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

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Journal Title
Journal ISSN
Volume Title
A2 Katsausartikkeli tieteellisessä aikakauslehdessä
Date
2023-04
Major/Subject
Mcode
Degree programme
Language
en
Pages
26
Series
Semiconductor Science and Technology, Volume 38, issue 4
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.
Description
Funding Information: The author is grateful to Prof. Volker L Deringer from the University of Oxford for useful comments on this manuscript, and to the Academy of Finland for personal financial support, under Research Fellow Grant #330488.
Keywords
atomistic simulation, disordered carbon, disordered silicon, machine learning potentials, molecular dynamics
Other note
Citation
Caro , M A 2023 , ' Machine learning based modeling of disordered elemental semiconductors: understanding the atomic structure of a-Si and a-C ' , Semiconductor Science and Technology , vol. 38 , no. 4 , 043001 . https://doi.org/10.1088/1361-6641/acba3d