineering
67. Reversible assembly of nanoparticles: theory, strategies and computational simulations
    Denis Gentili, Guido Ori · 2022 · Nanoscale
68. Deep dive into machine learning density functional theory for materials science and chemistry
    Lenz Fiedler, Karan Shah, Michael Bußmann, Attila Cangi · 2022 · Physical Review Materials
69. Learning to Make Chemical Predictions: The Interplay of Feature Representation, Data, and Machine Learning Methods
    Mojtaba Haghighatlari, Jie Li, Farnaz Heidar‐Zadeh, Yuchen Liu, Xingyi Guan, Teresa L. Head-Gordon · 2020 · Chem
70. Machine Learning: A New Paradigm in Computational Electrocatalysis
    Xu Zhang, Yun Tian, Letian Chen, Xu Hu, Zhen Ping Zhou · 2022 · The Journal of Physical Chemistry Letters
71. Machine learning on neutron and x-ray scattering and spectroscopies
    Zhantao Chen, Nina Andrejevic, Nathan C. Drucker, Thanh Nguyen, Rui Patrick Xian, Tess Smidt, Yao Wang, Ralph Ernstorfer, D. M. Tennant, Maria K. Y. Chan et al. · 2021 · Chemical Physics Reviews
72. Efficient parametrization of the atomic cluster expansion
    Anton S. Bochkarev, Yury V. Lysogorskiy, Sarath Kumar Menon, Minaam Qamar, Matous Mrovec, Ralf Drautz · 2022 · Physical Review Materials
73. Accelerating CALYPSO structure prediction by data-driven learning of a potential energy surface
    Qunchao Tong, Lantian Xue, Jian Lv, Yanchao Wang, Yanming Ma · 2018 · Faraday Discussions
74. Combining machine learning and high-throughput experimentation to discover photocatalytically active organic molecules
    Xiaobo Li, Phillip M. Maffettone, Yu Che, Tao Liu, Linjiang Chen, Andrew Ian Cooper · 2021 · Chemical Science
75. Learning Matter: Materials Design with Machine Learning and Atomistic Simulations
    Simon Axelrod, Daniel Schwalbe‐Koda, Somesh Mohapatra, James Damewood, Kevin P. Greenman, Rafael Gómez‐Bombarelli · 2022 · Accounts of Materials Research
76. Large-Scale Computational Screening of Molecular Organic Semiconductors Using Crystal Structure Prediction
    Yang, Jack, De, Sandip, Campbell, Josh E., Li, Sean, Ceriotti, Michele, Day, Graeme M. · 2018 · Chemistry of Materials
77. Screw dislocation structure and mobility in body centered cubic Fe predicted by a Gaussian Approximation Potential
    Francesco Maresca, Daniele Dragoni, Gábor Cśanyi, Nicola Marzari, W.A. Curtin · 2018 · npj Computational Materials
78. Transferability in interatomic potentials for carbon
    Carla de Tomás, Alireza Aghajamali, Jake Jones, Daniel J. Lim, María J. López, Irene Suarez‐Martinez, Nigel A. Marks · 2019 · Carbon
79. Modelling and understanding battery materials with machine-learning-driven atomistic simulations
    Volker L. Deringer · 2020 · Journal of Physics Energy
80. Designing and understanding light-harvesting devices with machine learning
    Florian Häse, Loı̈c M. Roch, Pascal Friederich, Alán Aspuru‐Guzik · 2020 · Nature Communications
81. Maximizing information from chemical engineering data sets: Applications to machine learning
    Alexander Thebelt, Johannes Wiebe, Jan Kronqvist, Calvin Tsay, Ruth Misener · 2022 · Chemical Engineering Science
82. Unified graph neural network force-field for the periodic table: solid state applications
    Kamal Kumar Choudhary, Brian DeCost, Lily Major, Keith T. Butler, Jeyan Thiyagalingam, Francesca Tavazza · 2023 · Digital Discovery
83. Discovering the building blocks of atomic systems using machine learning: application to grain boundaries
    Conrad W. Rosenbrock, Eric R. Homer, Gábor Cśanyi, Gus L. W. Hart · 2017 · npj Computational Materials
