Multi-fidelity machine learning models for accurate bandgap predictions of solids

Pilania, Ghanshyam; Gubernatis, James E.; Lookman, Turab

doi:10.1016/j.commatsci.2016.12.004

Title: Multi-fidelity machine learning models for accurate bandgap predictions of solids

Journal Article · Wed Dec 28 00:00:00 EST 2016 · Computational Materials Science

DOI:https://doi.org/10.1016/j.commatsci.2016.12.004· OSTI ID:1352377

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Los Alamos National Lab. (LANL), Los Alamos, NM (United States). Materials Science and Technology Division
Los Alamos National Lab. (LANL), Los Alamos, NM (United States). Theoretical Division

Here, we present a multi-fidelity co-kriging statistical learning framework that combines variable-fidelity quantum mechanical calculations of bandgaps to generate a machine-learned model that enables low-cost accurate predictions of the bandgaps at the highest fidelity level. Additionally, the adopted Gaussian process regression formulation allows us to predict the underlying uncertainties as a measure of our confidence in the predictions. In using a set of 600 elpasolite compounds as an example dataset and using semi-local and hybrid exchange correlation functionals within density functional theory as two levels of fidelities, we demonstrate the excellent learning performance of the method against actual high fidelity quantum mechanical calculations of the bandgaps. The presented statistical learning method is not restricted to bandgaps or electronic structure methods and extends the utility of high throughput property predictions in a significant way.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC52-06NA25396

OSTI ID:: 1352377

Alternate ID(s):: OSTI ID: 1397619

Report Number(s):: LA-UR-16-29228; TRN: US1700513

Journal Information:: Computational Materials Science, Vol. 129, Issue C; ISSN 0927-0256

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 192 works

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