An evolutionary variational autoencoder for perovskite discovery

Machine learning (ML) techniques emerged as viable means for novel materials discovery and target property determination. At the vanguard of discoverable energy materials are perovskite crystalline materials, which are known for their robust design space and multifunctionality. Previous efforts for...

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Veröffentlicht in:	Frontiers in materials Jg. 10
Hauptverfasser:	Chenebuah, Ericsson Tetteh, Nganbe, Michel, Tchagang, Alain Beaudelaire
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Frontiers Media S.A 22.09.2023
Schlagworte:	deep evolutionary learning density functional theory (DFT) genetic algorithm inverse design machine learning (ML) variational autoencoder (VAE)
ISSN:	2296-8016, 2296-8016
Online-Zugang:	Volltext
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