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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title Dual Machine Learning Discovery of the Radius of Informative Structural Environments in Metallic Glasses
Author(s) Muchen Wang, Yuchu Wang, Minhazul Islam, Yuchi Wang, Yunzhi Wang, Jinwoo Hwang, Yue Fan
On-Site Speaker (Planned) Muchen Wang
Abstract Scope Establishing structure-property relationships in metallic glasses remains challenging because their disordered atomic structures lack obvious descriptors beyond short-range order. Here, we use machine learning to identify an intrinsic structural length scale that optimally captures informative atomic environments in amorphous alloys. For a prototypical Zr-Cu metallic glass, we combine two independent ML strategies to define the Radius of Informative Structural Environments (RISE). A reductionist model using SOAP descriptors with XGBoost shows that atomic environments within a 5 Å radius maximize structural diversity and enable optimal prediction of configurational energy. In parallel, an emergent Vision Transformer trained directly on voxelized atomic configurations exhibits performance saturation when inter-patch communication corresponds to an equivalent radius of approximately 5 Å. The convergence of these distinct approaches provides data-driven evidence for a physically meaningful medium-range structural length scale. Robustness tests across multiple glass-forming systems further confirm that RISE is transferable and not a computational artifact.
Proceedings Inclusion? Planned:
Keywords Machine Learning,

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