Fingerprint-Based Detection of Non-Local Effects in the Electronic Structure of a Simple Single Component Covalent System

Using fingerprints used mainly in machine learning schemes of the potential energy surface, we detect in a fully algorithmic way long range effects on local physical properties in a simple covalent system of carbon atoms. The fact that these long range effects exist for many configurations implies that atomistic simulation methods, such as force fields or modern machine learning schemes, that are based on locality assumptions, are limited in accuracy. We show that the basic driving mechanism for the long range effects is charge transfer. If the charge transfer is known, locality can be recovered for certain quantities such as the band structure energy.

Automated summary

This study uses fingerprints in machine learning schemes to detect long range effects on local physical properties of carbon atoms in a simple covalent system. The driving mechanism for these effects is found to be charge transfer. The existence of these long range effects indicates limitations in accuracy for atomistic simulation methods based on locality assumptions.