Alloy Informatics through Ab Initio Charge Density Profiles: Case Study of Hydrogen Effects in Face-Centered Cubic Crystals

TL;DR

This paper introduces 'alloy informatics', a machine learning framework using first-principles charge density profiles to predict alloy properties and defect interactions, demonstrated through hydrogen effects in face-centered cubic crystals.

Contribution

It presents a novel approach combining ab initio charge density profiles with machine learning to analyze alloy and defect properties, revealing emergent charge response classes and migration barriers.

Findings

Charge density profiles correlate with atomic properties.

RDFs reveal screening effects and classify charge responses.

Charge features connect to hydrogen migration energy barriers.

Abstract

Materials design has traditionally evolved through trial-error approaches, mainly due to the non-local relationship between microstructures and properties such as strength and toughness. We propose 'alloy informatics' as a machine learning based prototype predictive approach for alloys and compounds, using electron charge density profiles derived from first-principle calculations. We demonstrate this framework in the case of hydrogen interstitials in face-centered cubic crystals, showing that their differential electron charge density profiles capture crystal properties and defect-crystal interaction properties. Radial Distribution Functions (RDFs) of defect-induced differential charge density perturbations highlight the resulting screening effect, and, together with hydrogen Bader charges, strongly correlate to a large set of atomic properties of the metal species forming the bulk crystal. We observe the spontaneous emergence of classes of charge responses while coarse-graining over crystal compositions. Nudge-Elastic-Band calculations show that RDFs and charge features also connect to hydrogen migration energy barriers between interstitial sites. Unsupervised machine-learning on RDFs supports classification, unveiling compositional and configurational non-localities in the similarities of the perturbed densities. Electron charge density perturbations may be considered as bias-free descriptors for a large variety of defects.