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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Veröffentlicht in:	Journal of non-crystalline solids Jg. 557; H. C; S. 119419
Hauptverfasser:	Liu, Han, Fu, Zipeng, Yang, Kai, Xu, Xinyi, Bauchy, Mathieu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Netherlands Elsevier B.V 01.04.2021 Elsevier
Schlagworte:	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
Online-Zugang:	Volltext
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