sGDML: Constructing accurate and data efficient molecular force fields using machine learning-Reference-Cited by-同舟云学术

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sGDML: Constructing accurate and data efficient molecular force fields using machine learning

Published:2019-07 Issue: Volume:240 Page:38-45
ISSN:0010-4655
Container-title:Computer Physics Communications
language:en
Short-container-title:Computer Physics Communications

Author:

Chmiela Stefan,Sauceda Huziel E.^ORCID,Poltavsky Igor,Müller Klaus-Robert,Tkatchenko Alexandre

Funder

Deutsche Forschungsgemeinschaft, Germany

European Research Council

German Ministry for Education and Research as Berlin Big Data Centre

Berlin Center for Machine Learning

Information & Communications Technology Planning & Evaluation (IITP)

National Science Foundation, United States

Publisher

Elsevier BV

Subject

General Physics and Astronomy,Hardware and Architecture

Reference67 articles.

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

2. Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies

3. Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

4. Machine Learning for Quantum Mechanical Properties of Atoms in Molecules

5. Solid harmonic wavelet scattering for predictions of molecule properties

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