Comparing molecules and solids across structural and alchemical space-Reference-Cited by-同舟云学术

Comparing molecules and solids across structural and alchemical space

Published:2016 Issue:20 Volume:18 Page:13754-13769
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

De Sandip¹²³⁴⁵,Bartók Albert P.⁶⁷⁸⁹,Csányi Gábor⁶⁷⁸⁹,Ceriotti Michele¹²³⁴⁵

Affiliation:

1. National Center for Computational Design and Discovery of Novel Materials (MARVEL)

2. Switzerland

3. Laboratory of Computational Science and Modelling

4. Institute of Materials

5. Ecole Polytechnique Fédérale de Lausanne

6. Engineering Laboratory

7. University of Cambridge

8. Cambridge CB2 1PZ

9. UK

Abstract

A general procedure to compare molecules and materials powers insightful representations of energy landscapes and precise machine-learning predictions of properties.

Funder

Isaac Newton Trust

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://pubs.rsc.org/en/content/articlepdf/2016/CP/C6CP00415F

Reference58 articles.

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2. Big Data of Materials Science: Critical Role of the Descriptor

3. Accelerated materials property predictions and design using motif-based fingerprints

4. Learning scheme to predict atomic forces and accelerate materials simulations

5. On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets

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