Quantum-chemical insights from deep tensor neural networks

Schütt, Kristof T., Arbabzadah, Farhad, Chmiela, Stefan, Müller, Klaus R., Tkatchenko, Alexandre·2017·Nature Communications

Cited by (1,001)

Items 301–400 of 1,001

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  3. Deeply learning molecular structure-property relationships using attention- and gate-augmented graph convolutional network
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  4. Atomic structures and orbital energies of 61,489 crystal-forming organic molecules
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  5. Machine-learning atomic simulation for heterogeneous catalysis
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  6. Strategies for the construction of machine-learning potentials for accurate and efficient atomic-scale simulations
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  8. Semi-local machine-learned kinetic energy density functional with third-order gradients of electron density
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  9. Active learning accelerates ab initio molecular dynamics on reactive energy surfaces
    Shi Jun Ang, Wujie Wang, Daniel Schwalbe‐Koda, Simon Axelrod, Rafael Gómez‐Bombarelli · 2021 · Chem
  10. <i>ChemML</i> : A machine learning and informatics program package for the analysis, mining, and modeling of chemical and materials data
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  11. Accurate Many-Body Repulsive Potentials for Density-Functional Tight Binding from Deep Tensor Neural Networks
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  12. The Rise of Neural Networks for Materials and Chemical Dynamics
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  13. A Graph to Graphs Framework for Retrosynthesis Prediction
    Chence Shi, Minkai Xu, Hongyu Guo, Ming Zhang, Jian Tang · 2020 · arXiv (Cornell University)
  14. 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
  15. Investigation on the ignition delay prediction model of multi-component surrogates based on back propagation (BP) neural network
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  16. Machine Learning of Reactive Potentials
    Yinuo Yang, Shuhao Zhang, Kavindri D. Ranasinghe, Olexandr Isayev, Adrián E. Roitberg · 2024 · Annual Review of Physical Chemistry
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  18. 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
  19. 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
  20. A transferable active-learning strategy for reactive molecular force fields
    Tom A. Young, Tristan Johnston-Wood, Volker L. Deringer, Fernanda Duarte · 2021 · Chemical Science
  21. Encoding the atomic structure for machine learning in materials science
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  22. Rational Design of Earth‐Abundant Catalysts toward Sustainability
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  23. Solid harmonic wavelet scattering for predictions of molecule properties
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    Andrew James Lew, Chi‐Hua Yu, Yu-Chuan Hsu, Markus J. Buehler · 2021 · npj 2D Materials and Applications
  25. AI-Driven Inverse Design of Materials: Past, Present, and Future
    Xu Han, Xin-De Wang, Meng-Yuan 孟圆 Xu 徐, Feng Zhen, Bo-Wen 博文 Yao 姚, Peng-Jie Guo, Ze-Feng Gao, Zhong-Yi Lu · 2025 · Chinese Physics Letters
  26. 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
  27. Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
    Niklas W. A. Gebauer, Michael Gastegger, Kristof T. Schütt · 2019 · arXiv (Cornell University)
  28. 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
  29. 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
  30. Hydrogen adsorption on doped MoS2 nanostructures
    Mikko O. Hakala, Rasmus Kronberg, Kari E. Laasonen · 2017 · Scientific Reports
  31. Machine learning activation energies of chemical reactions
    Toby Lewis‐Atwell, Piers A. Townsend, Matthew N. Grayson · 2021 · Wiley Interdisciplinary Reviews Computational Molecular Science
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    Yuyang He, You Zhou, Tao Wen, Shuang Zhang, Fang Huang, Xinyu Zou, Xiaogang Ma, Yueqin Zhu · 2022 · Applied Geochemistry
  33. Predicting chemical shifts with graph neural networks
    Ziyue Yang, Maghesree Chakraborty, Andrew D. White · 2021 · Chemical Science
  34. 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
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    J. Thorben Frank, Oliver T. Unke, Klaus‐Robert Müller, Stefan Chmiela · 2024 · Nature Communications
