Machine learning for glass science and engineering: A review

The design of new glasses is often plagued by poorly efficient Edisonian “trial-and-error” discovery approaches. As an alternative route, the Materials Genome Initiative has largely popularized new approaches relying on artificial intelligence and machine learning for accelerating the discovery and...

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Vydané v:	Journal of non-crystalline solids Ročník 557; číslo C; s. 119419
Hlavní autori:	Liu, Han, Fu, Zipeng, Yang, Kai, Xu, Xinyi, Bauchy, Mathieu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Netherlands Elsevier B.V 01.04.2021 Elsevier
Predmet:	Artificial neuron network Bayesian optimization Composition-property relationship Molecular dynamics simulation Structural signature Structural signature Artificial neuron network Composition-property relationship Molecular dynamics simulation Bayesian optimization
ISSN:	0022-3093, 1873-4812
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