Active meta-learning for predicting and selecting perovskite crystallization experiments-Reference-Cited by-同舟云学术

Active meta-learning for predicting and selecting perovskite crystallization experiments

Published:2022-02-14 Issue:6 Volume:156 Page:064108
ISSN:0021-9606
Container-title:The Journal of Chemical Physics
language:en
Short-container-title:J. Chem. Phys.

Author:

Shekar Venkateswaran¹^ORCID,Nicholas Gareth¹,Najeeb Mansoor Ani²^ORCID,Zeile Margaret²^ORCID,Yu Vincent¹,Wang Xiaorong¹,Slack Dylan¹,Li Zhi³,Nega Philip W.³,Chan Emory M.³^ORCID,Norquist Alexander J.²^ORCID,Schrier Joshua⁴^ORCID,Friedler Sorelle A.¹^ORCID

Affiliation:

1. Department of Computer Science, Haverford College, 370 Lancaster Avenue, Haverford, Pennsylvania 19041, USA

2. Department of Chemistry, Haverford College, 370 Lancaster Avenue, Haverford, Pennsylvania 19041, USA

3. The Molecular Foundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, California 94720, USA

4. Department of Chemistry, Fordham University, 441 E. Fordham Road, Bronx, New York 10458, USA

Funder

Defense Advanced Research Projects Agency

U.S. Department of Energy

Camille and Henry Dreyfus Foundation

National Science Foundation

Publisher

AIP Publishing

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

https://aip.scitation.org/doi/am-pdf/10.1063/5.0076636

Reference67 articles.

1. Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics

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