Discovering optoelectronic materials by combining density functional theory and machine learning
Date
2021
Authors
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Publisher
University of Delaware
Abstract
Computational simulations are playing an increasingly important role in materials design. First-principles calculations based on the density functional theory (DFT),where only atomic numbers and atomic positions are needed as input, have turned into powerful computational tools to explore the structural, electronic, optical, and magnetic properties of materials. Recent developments of hybrid functionals, which overcome the band-gap problem in DFT with standard approximations, have led to accurate predictions of the electronic band structure and related properties of a wide variety of materials, giving access to materials properties that are often difficult to probe, and leading to an unprecedented fundamental understanding of materials on the atomistic scale. ☐ In this thesis, hybrid DFT is used to explore the relationship between crystalline phases and electronic and optical properties of In2Se3. In2Se3is a distinguished member of 2D layered chalcogenides that is being considered for many technological applications, including solar cells, photodetectors and phase-change memory devices. We predicted a large disparity between its indirect fundamental gap and optical gap, shedding light on the puzzling wide range of reported values, from 0.55 eV to 1.5 eV. We also explored the effects of adding Te to 3D γphase of In2Se3, predicting thatγphase In2(Se1−xTex)3 alloys have large absorption coefficients, as well as band gaps and band edge positions that are suitable to photovoltaic applications. And in a joint experiment-theory effort, we studied the evolution of band gaps in layered perovskite-derived Ruddlesden-Popper phases An+1BnX3n+1 of chalcogenides, halides, and oxides, relating structural properties such as BX6 octahedral rotations to their electronic structure, paving way to the design of novel optoelectronic materials. ☐ Recent advances in computational materials science methods are increasing the quantity and complexity of generated data, at the same time machine learning algorithms which are used to identify correlations and patterns from large amounts of complex data are seeing rapid progress. The combination of these two methodologies has the potential to revolutionize new materials discovery and development. In this direction, we developed and employed a combined machine learning and DFT method to predict band gaps of perovskite materials. The predictive power of DFT calculations in describing semiconductors and insulators is severely limited, with band gaps are underestimated by 50% or higher. Using high-throughput DFT and hybrid functional calculations, we determined the band-gap correction for a representative set of oxide perovskites. Interestingly we find that the correction is not given by a simple scissors operator where the conduction band is pushed up while the valence band remains intact. Instead, we find that the correction involves pushing down the valence band by ∼1 eV and pushing up conduction band by ∼0.5 eV, thus causing systematic ∼1.5 eV correction for band gaps. The results are interpreted based on the orbital composition of the band edges and shine light on which atomic properties affect most of the correction.
Description
Keywords
Hybrid functionals, Computational simulations, Materials design, Band-gap problem, Atomistic scale