2020/03/31 by Ary R. Ferreira · 17 citations
Chemistry · Engineering · Materials Science · Physics and Astronomy · #Artificial intelligence #Atom (system on chip) #Categorization #Chemical bond #Chemical physics #Chemistry #Computational chemistry #Computer science #Crystal (programming language) #Density functional theory #Material Dynamics and Properties #Materials science #Metallic Glasses and Amorphous Alloys #Nanotechnology #Phase-change materials and chalcogenides #Physics #Population #Quantum mechanics #cond-mat.mtrl-sci #physics.chem-ph #physics.comp-ph
paper · pdf · doi:10.1103/physrevmaterials.4.113603
published in Physical Review Materials 4(11) (American Physical Society) · This update contains a number of enhancements. The manuscript has been updated after an anonymous review process by a journal whose policies discourage posting material from this process online. Few changes have been done in Sections I, II and III-A; however, Section III-B and IV have undergone significant changes
arxiv created 2020/07/21 · openalex publication_date 2020/11/09 · arxiv updated 2020/11/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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 paper 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.