English

Quantifying the Structure of Disordered Materials

Disordered Systems and Neural Networks 2022-11-16 v1

Abstract

Durable interest in developing a framework for the detailed structure of glassy materials has produced numerous structural descriptors that trade off between general applicability and interpretability. However, none approach the combination of simplicity and wide-ranging predictive power of the lattice-grain-defect framework for crystalline materials. Working from the hypothesis that the local atomic environments of a glassy material are constrained by enthalpy minimization to a low-dimensional manifold in atomic coordinate space, we develop a novel generalized distance function, the Gaussian Integral Inner Product (GIIP) distance, in connection with agglomerative clustering and diffusion maps, to parameterize that manifold. Applying this approach to a two-dimensional model crystal and a three-dimensional binary model metallic glass results in parameters interpretable as coordination number, composition, volumetric strain, and local symmetry. In particular, we show that a more slowly quenched glass has a higher degree of local tetrahedral symmetry at the expense of cyclic symmetry. While these descriptors require post-hoc interpretation, they minimize bias rooted in crystalline materials science and illuminate a range of structural trends that might otherwise be missed.

Keywords

Cite

@article{arxiv.2211.07790,
  title  = {Quantifying the Structure of Disordered Materials},
  author = {Thomas J. Hardin and Michael Chandross and Rahul Meena and Spencer Fajardo and Dimitris Giovanis and Ioannis G. Kevrekidis and Michael Falk and Michael Shields},
  journal= {arXiv preprint arXiv:2211.07790},
  year   = {2022}
}

Comments

18 pages, 4 figures

R2 v1 2026-06-28T05:54:21.952Z