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...

Full description

Bibliographic Details

Published in:	Frontiers in materials Vol. 10
Main Authors:	Chenebuah, Ericsson Tetteh, Nganbe, Michel, Tchagang, Alain Beaudelaire
Format:	Journal Article
Language:	English
Published:	Frontiers Media S.A 22.09.2023
Subjects:	deep evolutionary learning density functional theory (DFT) genetic algorithm inverse design machine learning (ML) variational autoencoder (VAE)
ISSN:	2296-8016, 2296-8016
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!