Machine learning-empowered study of metastable γ-CsPbI<sub>3</sub> under pressure and strain-Reference-Cited by-同舟云学术

Machine learning-empowered study of metastable γ-CsPbI₃ under pressure and strain

Published:2024 Issue:18 Volume:12 Page:11082-11089
ISSN:2050-7488
Container-title:Journal of Materials Chemistry A
language:en
Short-container-title:J. Mater. Chem. A

Author:

Han Minkyung¹²^ORCID,Peng Cheng³,Song Ruyi³,Ke Feng³,Nashed Youssef S. G.⁴^ORCID,Mao Wendy L.¹³,Jia Chunjing⁵,Lin Yu³^ORCID

Affiliation:

1. Department of Earth and Planetary Sciences, Stanford University, Stanford, CA, 94305, USA

2. Department of Materials Science and Engineering, Stanford University, Stanford, CA, 94305, USA

3. Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA

4. SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA

5. Department of Physics, University of Florida, Gainesville, FL, 32611, USA

Abstract

We demonstrate superior performance of various machine learning models in predicting properties of metastable γ-CsPbI3 under pressure and strain, revealing the relationships between its structure and property.

Funder

U.S. Department of Energy

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/TA/D4TA00174E

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