On representing chemical environments

Cited by (1,001)

Items 101–200 of 1,001

1. Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems
   Grisafi, Andrea, Wilkins, David M., Csányi, Gábor, Ceriotti, Michele · 2018 · Physical Review Letters
2. Library-Based LAMMPS Implementation of High-Dimensional Neural Network Potentials
   Andreas Singraber, Jörg Behler, Christoph Dellago · 2019 · Journal of Chemical Theory and Computation
3. FCHL revisited: Faster and more accurate quantum machine learning
   Anders Steen Christensen, Lars Andersen Bratholm, Felix A. Faber, O. Anatole von Lilienfeld · 2020 · The Journal of Chemical Physics
4. Representation of compounds for machine-learning prediction of physical properties
   Atsuto Seko, Hiroyuki Hayashi, Keita Nakayama, Akira Takahashi, Isao Tanaka · 2017 · Physical review. B./Physical review. B
5. wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials
   Michael Gastegger, Ludwig Schwiedrzik, Marius R. Bittermann, Florian Berzsenyi, Philipp Marquetand · 2018 · The Journal of Chemical Physics
6. Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity
   Huang, Bing, von Lilienfeld, O. Anatole · 2016 · The Journal of Chemical Physics
7. An accurate and transferable machine learning potential for carbon
   Patrick Rowe, Volker L. Deringer, Piero Gasparotto, Gábor Cśanyi, Angelos Michaelides · 2020 · The Journal of Chemical Physics
8. Machine learning in materials science: From explainable predictions to autonomous design
   Ghanshyam Pilania · 2021 · Computational Materials Science
9. Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships
   Janet, Jon Paul, Kulik, Heather J. · 2017 · The Journal of Physical Chemistry A
10. Moment Tensor Potentials
    Alexander V. Shapeev · 2016 · arXiv (Cornell University)
11. Electrostatic co-assembly of nanoparticles with oppositely charged small molecules into static and dynamic superstructures
    Tong Bian, Andrea Gardin, Julius Gemen, Lothar Houben, Claudio Perego, Byeongdu Lee, Nadav Elad, Zonglin Chu, Giovanni Maria Pavan, Rafał Klajn · 2021 · Nature Chemistry
12. Achieving DFT accuracy with a machine-learning interatomic potential: Thermomechanics and defects in bcc ferromagnetic iron
    Daniele Dragoni, Thomas D. Daff, Gábor Cśanyi, Nicola Marzari · 2018 · Physical Review Materials
13. The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules
    Justin S. Smith, R.I. Zubatyuk, Benjamin T. Nebgen, Nicholas Lubbers, Kipton M. Barros, Adrián E. Roitberg, Olexandr Isayev, Sergei Tretiak · 2020 · Scientific Data
14. A critical examination of compound stability predictions from machine-learned formation energies
    Bartel, Christopher J., Trewartha, Amalie, Wang, Qi, Dunn, Alexander, Jain, Anubhav, Ceder, Gerbrand · 2020 · npj Computational Materials
15. Applying machine learning techniques to predict the properties of energetic materials
    Daniel C. Elton, Zois Boukouvalas, Mark S. Butrico, Mark Fuge, Peter W. Chung · 2018 · Scientific Reports
16. Embedded Atom Neural Network Potentials: Efficient and Accurate Machine Learning with a Physically Inspired Representation
    Yaolong Zhang, Ce Hu, Bin Jiang · 2019 · The Journal of Physical Chemistry Letters
17. Genetic algorithms for computational materials discovery accelerated by machine learning
    Jennings, Paul C., Lysgaard, Steen, Hummelshøj, Jens Strabo, Vegge, Tejs, Bligaard, Thomas · 2019 · npj Computational Materials
