SchNetPack: A Deep Learning Toolbox For Atomistic Systems-Reference-Cited by-同舟云学术

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SchNetPack: A Deep Learning Toolbox For Atomistic Systems

Published:2018-11-27 Issue:1 Volume:15 Page:448-455
ISSN:1549-9618
Container-title:Journal of Chemical Theory and Computation
language:en
Short-container-title:J. Chem. Theory Comput.

Author:

Schütt K. T.¹^ORCID,Kessel P.¹,Gastegger M.¹,Nicoli K. A.¹,Tkatchenko A.²^ORCID,Müller K.-R.¹³⁴

Affiliation:

1. Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany

2. Physics and Materials Science Research Unit, University of Luxembourg, L-1511 Luxembourg, Luxembourg

3. Department of Brain and Cognitive Engineering, Korea University, Anam-dong, Seongbuk-gu, Seoul 02841, South Korea

4. Max-Planck-Institut für Informatik, Saarbrücken, Germany

Funder

Bundesministerium f?r Bildung und Forschung

H2020 European Research Council

Institute for Information & Communications Technology Promotion

Korean National Research Foundation

Korea MSIT

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry,Computer Science Applications

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.8b00908

Reference41 articles.

1. Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons

2. Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

3. How to represent crystal structures for machine learning: Towards fast prediction of electronic properties

4. Constructing high-dimensional neural network potentials: A tutorial review

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