Accurate Prediction of HSE06 Band Structures for a Diverse Set of Materials Using Δ-Learning

Adhikari, Santosh; Clary, Jacob; Sundararaman, Ravishankar; Musgrave, Charles; Vigil-Fowler, Derek; Sutton, Christopher

doi:10.1021/acs.chemmater.3c01131

Title: Accurate Prediction of HSE06 Band Structures for a Diverse Set of Materials Using Δ-Learning

Journal Article · 16 October 2023 · Chemistry of Materials

DOI: https://doi.org/10.1021/acs.chemmater.3c01131 · OSTI ID:2229565

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Univ. of South Carolina, Columbia, SC (United States)
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Rensselaer Polytechnic Inst., Troy, NY (United States)
Univ. of Colorado, Boulder, CO (United States)

Here we used machine learning (ML) to accurately predict eigenvalues of the hybrid HSE06 functional using eigenvalues computed by the less computationally expensive PBE functional and associated electronic features based on the k-point resolved atomic band character. The ML model was trained by using eigenvalues from only one k-point for each of the 168 compounds in the training set. The HSE06 eigenvalues across all k-points were then predicted for a separate set of 169 compounds with a mean absolute error (MAE) of 0.13 eV, representing a significant improvement over the error of PBE-computed eigenvalues relative to that of HSE06 (MAE = 0.96 eV). These accurately predicted eigenvalues result in remarkably accurate predictions for the band structures, projected density of states, and band gaps, even though the model was not explicitly trained on these other properties. Finally, we demonstrate that our ML model has a similar accuracy for both ternary and quaternary compounds well outside the initial training set and on systems with 112 and 160 atoms, demonstrating its potential to rapidly predict HSE06-quality electronic structures of complex materials that are practically unfeasible for HSE06.

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Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Energy Efficiency and Renewable Energy (EERE)

Grant/Contract Number:: AC36-08GO28308; SC0022247

OSTI ID:: 2229565

Report Number(s):: NREL/JA-2C00-88272; MainId:89047; UUID:362b1f14-9712-46e8-b326-6d7fce524196; MainAdminID:71273

Journal Information:: Chemistry of Materials, Vol. 35, Issue 20; ISSN 0897-4756

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
eigenvalues and eigenfunctions
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Title: Accurate Prediction of HSE06 Band Structures for a Diverse Set of Materials Using Δ-Learning

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