18. Transferable Machine-Learning Model of the Electron Density
    Andrea Grisafi, Alberto Fabrizio, Benjamin Meyer, David M. Wilkins, Clémence Corminbœuf, Michele Ceriotti · 2018 · ACS Central Science
19. Chemical shifts in molecular solids by machine learning
    Federico M. Paruzzo, Albert Hofstetter, Félix Musil, Sandip De, Michele Ceriotti, Lyndon Emsley · 2018 · Nature Communications
20. Pushing the Limit of Molecular Dynamics with Ab Initio Accuracy to 100 Million Atoms with Machine Learning
    Weile Jia, Han Wang, Mohan Chen, Denghui Lu, Lin Lin, Roberto Car, E Weinan, Linfeng Zhang · 2020 · International Conference for High Performance Computing, Networking, Storage and Analysis (SC)
21. Realistic Atomistic Structure of Amorphous Silicon from Machine-Learning-Driven Molecular Dynamics
    Volker L. Deringer, Noam Bernstein, Albert P. Bartók, Matthew J. Cliffe, Rachel Nathaniel Kerber, Lauren E. Marbella, Clare P. Grey, Stephen R. Elliott, Gábor Cśanyi · 2018 · The Journal of Physical Chemistry Letters
22. Rechargeable Alkali-Ion Battery Materials: Theory and Computation
    Anton Van der Ven, Zhi Deng, Swastika Banerjee, Shyue Ping Ong · 2020 · Chemical Reviews
23. Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra
    Kunal Ghosh, Annika Stuke, Milica Todorović, Peter Bjørn Jørgensen, Mikkel Nørgaard Schmidt, Aki Vehtari, Patrick Rinke · 2019 · Advanced Science
24. Predicting Catalytic Activity of Nanoparticles by a DFT-Aided Machine-Learning Algorithm
    Ryosuke Jinnouchi, Ryoji Asahi · 2017 · The Journal of Physical Chemistry Letters
25. Complex strengthening mechanisms in the NbMoTaW multi-principal element alloy
    Xiangguo Li, Chi Chen, Hui Zheng, Yunxing Zuo, Shyue Ping Ong · 2020 · npj Computational Materials
26. Descriptors representing two- and three-body atomic distributions and their effects on the accuracy of machine-learned inter-atomic potentials
    Ryosuke Jinnouchi, Ferenc Karsai, Carla Verdi, Ryoji Asahi, Georg Kresse · 2020 · The Journal of Chemical Physics
27. Performant implementation of the atomic cluster expansion (PACE) and application to copper and silicon
    Yury V. Lysogorskiy, Cas van der Oord, Anton S. Bochkarev, Sarath Kumar Menon, Matteo Rinaldi, Thomas Hammerschmidt, Matous Mrovec, Aidan P. Thompson, Gábor Cśanyi, Christoph Ortner et al. · 2021 · npj Computational Materials
28. Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation
    Rui Fang Ding, Junhong Chen, Yuxin Chen, Jianguo Liu, Yoshio Bando, Xuebin Wang · 2024 · Chemical Society Reviews
29. Machine learning hydrogen adsorption on nanoclusters through structural descriptors
    Marc O. J. Jäger, Eiaki V. Morooka, Filippo Federici Canova, Lauri Himanen, Adam Stuart Foster · 2018 · npj Computational Materials
30. Autonomous Discovery in the Chemical Sciences Part II: Outlook
    Connor W. Coley, Natalie S. Eyke, Klavs F. Jensen · 2019 · Angewandte Chemie International Edition
31. Electronic-structure methods for materials design
    Nicola Marzari, Andrea Ferretti, Chris M. Wolverton · 2021 · Nature Materials
32. A deep potential model with long-range electrostatic interactions
    Linfeng Zhang, Han Wang, Maria Carolina Muniz, Athanassios Z. Panagiotopoulos, Roberto Car, E Weinan · 2022 · The Journal of Chemical Physics
