Multi-scale approach for the prediction of atomic scale properties-Reference-Cited by-同舟云学术

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Multi-scale approach for the prediction of atomic scale properties

Published:2021 Issue:6 Volume:12 Page:2078-2090
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Grisafi Andrea¹²³⁴⁵^ORCID,Nigam Jigyasa¹²³⁴⁵^ORCID,Ceriotti Michele¹²³⁴⁵^ORCID

Affiliation:

1. Laboratory of Computational Science and Modeling

2. IMX

3. École Polytechnique Fédérale de Lausanne

4. 1015 Lausanne

5. Switzerland

Abstract

Multi-scale equivariant representations overcome the nearsightedness of local machine-learning approaches.

Funder

H2020 European Research Council

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Max-Planck-Gesellschaft

École Polytechnique Fédérale de Lausanne

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2021/SC/D0SC04934D

Reference92 articles.

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

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

3. Graphite-diamond phase coexistence study employing a neural-network mapping of theab initiopotential energy surface

4. Microscopic Origins of the Anomalous Melting Behavior of Sodium under High Pressure

5. Neural network interatomic potential for the phase change material GeTe

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