Machine learning-empowered study of metastable γ-CsPbI 3 under pressure and strain

Han, Minkyung; Peng, Cheng; Song, Ruyi; Ke, Feng; Nashed, Youssef S. G.; Mao, Wendy L.; Jia, Chunjing; Lin, Yu

doi:10.1039/D4TA00174E

Title: Machine learning-empowered study of metastable γ-CsPbI ₃ under pressure and strain

Journal Article · 08 May 2024 · Journal of Materials Chemistry. A

DOI: https://doi.org/10.1039/D4TA00174E · OSTI ID:2335965

^[1]; Peng, Cheng ^[2]; Song, Ruyi ^[2]; Ke, Feng ^[2];

^[3]; Mao, Wendy L. ^[4]; Jia, Chunjing ^[5];

^[2]

Department of Earth and Planetary Sciences, Stanford University, Stanford, CA, 94305, USA, Department of Materials Science and Engineering, Stanford University, Stanford, CA, 94305, USA
Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA
SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA
Department of Earth and Planetary Sciences, Stanford University, Stanford, CA, 94305, USA, Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA
Department of Physics, University of Florida, Gainesville, FL, 32611, USA

We demonstrate superior performance of various machine learning models in predicting properties of metastable γ-CsPbI ₃ under pressure and strain, revealing the relationships between its structure and property.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: AC02-76SF00515; SC0022216

OSTI ID:: 2335965

Journal Information:: Journal of Materials Chemistry. A, Journal Name: Journal of Materials Chemistry. A Journal Issue: 18 Vol. 12; ISSN JMCAET; ISSN 2050-7488

Publisher:: Royal Society of Chemistry (RSC)Copyright Statement

Country of Publication:: United Kingdom

Language:: English

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