Machine learning properties of binary wurtzite superlattices

Pilania, G.; Liu, X. -Y.

doi:10.1007/s10853-018-1987-z

Title: Machine learning properties of binary wurtzite superlattices

Journal Article · Fri Jan 12 00:00:00 EST 2018 · Journal of Materials Science

DOI:https://doi.org/10.1007/s10853-018-1987-z· OSTI ID:1430015

^[1]; Liu, X. -Y. ^[1]

Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

The burgeoning paradigm of high-throughput computations and materials informatics brings new opportunities in terms of targeted materials design and discovery. The discovery process can be significantly accelerated and streamlined if one can learn effectively from available knowledge and past data to predict materials properties efficiently. Indeed, a very active area in materials science research is to develop machine learning based methods that can deliver automated and cross-validated predictive models using either already available materials data or new data generated in a targeted manner. In the present paper, we show that fast and accurate predictions of a wide range of properties of binary wurtzite superlattices, formed by a diverse set of chemistries, can be made by employing state-of-the-art statistical learning methods trained on quantum mechanical computations in combination with a judiciously chosen numerical representation to encode materials’ similarity. These surrogate learning models then allow for efficient screening of vast chemical spaces by providing instant predictions of the targeted properties. Moreover, the models can be systematically improved in an adaptive manner, incorporate properties computed at different levels of fidelities and are naturally amenable to inverse materials design strategies. Finally, while the learning approach to make predictions for a wide range of properties (including structural, elastic and electronic properties) is demonstrated here for a specific example set containing more than 1200 binary wurtzite superlattices, the adopted framework is equally applicable to other classes of materials as well.

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

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC52-06NA25396

OSTI ID:: 1430015

Report Number(s):: LA-UR-17-30057; TRN: US1802746

Journal Information:: Journal of Materials Science, Vol. 53, Issue 9; ISSN 0022-2461

Publisher:: SpringerCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 20 works

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