Generative Adversarial Networks for Crystal Structure Prediction-Reference-Cited by-同舟云学术

Generative Adversarial Networks for Crystal Structure Prediction

Published:2020-07-10 Issue:8 Volume:6 Page:1412-1420
ISSN:2374-7943
Container-title:ACS Central Science
language:en
Short-container-title:ACS Cent. Sci.

Author:

Kim Sungwon¹,Noh Juhwan¹,Gu Geun Ho¹,Aspuru-Guzik Alan²³⁴^ORCID,Jung Yousung¹^ORCID

Affiliation:

1. Department of Chemical and Biomolecular Engineering, KAIST, 291 Daehak-ro, Daejeon 34141, South Korea

2. Chemical Physics Theory Group, Department of Chemistry and Department of Computer Science, University of Toronto, Toronto, Ontario M55S 3H6, Canada

3. Vector Institute for Artificial Intelligence, Toronto, Ontario M5S 1M1, Canada

4. Canadian Institute for Advanced Research (CIFAR) Lebovic Fellow, Toronto, Ontario M5S 1M1, Canada

Funder

National Research Foundation of Korea

Publisher

American Chemical Society (ACS)

Subject

General Chemical Engineering,General Chemistry

Link

http://pubs.acs.org/doi/pdf/10.1021/acscentsci.0c00426

Reference66 articles.

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4. Discovery of Manganese-Based Solar Fuel Photoanodes via Integration of Electronic Structure Calculations, Pourbaix Stability Modeling, and High-Throughput Experiments

5. First principles high throughput screening of oxynitrides for water-splitting photocatalysts

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