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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Popis
Shrnutí:	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 optimization of novel, advanced materials. Here, we review some recent progress in adopting machine learning to accelerate the design of new glasses with tailored properties. •We review some recent progress in machine learning applied to glass science.•We provide an introduction to common machine learning techniques.•We highlight the benefits of “physics-informed machine learning.”•We show how machine learning and physics-based modeling can be used in synergy.•We discuss some potential future directions in the use of machine learning in glass science.
Bibliografia:	USDOE
ISSN:	0022-3093 1873-4812
DOI:	10.1016/j.jnoncrysol.2019.04.039