33. sGDML: Constructing accurate and data efficient molecular force fields using machine learning
    Stefan Chmiela, Huziel E. Sauceda, Igor Poltavsky, Klaus‐Robert Müller, Alexandre Tkatchenko · 2019 · Computer Physics Communications
34. Atomistic calculations and materials informatics: A review
    Logan T. Ward, Chris M. Wolverton · 2016 · Current Opinion in Solid State and Materials Science
35. Quantum Machine Learning in Chemical Compound Space
    O. Anatole von Lilienfeld · 2017 · Angewandte Chemie International Edition
36. Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation
    Çiğdem Altıntaş, Ömer Faruk Altundal, Seda Keskın, Ramazan Yıldırım · 2021 · Journal of Chemical Information and Modeling
37. Theoretical prediction on thermal and mechanical properties of high entropy (Zr0.2Hf0.2Ti0.2Nb0.2Ta0.2)C by deep learning potential
    Fu‐Zhi Dai, Bo Wen, Yinjie Sun, Huimin Xiang, Yanchun Zhou · 2020 · Journal of Material Science and Technology
38. Machine Learning Interatomic Potentials and Long-Range Physics
    Dylan M. Anstine, Olexandr Isayev · 2023 · The Journal of Physical Chemistry A
39. Quantum machine learning using atom-in-molecule-based fragments selected on the fly
    Bing Huang, O. Anatole von Lilienfeld · 2020 · Nature Chemistry
40. A universal strategy for the creation of machine learning-based atomistic force fields
    Tran Doan Huan, Rohit Batra, James Chapman, Sridevi Krishnan, Lihua Chen, Rampi Ramprasad · 2017 · npj Computational Materials
41. Parallel Multistream Training of High-Dimensional Neural Network Potentials
    Andreas Singraber, Tobias Morawietz, Jörg Behler, Christoph Dellago · 2019 · Journal of Chemical Theory and Computation
42. Extending the accuracy of the SNAP interatomic potential form
    Mitchell A. Wood, Aidan P. Thompson · 2018 · The Journal of Chemical Physics
43. Applications of machine learning in metal-organic frameworks
    Sanggyu Chong, Sangwon Lee, Baekjun Kim, Jihan Kim · 2020 · Coordination Chemistry Reviews
44. Cormorant: Covariant Molecular Neural Networks
    Brandon Anderson, Truong-Son Hy, Risi Kondor · 2019 · arXiv (Cornell University)
45. Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties
    O. Anatole von Lilienfeld, Raghunathan Ramakrishnan, Matthias Rupp, Aaron M. Knoll · 2015 · International Journal of Quantum Chemistry
46. Directional Message Passing for Molecular Graphs
    Johannes Klicpera, Janek Groß, Stephan Günnemann · 2020 · arXiv (Cornell University)
47. Machine learning for the structure–energy–property landscapes of molecular crystals
    Musil, Félix, De, Sandip, Yang, Jack, Campbell, Joshua E., Day, Graeme M., Ceriotti, Michele · 2018 · Chemical Science
48. The 2020 magnetism roadmap
    E. Y. Vedmedenko, Roland Kenji Kawakami, Denis D. Sheka, Pietro Gambardella, Andrei I. Kirilyuk, Atsufumi Hirohata, Christian Binek, O. Chubykalo‐Fesenko, Stefano Sanvito, Brian J. Kirby et al. · 2020 · Journal of Physics D Applied Physics
49. Development of a machine learning potential for graphene
    Patrick Rowe, Gábor Cśanyi, Dario Alfè, Angelos Michaelides · 2018 · Physical review. B./Physical review. B
50. Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems
    Paraskevi Gkeka, Gabriel Stoltz, Amir Barati Farimani, Zineb Belkacemi, Michele Ceriotti, John D. Chodera, Aaron R. Dinner, Andrew L. Ferguson, Jean‐Bernard Maillet, Hervé Minoux et al. · 2020 · Journal of Chemical Theory and Computation
