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

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.

References in corpus (10)

• A Spectral Analysis Method for Automated Generation of Quantum-Accurate Interatomic Potentials
• Machine-learning based interatomic potential for amorphous carbon
• GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations
• An Accurate and Transferable Machine Learning Potential for Carbon
• The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials
• Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine-learning molecular dynamics simulations
• Ephemeral data derived potentials for random structure search
• Accurate computational prediction of core-electron binding energies in carbon-based materials: A machine-learning model combining density-functional theory and $\mathbf{GW}$
• Ionic liquids under confinement: From systematic variations of the ion and pore sizes towards an understanding of structure and dynamics in complex porous carbons
• Combining phonon accuracy with high transferability in Gaussian approximation potential models

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