Uncertainty-Quantified Hybrid Machine Learning/Density Functional Theory High Throughput Screening Method for Crystals-Reference-Cited by-同舟云学术

Uncertainty-Quantified Hybrid Machine Learning/Density Functional Theory High Throughput Screening Method for Crystals

Published:2020-03-25 Issue:4 Volume:60 Page:1996-2003
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Noh Juhwan¹,Gu Geun Ho¹,Kim Sungwon¹,Jung Yousung¹²^ORCID

Affiliation:

1. Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Daejeon 34141, Republic of Korea

2. Saudi Aramco–KAIST CO2 Management CenterKorea Advanced Institute of Science and Technology (KAIST)291 Daehak-ro, Daejeon 34141, Republic of Korea

Funder

Saudi-Aramco???KAIST CO2 Management Center

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.0c00003

Reference49 articles.

1. Machine learning for renewable energy materials

2. Computational Screening of All Stoichiometric Inorganic Materials

3. Inverse design in search of materials with target functionalities

4. Learning from the Harvard Clean Energy Project: The Use of Neural Networks to Accelerate Materials Discovery

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

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