51. Accurate interatomic force fields via machine learning with covariant kernels
    Aldo Glielmo, Peter Sollich A, Alessandro De Vita · 2017 · Physical review. B./Physical review. B
52. Search for Catalysts by Inverse Design: Artificial Intelligence, Mountain Climbers, and Alchemists
    Jessica G. Freeze, H. Ray Kelly, Víctor S. Batista · 2019 · Chemical Reviews
53. Data-Driven Learning of Total and Local Energies in Elemental Boron
    Volker L. Deringer, Chris J. Pickard, Gábor Cśanyi · 2018 · Physical Review Letters
54. Machine learning potentials for extended systems: a perspective
    Jörg Behler, Gábor Cśanyi · 2021 · The European Physical Journal B
55. Accurate molecular polarizabilities with coupled cluster theory and machine learning
    Wilkins, David M., Grisafi, Andrea, Yang, Yang, Lao, Ka Un, DiStasio, Robert A., Ceriotti, Michele · 2019 · Proceedings of the National Academy of Sciences
56. On-the-Fly Active Learning of Interatomic Potentials for Large-Scale Atomistic Simulations
    Ryosuke Jinnouchi, Kazutoshi Miwa, Ferenc Karsai, Georg Kresse, Ryoji Asahi · 2020 · The Journal of Physical Chemistry Letters
57. Machine-learning approach for one- and two-body corrections to density functional theory: Applications to molecular and condensed water
    Albert P. Bartók, Michael J. Gillan, Frederick R. Manby, Gábor Cśanyi · 2013 · Physical Review B
58. Historical review of computer simulation of radiation effects in materials
    K. Nordlund · 2019 · Journal of Nuclear Materials
59. Evaluation of the MACE force field architecture: From medicinal chemistry to materials science
    Dávid Péter Kovács, Ilyes Batatia, Eszter Sára Arany, Gábor Cśanyi · 2023 · The Journal of Chemical Physics
60. Ab Initio Machine Learning in Chemical Compound Space
    Bing Huang, O. Anatole von Lilienfeld · 2021 · Chemical Reviews
61. De novo exploration and self-guided learning of potential-energy surfaces
    Noam Bernstein, Gábor Cśanyi, Volker L. Deringer · 2019 · npj Computational Materials
62. High-Fidelity Potential Energy Surfaces for Gas-Phase and Gas–Surface Scattering Processes from Machine Learning
    Bin Jiang, Jun Li, Hua Guo · 2020 · The Journal of Physical Chemistry Letters
63. Learning scheme to predict atomic forces and accelerate materials simulations
    Venkatesh Botu, Rampi Ramprasad · 2015 · Physical Review B
64. Ab initio phase diagram and nucleation of gallium
    Haiyang Niu, Luigi Bonati, Pablo M. Piaggi, Michele Parrinello · 2020 · Nature Communications
65. Modeling the formation and growth of atmospheric molecular clusters: A review
    Jonas Elm, Jakub Kubečka, Vitus Besel, Matias J. Jääskeläinen, Roope Halonen, Theo Kurtén, Hanna Vehkamäki · 2020 · Journal of Aerosol Science
66. Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide
    Sivaraman, Ganesh, Krishnamoorthy, Anand Narayanan, Baur, Matthias, Holm, Christian, Stan, Marius, Csányi, Gábor, Benmore, Chris, Vázquez-Mayagoitia, Álvaro · 2020 · npj Computational Materials
67. Artificial intelligence to bring nanomedicine to life
    Nikita Serov, Vladimir V. Vinogradov · 2022 · Advanced Drug Delivery Reviews
68. Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges
    Guang Chen, Zhiqiang Shen, Akshay Iyer, Umar Farooq Ghumman, Shan Tang, Jinbo Bi, Wei Chen, Ying Li · 2020 · Polymers
