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  SchNet – A deep learning architecture for molecules and materials

Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A., & Müller, K.-R. (2018). SchNet – A deep learning architecture for molecules and materials. The Journal of Chemical Physics, 148(24):. doi:10.1063/1.5019779.

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アイテムのパーマリンク: https://hdl.handle.net/21.11116/0000-0001-3BD6-C 版のパーマリンク: https://hdl.handle.net/21.11116/0000-0001-4FE6-4
資料種別: 学術論文

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 作成者:
Schütt, K. T.1, 著者
Sauceda, Huziel E.2, 著者           
Kindermans, P.-J.1, 著者
Tkatchenko, Alexandre3, 著者           
Müller, Klaus-Robert1, 4, 5, 著者           
所属:
1Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany, ou_persistent22              
2Theory, Fritz Haber Institute, Max Planck Society, ou_634547              
3Physics and Materials Science Research Unit, University of Luxembourg, ou_persistent22              
4Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society, ou_40046              
5Department of Brain and Cognitive Engineering, Korea University, Anam-dong, Seongbuk-gu, Seoul 136-713, South Korea, ou_persistent22              

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キーワード: -
 要旨: Deep learning has led to a paradigm shift in artificial intelligence, including web, text, and image search, speech recognition, as well as bioinformatics, with growing impact in chemical physics. Machine learning, in general, and deep learning, in particular, are ideally suitable for representing quantum-mechanical interactions, enabling us to model nonlinear potential-energy surfaces or enhancing the exploration of chemical compound space. Here we present the deep learning architecture SchNet that is specifically designed to model atomistic systems by making use of continuous-filter convolutional layers. We demonstrate the capabilities of SchNet by accurately predicting a range of properties across chemical space for molecules and materials, where our model learns chemically plausible embeddings of atom types across the periodic table. Finally, we employ SchNet to predict potential-energy surfaces and energy-conserving force fields for molecular dynamics simulations of small molecules and perform an exemplary study on the quantum-mechanical properties of C20-fullerene that would have been infeasible with regular ab initio molecular dynamics.

資料詳細

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言語: eng - English
 日付: 2017-12-162018-03-082018-03-292018-06-18
 出版の状態: 出版
 ページ: 11
 出版情報: -
 目次: -
 査読: 査読あり
 識別子(DOI, ISBNなど): DOI: 10.1063/1.5019779
 学位: -

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Project information

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Project name : BeStMo - Beyond Static Molecules: Modeling Quantum Fluctuations in Complex Molecular Environments
Grant ID : 725291
Funding program : Horizon 2020 (H2020)
Funding organization : European Commission (EC)
Project name : ZERO-TRAIN-BCI - Combining constrained based learning and transfer learning to facilitate Zero-training Brain-Computer Interfacing
Grant ID : 657679
Funding program : Horizon 2020 (H2020)
Funding organization : European Commission (EC)

出版物 1

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出版物名: The Journal of Chemical Physics
  その他 : J. Chem. Phys.
種別: 学術雑誌
 著者・編者:
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出版社, 出版地: Woodbury, N.Y. : American Institute of Physics
ページ: 11 巻号: 148 (24) 通巻号: 241722 開始・終了ページ: - 識別子(ISBN, ISSN, DOIなど): ISSN: 0021-9606
CoNE: https://pure.mpg.de/cone/journals/resource/954922836226