First-principles prediction of electronic transport in fabricated semiconductor heterostructures via physics-aware machine learning

Abstract First-principles techniques for electronic transport property prediction have seen rapid progress in recent years. However, it remains a challenge to predict properties of heterostructures incorporating fabrication-dependent variability. Machine-learning (ML) approaches are increasingly bei...

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Autores principales:	Artem K. Pimachev, Sanghamitra Neogi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/d9be47d814294a37a10ea655404a7cf6
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spelling	oai:doaj.org-article:d9be47d814294a37a10ea655404a7cf62021-12-02T17:39:52ZFirst-principles prediction of electronic transport in fabricated semiconductor heterostructures via physics-aware machine learning10.1038/s41524-021-00562-02057-3960https://doaj.org/article/d9be47d814294a37a10ea655404a7cf62021-06-01T00:00:00Zhttps://doi.org/10.1038/s41524-021-00562-0https://doaj.org/toc/2057-3960Abstract First-principles techniques for electronic transport property prediction have seen rapid progress in recent years. However, it remains a challenge to predict properties of heterostructures incorporating fabrication-dependent variability. Machine-learning (ML) approaches are increasingly being used to accelerate design and discovery of new materials with targeted properties, and extend the applicability of first-principles techniques to larger systems. However, few studies exploited ML techniques to characterize relationships between local atomic structures and global electronic transport coefficients. In this work, we propose an electronic-transport-informatics (ETI) framework that trains on ab initio models of small systems and predicts thermopower of fabricated silicon/germanium heterostructures, matching measured data. We demonstrate application of ML approaches to extract important physics that determines electronic transport in semiconductor heterostructures, and bridge the gap between ab initio accessible models and fabricated systems. We anticipate that ETI framework would have broad applicability to diverse materials classes.Artem K. PimachevSanghamitra NeogiNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 Artem K. Pimachev Sanghamitra Neogi First-principles prediction of electronic transport in fabricated semiconductor heterostructures via physics-aware machine learning
description	Abstract First-principles techniques for electronic transport property prediction have seen rapid progress in recent years. However, it remains a challenge to predict properties of heterostructures incorporating fabrication-dependent variability. Machine-learning (ML) approaches are increasingly being used to accelerate design and discovery of new materials with targeted properties, and extend the applicability of first-principles techniques to larger systems. However, few studies exploited ML techniques to characterize relationships between local atomic structures and global electronic transport coefficients. In this work, we propose an electronic-transport-informatics (ETI) framework that trains on ab initio models of small systems and predicts thermopower of fabricated silicon/germanium heterostructures, matching measured data. We demonstrate application of ML approaches to extract important physics that determines electronic transport in semiconductor heterostructures, and bridge the gap between ab initio accessible models and fabricated systems. We anticipate that ETI framework would have broad applicability to diverse materials classes.
format	article
author	Artem K. Pimachev Sanghamitra Neogi
author_facet	Artem K. Pimachev Sanghamitra Neogi
author_sort	Artem K. Pimachev
title	First-principles prediction of electronic transport in fabricated semiconductor heterostructures via physics-aware machine learning
title_short	First-principles prediction of electronic transport in fabricated semiconductor heterostructures via physics-aware machine learning
title_full	First-principles prediction of electronic transport in fabricated semiconductor heterostructures via physics-aware machine learning
title_fullStr	First-principles prediction of electronic transport in fabricated semiconductor heterostructures via physics-aware machine learning
title_full_unstemmed	First-principles prediction of electronic transport in fabricated semiconductor heterostructures via physics-aware machine learning
title_sort	first-principles prediction of electronic transport in fabricated semiconductor heterostructures via physics-aware machine learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d9be47d814294a37a10ea655404a7cf6
work_keys_str_mv	AT artemkpimachev firstprinciplespredictionofelectronictransportinfabricatedsemiconductorheterostructuresviaphysicsawaremachinelearning AT sanghamitraneogi firstprinciplespredictionofelectronictransportinfabricatedsemiconductorheterostructuresviaphysicsawaremachinelearning
_version_	1718379793946247168

First-principles prediction of electronic transport in fabricated semiconductor heterostructures via physics-aware machine learning

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