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Hu et al. J Mater Inf 2023;3:1 Journal of Materials
DOI: 10.20517/jmi.2022.28 Informatics

Research Article Open Access

Data-driven prediction of the glass-forming ability of
modeled alloys by supervised machine learning

1,
Yuan-Chao Hu *, Jiachuan Tian 2
1 Department of Mechanical Engineering & Materials Science, Yale University, New Haven, CT 06520, USA.
2 Meta Platforms, Inc., Menlo Park, CA 94025, USA.

*Correspondence to: Dr. Yuan-Chao Hu, Department of Mechanical Engineering & Materials Science, Yale University, New Haven,
CT 06520, USA. E-mail: ychu0213@gmail.com; ORCID: 0000-0001-9872-7854
How to cite this article: Hu YC, Tian J. Data-driven prediction of the glass-forming ability of modeled alloys by supervised machine
learning. J Mater Inf 2023;3:1. http://dx.doi.org/10.20517/jmi.2022.28
Received: 27 Sep 2022 First Decision: 16 Nov 2022 Revised: 4 Jan 2023 Accepted: 1 Feb 2023 Published: 17 Feb 2023

Academic Editor: Xingjun Liu Copy Editor: Ke-Cui Yang Production Editor: Ke-Cui Yang

Abstract
The ability of a matter to fall into a glassy state upon cooling differs greatly among metallic alloys. It is conventionally
measured by the critical cooling rate , below which crystallization inevitably happens. There are a lot of factors
involved in determining for an alloy, including both elemental features and alloy properties. However, the underlying
physical mechanism is still far from being well understood. Therefore, the design of new metallic glasses is mainly by
time- and labor-consuming trial-and-error experiments. This considerably slows down the development process of
metallic glasses. Nowadays, large-scale computer simulations have been playing a significant role in understanding
glass formation. Although the atomic-scale features can be well captured, the simulations themselves are constrained
to a limited timescale. To overcome these issues, we propose to explore the glass-forming ability of the modeled alloys
from computer simulations by supervised machine learning. We aim to gain insights into the key features determining
and found that the non-linear couplings of the geometrical and energetic factors are of great importance. An
optimized machine learning model is then established to predict new glass formers with a timescale beyond the
current simulation capability. This study will shed new light on both unveiling the glass formation mechanism and
guiding new alloy design in practice.

Keywords: Metallic glasses, molecular dynamics simulations, glass-forming ability, machine learning, data mining

© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, shar-
ing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you
give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate
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