Structure-Based Synthesizability Prediction of Crystals Using Partially Supervised Learning-Reference-Cited by-同舟云学术

Structure-Based Synthesizability Prediction of Crystals Using Partially Supervised Learning

Published:2020-10-26 Issue:44 Volume:142 Page:18836-18843
ISSN:0002-7863
Container-title:Journal of the American Chemical Society
language:en
Short-container-title:J. Am. Chem. Soc.

Author:

Jang Jidon¹,Gu Geun Ho¹,Noh Juhwan¹,Kim Juhwan¹,Jung Yousung¹^ORCID

Affiliation:

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

Publisher

American Chemical Society (ACS)

Subject

Colloid and Surface Chemistry,Biochemistry,General Chemistry,Catalysis

Link

https://pubs.acs.org/doi/pdf/10.1021/jacs.0c07384

Reference53 articles.

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2. Phosphates as Lithium-Ion Battery Cathodes: An Evaluation Based on High-Throughput ab Initio Calculations

3. Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach

4. Towards the computational design of solid catalysts

5. Robust and synthesizable photocatalysts for CO2 reduction: a data-driven materials discovery

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