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

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) mod...

Full description

Saved in:
Bibliographic Details
Published in:npj computational materials 2021-08, Vol.7 (1), p.1-10, Article 138
Main Authors: Zhou, Z. Q., He, Q. F., Liu, X. D., Wang, Q., Luan, J. H., Liu, C. T., Yang, Y.
Format: Article
Language:English
Subjects:
639/301/1023/1026
639/301/1023/218
639/301/1034/1037
Algorithms
Amorphization
Amorphous materials
Characterization and Evaluation of Materials
Chemistry and Materials Science
Composition
Computational Intelligence
Computer applications
Learning algorithms
Machine learning
Materials Science
Mathematical and Computational Engineering
Mathematical and Computational Physics
Mathematical Modeling and Industrial Mathematics
Metallic glasses
Quaternary alloys
Theoretical
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2057-3960
2057-3960
DOI:10.1038/s41524-021-00607-4