Modeling the dielectric constants of crystals using machine learning-Reference-Cited by-同舟云学术

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Modeling the dielectric constants of crystals using machine learning

Published:2020-07-14 Issue:2 Volume:153 Page:024503
ISSN:0021-9606
Container-title:The Journal of Chemical Physics
language:en
Short-container-title:J. Chem. Phys.

Author:

Morita Kazuki¹^ORCID,Davies Daniel W.²^ORCID,Butler Keith T.³^ORCID,Walsh Aron¹⁴^ORCID

Affiliation:

1. Department of Materials, Imperial College London, London SW7 2AZ, United Kingdom

2. Department of Chemistry, University College London, London WC1H 0AJ, United Kingdom

3. SciML, Scientific Computer Division, Rutherford Appleton Laboratory, Harwell OX11 0QX, United Kingdom

4. Department of Materials Science and Engineering, Yonsei University, Seoul 03722, South Korea

Funder

Engineering and Physical Sciences Research Council

Publisher

AIP Publishing

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://aip.scitation.org/doi/pdf/10.1063/5.0013136

Reference57 articles.

1. Wave-Number-Dependent Dielectric Function of Semiconductors

2. Empirical electronic polarizabilities in oxides, hydroxides, oxyfluorides, and oxychlorides

3. Empirical electronic polarizabilities of ions for the prediction and interpretation of refractive indices: Oxides and oxysalts

4. Novel high-κ dielectrics for next-generation electronic devices screened by automated ab initio calculations

5. Searching for materials with high refractive index and wide band gap: A first-principles high-throughput study

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