Issue 7, 2024

InterMat: accelerating band offset prediction in semiconductor interfaces with DFT and deep learning

Abstract

We introduce a computational framework (InterMat) to predict band offsets of semiconductor interfaces using density functional theory (DFT) and graph neural networks (GNN). As a first step, we benchmark OptB88vdW generalized gradient approximation (GGA) work functions and electron affinities for surfaces against experimental data with accuracies of 0.29 eV and 0.39 eV, respectively. Similarly, we evaluate band offset values using independent unit (IU) and alternate slab junction (ASJ) models leading to accuracies of 0.45 eV and 0.22 eV, respectively. We use bulk band structure calculations with the TBmBJ meta-GGA functional to correct for band gap underestimation when predicting conduction band properties. During ASJ structure generation, we use Zur's algorithm along with a unified GNN force-field to tackle the conformation challenges of interface design. At present, we have 607 surface work functions calculated with DFT, from which we can compute 183 921 IU band offsets as well as 593 directly calculated ASJ band offsets. Finally, as the space of all possible heterojunctions is too large to simulate with DFT, we develop generalized GNN models to quickly predict bulk band edges with an accuracy of 0.26 eV. We show how these models can be used to predict relevant quantities including ionization potentials, electron affinities, and IU-based band offsets. We establish simple rules using the above models to pre-screen potential semiconductor devices from a vast pool of nearly 1.4 trillion candidate interfaces. InterMat is available at website: https://github.com/usnistgov/intermat.

Graphical abstract: InterMat: accelerating band offset prediction in semiconductor interfaces with DFT and deep learning

Supplementary files

Article information

Article type
Paper
Submitted
26 Jan 2024
Accepted
21 May 2024
First published
23 May 2024
This article is Open Access
Creative Commons BY license

Digital Discovery, 2024,3, 1365-1377

InterMat: accelerating band offset prediction in semiconductor interfaces with DFT and deep learning

K. Choudhary and K. F. Garrity, Digital Discovery, 2024, 3, 1365 DOI: 10.1039/D4DD00031E

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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