Concurrent prediction of metallic glasses’ global energy and internal structural heterogeneity by interpretable machine learning

Abstract

Predicting glasses’ properties from their structures is a formidable challenge because of the inherently disordered atomic configurations. Here we tackle the problem using a new two-stage (encoding/interpreting) machine learning pipeline. First, local environments are encoded by the Smooth Overlap of Atomic Positions (SOAP) descriptors, which are then fed into extreme gradient boosting tree algorithm to train/predict given samples’ configurational energy. 40 important unique local environments (ULEs) most responsible for the global energy of ZrCu-based glasses are identified. Markedly, we discover that the same short-range orders of Voronoi cells, when embedded in various ULEs, could impact the sample's global stability in qualitatively different manners. These new findings thus reveal a profound connection between short-range orders and medium-range orders. In the second stage, a designed interpreting stage is employed to decompose a sample's 3 N degrees-of-freedom configuration into a 40-dimension probability spectrum barcode via frequency mapping of those ULEs. We demonstrate that, in addition to the global energy prediction, by analyzing barcode-elements’ occupational fractions and fluctuations, one can simultaneously assess samples’ structural heterogeneity, which is known as a crucial quantity to dictate metallic glasses’ deformation behaviors. The implications of our findings to a barcode-mediated new strategy of inverse engineering design of metallic glasses are also discussed.