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 (38)

• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• On representing chemical environments
• Moment Tensor Potentials: a class of systematically improvable interatomic potentials
• Ab initio Random Structure Searching
• A Spectral Analysis Method for Automated Generation of Quantum-Accurate Interatomic Potentials
• DScribe: Library of Descriptors for Machine Learning in Materials Science
• Comparing molecules and solids across structural and alchemical space
• Machine-learning based interatomic potential for amorphous carbon
• Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport
• Physics-inspired structural representations for molecules and materials
• Machine learning a general purpose interatomic potential for silicon
• GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations
• Environment Dependent Interatomic Potential for Bulk Silicon
• FCHL revisited: faster and more accurate quantum machine learning
• An Accurate and Transferable Machine Learning Potential for Carbon
• Modeling heat transport in crystals and glasses from a unified lattice-dynamical approach
• De novo exploration and self-guided learning of potential-energy surfaces
• A Machine Learning Potential for Graphene
• Atom-Density Representations for Machine Learning
• Growth Mechanism and Origin of High $s{p}^3$ Content in Tetrahedral Amorphous Carbon
• Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning
• Optimizing many-body atomic descriptors for enhanced computational performance of machine learning based interatomic potentials
• Learning the electronic density of states in condensed matter
• Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles
• The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials
• Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures
• Machine learning driven simulated deposition of carbon films: from low-density to diamondlike amorphous carbon
• 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
• Machine learning force fields based on local parametrization of dispersion interactions: Application to the phase diagram of C${}_{60}$
• Atomic Permutationally Invariant Polynomials for Fitting Molecular Force Fields
• Combining phonon accuracy with high transferability in Gaussian approximation potential models
• Towards device-size atomistic models of amorphous silicon
• Carbon-carbon supercapacitors: Beyond the average pore size or how electrolyte confinement and inaccessible pores affect the capacitance
• Origin of micron-scale propagation lengths of heat-carrying acoustic excitations in amorphous silicon
• The Homunculus Brain and Categorical Logic

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