Discovery of Quaternary Perovskites via Stacked Machine Learning and Generative Models

This study explores machine learning-driven prediction of perovskite cathode materials for solid oxide fuel cells (SOFCs) by analyzing formation energy and bandgap properties. Addressing experimental challenges in material discovery, we developed a predictive framework using 602,000 compounds with f...

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Detalhes bibliográficos

Principais autores:	Ruichen Tian, Aldrin D. Calderon, Xiaoyu Liu, Hui Fu
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Engineered Science Publisher LLC 2025-06-01
Colecção:	Engineered Science
Assuntos:	perovskites materials design materials properties prediction machine learning stacking model variational autoencoders
Acesso em linha:	https://www.espublisher.com/journals/articlehtml/engineered-science/10.30919-es1561
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