69. AI Applications through the Whole Life Cycle of Material Discovery
    Jiali Li, Kaizhuo Lim, Haitao Yang, Zekun Ren, S. Raghavan, Po‐Yen Chen, Tonio Buonassisi, Xiaonan Wang · 2020 · Matter
70. Data‐Driven Materials Innovation and Applications
    Zhuo Wang, Zhehao Sun, Hang Yin, Xinghui Liu, Jinlan Wang, Haitao Zhao, Cheng Heng Pang, Tao Wu, Shuzhou Li, Zongyou Yin et al. · 2022 · Advanced Materials
71. Incompleteness of Atomic Structure Representations
    Sergey N. Pozdnyakov, Michael J. Willatt, Albert P. Bartók, Christoph Ortner, Gábor Cśanyi, Michele Ceriotti · 2020 · Physical Review Letters
72. How Machine Learning Will Revolutionize Electrochemical Sciences
    Aashutosh N. Mistry, Alejandro A. Franco, Samuel J. Cooper, Scott Alan Roberts, Venkatasubramanian Viswanathan · 2021 · ACS Energy Letters
73. Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials
    Bohayra Mortazavi · 2024 · Advanced Energy Materials
74. Identifying domains of applicability of machine learning models for materials science
    Christopher A. Sutton, Mario Boley, Luca M. Ghiringhelli, Matthias Rupp, Jilles Vreeken, Matthias Scheffler · 2020 · Nature Communications
75. Machine learning in chemical reaction space
    Sina Stocker, Gábor Cśanyi, Karsten Reuter, Johannes T. Margraf · 2020 · Nature Communications
76. Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics
    Rama Krishnan Vasudevan, Kamal Kumar Choudhary, Apurva Mehta, Ryan P. Smith, Aaron Gilad Kusne, Francesca Tavazza, Lukáš Vlček, Maxim A Ziatdinov, Sergei V. Kalinin, Jason R. Hattrick-Simpers · 2019 · MRS Communications
77. Choosing the right molecular machine learning potential
    Max Pinheiro, Fuchun Ge, Nicolas Ferré, Pavlo O. Dral, Mario Barbatti · 2021 · Chemical Science
78. Roadmap on Machine learning in electronic structure
    Heather J. Kulik, Thomas Hammerschmidt, Jonathan Schmidt, Silvana Botti, Miguel A. L. Marques, Mario Boley, Matthias Scheffler, Milica Todorović, Patrick Rinke, Corey Oses et al. · 2022 · Electronic Structure
79. Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns
    Tânia F. G. G. Cova, Alberto A. C. C. Pais · 2019 · Frontiers in Chemistry
80. Machine Learning for Atomic Simulation and Activity Prediction in Heterogeneous Catalysis: Current Status and Future
    Sicong Ma, Zhi‐Pan Liu · 2020 · ACS Catalysis
81. DPA-2: a large atomic model as a multi-task learner
    Duo Zhang, Xinzijian Liu, Xiangyu Zhang, Chengqian Zhang, Chun Cai, Hangrui Bi, Yiming Du, Xuejian Qin, Anyang Peng, Jiameng Huang et al. · 2024 · npj Computational Materials
82. Machine-learned multi-system surrogate models for materials prediction
    Chandramouli Nyshadham, Matthias Rupp, Brayden Bekker, Alexander V. Shapeev, Tim Mueller, Conrad W. Rosenbrock, Gábor Cśanyi, David Wingate, Gus L. W. Hart · 2019 · npj Computational Materials
83. Machine learning assisted high-throughput screening of transition metal single atom based superb hydrogen evolution electrocatalysts
    Muhammad Umer, Sohaib Umer, Mohammad Zafari, Miran Ha, Rohit Anand, Amir Hajibabaei, Ather Abbas, Geunsik Lee, Kwang S. Kim · 2022 · Journal of Materials Chemistry A
84. Database of Two-Dimensional Hybrid Perovskite Materials: Open-Access Collection of Crystal Structures, Band Gaps, and Atomic Partial Charges Predicted by Machine Learning
