Electronic structure based descriptor for characterizing local atomic environments

TL;DR

This paper introduces a low-dimensional electronic structure descriptor based on local density of states moments, enabling intuitive analysis and classification of atomic environments with applications in simulations and machine learning.

Contribution

The paper develops a 2-D moments-descriptor derived from local density of states moments that effectively characterizes local atomic environments and relates to bond energy.

Findings

The moments-descriptor captures the largest contributions to local bond energy.

Distances in the descriptor map correlate with energy differences.

The method is validated with TB and DFT calculations.

Abstract

A quantitative descriptor of local atomic environments is often required for the analysis of atomistic data. Descriptors of the local atomic environment ideally provide physically and chemically intuitive insight. This requires descriptors that are low-dimensional representations of the interplay between atomic geometry and electronic bond formation. The moments of the local density of states (DOS) relate the atomic structure to the electronic structure and bond chemistry. This makes it possible to construct electronic structure based descriptors of the local atomic environment that have an immediate relation to the binding energy. We show that a low-dimensional moments-descriptor is sufficient as the lowest moments, calculated from the closest atomic neighborhood, carry the largest contributions to the local bond energy. Here, we construct moments-descriptors that project the space of local atomic environments on a 2-D map. We discuss in detail the separation of various atomic environments and their connections in the map. The distances in the map may be related to energy differences between local atomic environments as we show by analytic considerations based on analytic bond-order potentials (BOP) and by numerical assessment using TB and density-functional theory calculations. Possible applications of the proposed moments-descriptors include the classification of local atomic environments in molecular-dynamic simulations, the selection of structure sets for developing and testing interatomic potentials, as well as the construction of descriptors for machine-learning applications.