Machine learning based modeling of disordered elemental semiconductors: understanding the atomic structure of a-Si and a-C

TL;DR

This paper reviews how machine learning models are transforming the understanding of atomic structures in disordered semiconductors like a-C and a-Si, overcoming previous computational limitations.

Contribution

It provides an overview of ML atomistic modeling techniques and their application to revealing atomic structures of amorphous semiconductors.

Findings

ML approaches accurately approximate DFT potential energy surfaces

ML simulations offer new insights into atomic arrangements of a-C and a-Si

ML methods significantly reduce computational time for modeling disordered semiconductors

Abstract

Disordered elemental semiconductors, most notably a-C and a-Si, are ubiquitous in a myriad of different applications. These exploit their unique mechanical and electronic properties. In the past couple of decades, density functional theory (DFT) and other quantum mechanics-based computational simulation techniques have been successful at delivering a detailed understanding of the atomic and electronic structure of crystalline semiconductors. Unfortunately, the complex structure of disordered semiconductors sets the time and length scales required for DFT simulation of these materials out of reach. In recent years, machine learning (ML) approaches to atomistic modeling have been developed that provide an accurate approximation of the DFT potential energy surface for a small fraction of the computational time. These ML approaches have now reached maturity and are starting to deliver the first conclusive insights into some of the missing details surrounding the intricate atomic structure of disordered semiconductors. In this Topical Review we give a brief introduction to ML atomistic modeling and its application to amorphous semiconductors. We then take a look at how ML simulations have been used to improve our current understanding of the atomic structure of a-C and a-Si.