Perspective on integrating machine learning into computational chemistry and materials science-Reference-Cited by-同舟云学术

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Perspective on integrating machine learning into computational chemistry and materials science

Published:2021-06-21 Issue:23 Volume:154 Page:230903
ISSN:0021-9606
Container-title:The Journal of Chemical Physics
language:en
Short-container-title:J. Chem. Phys.

Author:

Westermayr Julia¹^ORCID,Gastegger Michael²^ORCID,Schütt Kristof T.²³^ORCID,Maurer Reinhard J.¹^ORCID

Affiliation:

1. Department of Chemistry, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, United Kingdom

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

3. Berlin Institute for the Foundations of Learning and Data, 10587 Berlin, Germany

Funder

Austrian Science Fund

Berlin Center for Machine Learning

BASLEARN–TU Berlin/BASF Joint Lab for Machine Learning

UK Research and Innovation

Publisher

AIP Publishing

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

https://aip.scitation.org/doi/pdf/10.1063/5.0047760

Reference304 articles.

1. The CECAM electronic structure library and the modular software development paradigm

2. ELSI — An open infrastructure for electronic structure solvers

3. Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces

4. Fingerprints for Spin-Selection Rules in the Interaction Dynamics ofO2at Al(111)

5. Interpolating moving least-squares methods for fitting potential energy surfaces: A strategy for efficient automatic data point placement in high dimensions

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