    Ekaterina I. Marchenko, Sergey A. Fateev, Andrey A. Petrov, Vadim Korolev, Artem A. Mitrofanov, Andrey V. Petrov, Eugene Alekseevich Goodilin, Alexey B. Tarasov · 2020 · Chemistry of Materials
85. Machine learning with force-field-inspired descriptors for materials: Fast screening and mapping energy landscape
    Kamal Kumar Choudhary, Brian DeCost, Francesca Tavazza · 2018 · Physical Review Materials
86. End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems
    Linfeng Zhang, Jiequn Han, Han Wang, Wissam Abdo Saidi, Roberto Car, E Weinan · 2018 · arXiv (Cornell University)
87. Unsupervised machine learning in atomistic simulations, between predictions and understanding
    Ceriotti, Michele · 2019 · The Journal of Chemical Physics
88. Neural Network-Assisted Development of High-Entropy Alloy Catalysts: Decoupling Ligand and Coordination Effects
    Zhuole Lu, Zhiwen Chen, Chandra Veer Singh · 2020 · Matter
89. Retrospective on a decade of machine learning for chemical discovery
    O. Anatole von Lilienfeld, Kieron Burke · 2020 · Nature Communications
90. Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions
    Nguyen, Thuong T., Székely, Eszter, Imbalzano, Giulio, Behler, Jörg, Csányi, Gábor, Ceriotti, Michele, Götz, Andreas W., Paesani, Francesco · 2018 · The Journal of Chemical Physics
91. Recent advances in machine learning towards multiscale soft materials design
    Nicholas E. Jackson, Michael A. Webb, Juan Pablo · 2019 · Current Opinion in Chemical Engineering
92. Learning grain boundary segregation energy spectra in polycrystals
    Malik Wagih, Peter Mahler Larsen, Christopher Allan Schuh · 2020 · Nature Communications
93. MOFormer: Self-Supervised Transformer Model for Metal–Organic Framework Property Prediction
    Zhonglin Cao, Rishikesh Magar, Yuyang Wang, Amir Barati Farimani · 2023 · Journal of the American Chemical Society
94. Revealing the intrinsic nature of the mid-gap defects in amorphous Ge2Sb2Te5
    Konstantinos Konstantinou, Felix C. Mocanu, Tae-Hoon Lee, Stephen R. Elliott · 2019 · Nature Communications
95. When Machine Learning Meets 2D Materials: A Review
    Bin Lu, Yuze Xia, Yuqian Ren, Xie Miaomiao, Liguo Zhou, Giovanni Vinai, Simon A. Morton, Andrew T. S. Wee, Wilfred G. van der Wiel, Wen Zhang et al. · 2024 · Advanced Science
96. Scoring Functions for Protein-Ligand Binding Affinity Prediction Using Structure-based Deep Learning: A Review
    Rocco Meli, Garrett M. Morris, Philip Charles Biggin · 2022 · Frontiers in Bioinformatics
97. Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization
    Christos Xiouras, Fabio Cameli, Gustavo Lunardon Quilló, Michail E. Kavousanakis, Dionisios G. Vlachos, Georgios D. Stefanidis · 2022 · Chemical Reviews
98. Accurate force field for molybdenum by machine learning large materials data
    Chi Chen, Zhi Deng, Richard Tran, Hanmei Tang, Iek‐Heng Chu, Shyue Ping Ong · 2017 · Physical Review Materials
99. Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry
    Nandy, Aditya, Duan, Chenru, Janet, Jon Paul, Gugler, Stefan, Kulik, Heather J. · 2018 · Industrial & Engineering Chemistry Research
100. ARC–MOF: A Diverse Database of Metal-Organic Frameworks with DFT-Derived Partial Atomic Charges and Descriptors for Machine Learning
     Jake Burner, Jun Luo, Andrew White, Adam Mirmiran, Ohmin Kwon, Peter G. Boyd, Steven M. Maley, Marco Gibaldi, Scott Simrod, Victoria Ogden et al. · 2023 · Chemistry of Materials