Machine learning–assisted ECNLE theory for predicting dynamical properties of FeCoCrMoCBTm metallic glass

Que Ngo; Anh D. Phan; T. Nga Do; Van Huynh Tran

doi:10.15625/0868-3166/23580

Author affiliations

Authors

Ngo T. Que Phenikaa Institute for Advanced Study, Phenikaa University, Hanoi 12116, Vietnam
Anh D. Phan
\(^1\)Phenikaa Institute for Advanced Study, Phenikaa University, Hanoi 12116, Vietnam
\(^2\)Faculty of Materials Science and Engineering, Phenikaa University, Hanoi 12116, Vietnam
Do T. Nga Institute of Physics, Vietnam Academy of Science and Technology, 10 Dao Tan, Giang Vo, Hanoi 11108, Vietnam
Tran Van Huynh Department of Basic Sciences and Foreign Languages, University of Fire, 243 Khuat Duy Tien, Hanoi 120602, Vietnam

DOI:

https://doi.org/10.15625/0868-3166/23580

Keywords:

metallics glasses, relaxation time, diffusion, machine learning

Abstract

In this work, we present an integrated modeling framework that combines machine learning (ML) and the Elastically Collective Nonlinear Langevin Equation (ECNLE) theory to investigate the glass transition dynamics of metallic glass systems. We focus on the Fe₄₂Co₆Cr₁₅Mo₁₄C_xB_21-xTm₂ alloy family. The glass transition temperatures T_g of this alloy family are predicted using machine learning models trained on experimental datasets. These ML-predicted-T_g values are then used as input to the ECNLE theory to compute the temperature dependence of structural relaxation times, dynamic fragility, and diffusion coefficients. Our predictions of T_g and dynamic fragility quantitatively agree with the experimental data. This shows that the combined ML-ECNLE approach provides a practical and scalable tool for characterizing the relaxation behavior and diffusion dynamics metallic glasses when experimental data remain limited.

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