  The chemistry (composition and bonding information) of metallic glasses (MGs)
is at least as important as structural topology for understanding their
properties and production/processing peculiarities. This article reports a
machine learning (ML)-based approach that brings an unprecedented "big picture"
view of chemical bond strengths in MGs of a prototypical alloy system. The
connection between electronic structure and chemical bonding is given by
crystal orbital Hamilton population (COHP) analysis; within the framework of
density functional theory (DFT). The stated comprehensive overview is made
possible through a combination of: efficient quantitative estimate of bond
strengths supplied by COHP analysis; representative statistics regarding
structure in terms of atomic configurations achieved with classical molecular
dynamics simulations; and the smooth overlap of atomic positions (SOAP)
descriptor. The study is supplemented by an application of that ML model under
the scope of mechanical loading; in which the resulting overview of chemical
bond strengths revealed a chemical/structural heterogeneity that is in line
with the tendency to bond exchange verified for atomic local environments. The
encouraging results pave the way towards alternative approaches applicable in
plenty of other contexts in which atom categorization (from the perspective of
chemical bonds) plays a key role.