84. Transferable Multilevel Attention Neural Network for Accurate Prediction of Quantum Chemistry Properties via Multitask Learning
    Ziteng Liu, Liqiang Lin, Qingqing Jia, Zheng Cheng, Yanyan Jiang, Yanwen Guo, Jing Ma · 2021 · Journal of Chemical Information and Modeling
85. Atomic cluster expansion of scalar, vectorial, and tensorial properties including magnetism and charge transfer
    Ralf Drautz · 2020 · Physical review. B./Physical review. B
86. Targeted design of advanced electrocatalysts by machine learning
    Letian Chen, Xu Zhang, An Chen, Sai Yao, Xu Hu, Zhen Ping Zhou · 2021 · CHINESE JOURNAL OF CATALYSIS (CHINESE VERSION)
87. Multi-step nucleation pathway of C-S-H during cement hydration from atomistic simulations
    Xabier M. Aretxabaleta, Jon López-Zorrilla, I. Etxebarria, Hegoi Manzano · 2023 · Nature Communications
88. Temperature Dependent Thermal and Elastic Properties of High Entropy (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)B2: Molecular Dynamics Simulation by Deep Learning Potential
    Fu‐Zhi Dai, Yinjie Sun, Bo Wen, Huimin Xiang, Yanchun Zhou · 2020 · Journal of Material Science and Technology
89. Polymer graph neural networks for multitask property learning
    Owen Queen, Gavin A. McCarver, Saitheeraj Thatigotla, Brendan P. Abolins, Cameron L. Brown, Vasileios Maroulas, Konstantinos D. Vogiatzis · 2023 · npj Computational Materials
90. Accelerating the prediction of stable materials with machine learning
    Sean D. Griesemer, Yi Xia, Chris M. Wolverton · 2023 · Nature Computational Science
91. Molecular dynamics simulations of heat transport using machine-learned potentials: A mini-review and tutorial on GPUMD with neuroevolution potentials
    Haikuan Dong, Yongbo Shi, Penghua Ying, Ke Wei Xu, Ting Liang, Yanzhou Wang, Zezhu Zeng, Xin Wu, Wenjiang Zhou, Shiyun Xiong et al. · 2024 · Journal of Applied Physics
92. Similarity Between Amorphous and Crystalline Phases: The Case of TiO2
    Juraj Mavračić, Felix C. Mocanu, Volker L. Deringer, Gábor Cśanyi, Stephen R. Elliott · 2018 · The Journal of Physical Chemistry Letters
93. Artificial intelligence: machine learning for chemical sciences
    Akshaya Karthikeyan, U. Deva Priyakumar · 2021 · Journal of Chemical Sciences
94. Advances of machine learning in materials science: Ideas and techniques
    Sue Sin Chong, Yi Sheng Ng, Hui‐Qiong Wang, Jin‐Cheng Zheng · 2023 · Frontiers of Physics
95. Data-efficient machine learning for molecular crystal structure prediction
    Simon Wengert, Gábor Cśanyi, Karsten Reuter, Johannes T. Margraf · 2021 · Chemical Science
96. Machine learning for predicting thermal transport properties of solids
    Xin Qian, Ronggui Yang · 2021 · Materials Science and Engineering R Reports
97. Reactivity of Amorphous Carbon Surfaces: Rationalizing the Role of Structural Motifs in Functionalization Using Machine Learning
    A. Miguel, Anja Aarva, Volker L. Deringer, Gábor Cśanyi, Tomi T. Laurila · 2018 · Chemistry of Materials
98. Combining Machine Learning Potential and Structure Prediction for Accelerated Materials Design and Discovery
    Qunchao Tong, Pengyue Gao, Hanyu Liu, Yu Xie, Jian Lv, Yanchao Wang, Jijun Zhao · 2020 · The Journal of Physical Chemistry Letters
99. Machine Learning for Chemistry: Basics and Applications
    Yunfei Shi, Zhengxin Yang, Sicong Ma, Pei‐Lin Kang, Cheng Shang, P. Hu, Zhi‐Pan Liu · 2023 · Engineering
100. Machine learning in materials research: Developments over the last decade and challenges for the future
     Anubhav Jain · 2024 · Current Opinion in Solid State and Materials Science