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Machine learning meets chemical physics

Published:2021-04-28 Issue:16 Volume:154 Page:160401
ISSN:0021-9606
Container-title:The Journal of Chemical Physics
language:en
Short-container-title:J. Chem. Phys.

Author:

Ceriotti Michele¹^ORCID,Clementi Cecilia²^ORCID,Anatole von Lilienfeld O.³^ORCID

Affiliation:

1. Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland

2. Department of Physics, Freie Universität Berlin, Arnimallee 14, 14195 Berlin, Germany

3. Faculty of Physics, University of Vienna, Kolingasse 14-18, 1090 Vienna, Austria

Publisher

AIP Publishing

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

https://aip.scitation.org/doi/pdf/10.1063/5.0051418

Reference72 articles.

1. Guest Editorial: Special Topic on Data-Enabled Theoretical Chemistry

2. A look inside the black box: Using graph-theoretical descriptors to interpret a Continuous-Filter Convolutional Neural Network (CF-CNN) trained on the global and local minimum energy structures of neutral water clusters

3. Descriptors representing two- and three-body atomic distributions and their effects on the accuracy of machine-learned inter-atomic potentials

4. The effect of descriptor choice in machine learning models for ionic liquid melting point prediction

5. Sensitivity and dimensionality of atomic environment representations used for machine learning interatomic potentials

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4. Accuracy, Performance, and Transferability of Interparticle Potentials for Al–Cu Alloys: Comparison of Embedded Atom and Deep Machine Learning Models;Journal of Experimental and Theoretical Physics;2023-12

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