Rational design of chemically complex metallic glasses by hybrid modeling guided machine learning

Abstract The compositional design of metallic glasses (MGs) is a long-standing issue in materials science and engineering. However, traditional experimental approaches based on empirical rules are time consuming with a low efficiency. In this work, we successfully developed a hybrid machine learning...

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Autores principales:	Z. Q. Zhou, Q. F. He, X. D. Liu, Q. Wang, J. H. Luan, C. T. Liu, Y. Yang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/0908c53c80d347a2af2c2dfa3e1b2dc5
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spelling	oai:doaj.org-article:0908c53c80d347a2af2c2dfa3e1b2dc52021-12-02T16:35:05ZRational design of chemically complex metallic glasses by hybrid modeling guided machine learning10.1038/s41524-021-00607-42057-3960https://doaj.org/article/0908c53c80d347a2af2c2dfa3e1b2dc52021-08-01T00:00:00Zhttps://doi.org/10.1038/s41524-021-00607-4https://doaj.org/toc/2057-3960Abstract The compositional design of metallic glasses (MGs) is a long-standing issue in materials science and engineering. However, traditional experimental approaches based on empirical rules are time consuming with a low efficiency. In this work, we successfully developed a hybrid machine learning (ML) model to address this fundamental issue based on a database containing ~5000 different compositions of metallic glasses (either bulk or ribbon) reported since 1960s. Unlike the prior works relying on empirical parameters for featurization of data, we designed modeling guided data descriptors in line with the recent theoretical models on amorphization in chemically complex alloys for the development of the hybrid classification-regression ML algorithms. Our hybrid ML modeling was validated both numerically and experimentally. Most importantly, it enabled the discovery of MGs (either bulk or ribbon) through the ML-aided deep search of a multitude of quaternary to scenery alloy compositions. The computational framework herein established is expected to accelerate the design of MG compositions and expand their applications by probing the complex and multi-dimensional compositional space that has never been explored before.Z. Q. ZhouQ. F. HeX. D. LiuQ. WangJ. H. LuanC. T. LiuY. YangNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 Z. Q. Zhou Q. F. He X. D. Liu Q. Wang J. H. Luan C. T. Liu Y. Yang Rational design of chemically complex metallic glasses by hybrid modeling guided machine learning
description	Abstract The compositional design of metallic glasses (MGs) is a long-standing issue in materials science and engineering. However, traditional experimental approaches based on empirical rules are time consuming with a low efficiency. In this work, we successfully developed a hybrid machine learning (ML) model to address this fundamental issue based on a database containing ~5000 different compositions of metallic glasses (either bulk or ribbon) reported since 1960s. Unlike the prior works relying on empirical parameters for featurization of data, we designed modeling guided data descriptors in line with the recent theoretical models on amorphization in chemically complex alloys for the development of the hybrid classification-regression ML algorithms. Our hybrid ML modeling was validated both numerically and experimentally. Most importantly, it enabled the discovery of MGs (either bulk or ribbon) through the ML-aided deep search of a multitude of quaternary to scenery alloy compositions. The computational framework herein established is expected to accelerate the design of MG compositions and expand their applications by probing the complex and multi-dimensional compositional space that has never been explored before.
format	article
author	Z. Q. Zhou Q. F. He X. D. Liu Q. Wang J. H. Luan C. T. Liu Y. Yang
author_facet	Z. Q. Zhou Q. F. He X. D. Liu Q. Wang J. H. Luan C. T. Liu Y. Yang
author_sort	Z. Q. Zhou
title	Rational design of chemically complex metallic glasses by hybrid modeling guided machine learning
title_short	Rational design of chemically complex metallic glasses by hybrid modeling guided machine learning
title_full	Rational design of chemically complex metallic glasses by hybrid modeling guided machine learning
title_fullStr	Rational design of chemically complex metallic glasses by hybrid modeling guided machine learning
title_full_unstemmed	Rational design of chemically complex metallic glasses by hybrid modeling guided machine learning
title_sort	rational design of chemically complex metallic glasses by hybrid modeling guided machine learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0908c53c80d347a2af2c2dfa3e1b2dc5
work_keys_str_mv	AT zqzhou rationaldesignofchemicallycomplexmetallicglassesbyhybridmodelingguidedmachinelearning AT qfhe rationaldesignofchemicallycomplexmetallicglassesbyhybridmodelingguidedmachinelearning AT xdliu rationaldesignofchemicallycomplexmetallicglassesbyhybridmodelingguidedmachinelearning AT qwang rationaldesignofchemicallycomplexmetallicglassesbyhybridmodelingguidedmachinelearning AT jhluan rationaldesignofchemicallycomplexmetallicglassesbyhybridmodelingguidedmachinelearning AT ctliu rationaldesignofchemicallycomplexmetallicglassesbyhybridmodelingguidedmachinelearning AT yyang rationaldesignofchemicallycomplexmetallicglassesbyhybridmodelingguidedmachinelearning
_version_	1718383732822376448

Rational design of chemically complex metallic glasses by hybrid modeling guided machine learning

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