Predicting the Formability of Hybrid Organic–Inorganic Perovskites via an Interpretable Machine Learning Strategy
Author:
Affiliation:
1. Department of Chemistry, College of Sciences, Shanghai University, Shanghai 200444, China
2. Materials Genome Institute, Shanghai University, Shanghai 200444, China
Funder
Science and Technology Commission of Shanghai Municipality
National Key Research and Development Program of China
Publisher
American Chemical Society (ACS)
Subject
General Materials Science,Physical and Theoretical Chemistry
Link
https://pubs.acs.org/doi/pdf/10.1021/acs.jpclett.1c01939
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1. Oxide perovskites, double perovskites and derivatives for electrocatalysis, photocatalysis, and photovoltaics
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5. Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning
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