  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.