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    Hao Wang, Xun Guo, Linfeng Zhang, Han Wang, Jianming Xue · 2019 · Applied Physics Letters
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    Yang Zhou, Zachary While, Evangelos Kalogerakis · 2019 · IEEE/CVF International Conference on Computer Vision (ICCV)
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    Daniel Schwalbe‐Koda, Aik Rui Tan, Rafael Gómez‐Bombarelli · 2021 · Nature Communications
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    Michael Lutter, Jan Peters · 2023 · The International Journal of Robotics Research
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    Gregor N. C. Simm, Markus Reiher · 2018 · Journal of Chemical Theory and Computation
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    Adam S. Abbott, Justin M. Turney, Boyi Zhang, Daniel G. A. Smith, Doaa Altarawy, Henry F. Schaefer · 2019 · Journal of Chemical Theory and Computation
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    Alon Henson, Piotr S. Gromski, Leroy Cronin · 2018 · ACS Central Science
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  59. 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
  60. Metadynamics for training neural network model chemistries: A competitive assessment
    John E. Herr, Kun Yao, Ryker Mcintyre, David W. Toth, John Parkhill · 2018 · The Journal of Chemical Physics
  61. 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
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  64. Advancing materials discovery through artificial intelligence
    Martin Otyepka, Martin Pykal, Michal Otyepka · 2025 · Applied Materials Today
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    Simon Wiedemann, Klaus‐Robert Müller, Wojciech Samek · 2019 · IEEE Transactions on Neural Networks and Learning Systems
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  71. How Much Chemistry Does a Deep Neural Network Need to Know to Make Accurate Predictions?
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  72. Machine Learning for Molecular Dynamics on Long Timescales
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  74. Advancing drug discovery via GPU-based deep learning
    Erik Gawehn, Jan A. Hiss, J.B. Brown, Gisbert Schneider · 2018 · Expert Opinion on Drug Discovery
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    Julia Westermayr, Philipp Marquetand · 2020 · Machine Learning Science and Technology
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    Akash Ashok Deshmukh, Raghavan Ranganathan · 2024 · Journal of Material Science and Technology
  78. Mind and machine in drug design
    Gisbert Schneider · 2019 · Nature Machine Intelligence
  79. AP-Net: An atomic-pairwise neural network for smooth and transferable interaction potentials
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  83. Neural Message Passing for NMR Chemical Shift Prediction
    Youngchun Kwon, Dongseon Lee, Youn-Suk Choi, Myeonginn Kang, Seokho Kang · 2020 · Journal of Chemical Information and Modeling
  84. Graph Kernels
    Karsten Michael Borgwardt, Elisabetta Ghisu, Felipe Llinares-López, Leslie O’Bray, Bastian Rieck · 2020 · Foundations and Trends® in Machine Learning
  85. Enhanced Deep‐Learning Prediction of Molecular Properties via Augmentation of Bond Topology
    Hyeoncheol Cho, Insung S. Choi · 2019 · ChemMedChem
  86. Applying a machine learning interatomic potential to unravel the effects of local lattice distortion on the elastic properties of multi-principal element alloys
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  87. Predicting molecular properties with covariant compositional networks
    Truong Son Hy, Shubhendu Trivedi, Horace Pan, Brandon Anderson, Risi Kondor · 2018 · The Journal of Chemical Physics
  88. 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
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    Zijing Xie, Yunjun Yao, Qingxin Tang, Meng Liu, Joshua B. Fisher, Jiquan Chen, Xiaotong Zhang, Kun Jia, Yufu Li, Ke Shang et al. · 2024 · Journal of Hydrology
  91. Electronic structure at coarse-grained resolutions from supervised machine learning
    Nicholas E. Jackson, Alec Bowen, Lucas W. Antony, Michael A. Webb, Venkatram Vishwanath, Juan Pablo · 2019 · Science Advances
  92. Chemical space exploration guided by deep neural networks
    Dmitry S. Karlov, Sergey Sosnin, Igor V. Tetko, Maxim V. Fedorov · 2019 · RSC Advances
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    J. A. Ellis, Lenz Fiedler, Gabriel A Popoola, Normand A. Modine, J. A. Stephens, Aidan P. Thompson, Attila Cangi, Sivasankaran Rajamanickam · 2021 · Physical review. B./Physical review. B
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