Machine learning for deep elastic strain engineering of semiconductor electronic band structure and effective mass

Abstract The controlled introduction of elastic strains is an appealing strategy for modulating the physical properties of semiconductor materials. With the recent discovery of large elastic deformation in nanoscale specimens as diverse as silicon and diamond, employing this strategy to improve devi...

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Autores principales:	Evgenii Tsymbalov, Zhe Shi, Ming Dao, Subra Suresh, Ju Li, Alexander Shapeev
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/05ea09ba993c4d1f84826a23b4e86974
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spelling	oai:doaj.org-article:05ea09ba993c4d1f84826a23b4e869742021-12-02T15:49:38ZMachine learning for deep elastic strain engineering of semiconductor electronic band structure and effective mass10.1038/s41524-021-00538-02057-3960https://doaj.org/article/05ea09ba993c4d1f84826a23b4e869742021-05-01T00:00:00Zhttps://doi.org/10.1038/s41524-021-00538-0https://doaj.org/toc/2057-3960Abstract The controlled introduction of elastic strains is an appealing strategy for modulating the physical properties of semiconductor materials. With the recent discovery of large elastic deformation in nanoscale specimens as diverse as silicon and diamond, employing this strategy to improve device performance necessitates first-principles computations of the fundamental electronic band structure and target figures-of-merit, through the design of an optimal straining pathway. Such simulations, however, call for approaches that combine deep learning algorithms and physics of deformation with band structure calculations to custom-design electronic and optical properties. Motivated by this challenge, we present here details of a machine learning framework involving convolutional neural networks to represent the topology and curvature of band structures in k-space. These calculations enable us to identify ways in which the physical properties can be altered through “deep” elastic strain engineering up to a large fraction of the ideal strain. Algorithms capable of active learning and informed by the underlying physics were presented here for predicting the bandgap and the band structure. By training a surrogate model with ab initio computational data, our method can identify the most efficient strain energy pathway to realize physical property changes. The power of this method is further demonstrated with results from the prediction of strain states that influence the effective electron mass. We illustrate the applications of the method with specific results for diamonds, although the general deep learning technique presented here is potentially useful for optimizing the physical properties of a wide variety of semiconductor materials.Evgenii TsymbalovZhe ShiMing DaoSubra SureshJu LiAlexander ShapeevNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-10 (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 Evgenii Tsymbalov Zhe Shi Ming Dao Subra Suresh Ju Li Alexander Shapeev Machine learning for deep elastic strain engineering of semiconductor electronic band structure and effective mass
description	Abstract The controlled introduction of elastic strains is an appealing strategy for modulating the physical properties of semiconductor materials. With the recent discovery of large elastic deformation in nanoscale specimens as diverse as silicon and diamond, employing this strategy to improve device performance necessitates first-principles computations of the fundamental electronic band structure and target figures-of-merit, through the design of an optimal straining pathway. Such simulations, however, call for approaches that combine deep learning algorithms and physics of deformation with band structure calculations to custom-design electronic and optical properties. Motivated by this challenge, we present here details of a machine learning framework involving convolutional neural networks to represent the topology and curvature of band structures in k-space. These calculations enable us to identify ways in which the physical properties can be altered through “deep” elastic strain engineering up to a large fraction of the ideal strain. Algorithms capable of active learning and informed by the underlying physics were presented here for predicting the bandgap and the band structure. By training a surrogate model with ab initio computational data, our method can identify the most efficient strain energy pathway to realize physical property changes. The power of this method is further demonstrated with results from the prediction of strain states that influence the effective electron mass. We illustrate the applications of the method with specific results for diamonds, although the general deep learning technique presented here is potentially useful for optimizing the physical properties of a wide variety of semiconductor materials.
format	article
author	Evgenii Tsymbalov Zhe Shi Ming Dao Subra Suresh Ju Li Alexander Shapeev
author_facet	Evgenii Tsymbalov Zhe Shi Ming Dao Subra Suresh Ju Li Alexander Shapeev
author_sort	Evgenii Tsymbalov
title	Machine learning for deep elastic strain engineering of semiconductor electronic band structure and effective mass
title_short	Machine learning for deep elastic strain engineering of semiconductor electronic band structure and effective mass
title_full	Machine learning for deep elastic strain engineering of semiconductor electronic band structure and effective mass
title_fullStr	Machine learning for deep elastic strain engineering of semiconductor electronic band structure and effective mass
title_full_unstemmed	Machine learning for deep elastic strain engineering of semiconductor electronic band structure and effective mass
title_sort	machine learning for deep elastic strain engineering of semiconductor electronic band structure and effective mass
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/05ea09ba993c4d1f84826a23b4e86974
work_keys_str_mv	AT evgeniitsymbalov machinelearningfordeepelasticstrainengineeringofsemiconductorelectronicbandstructureandeffectivemass AT zheshi machinelearningfordeepelasticstrainengineeringofsemiconductorelectronicbandstructureandeffectivemass AT mingdao machinelearningfordeepelasticstrainengineeringofsemiconductorelectronicbandstructureandeffectivemass AT subrasuresh machinelearningfordeepelasticstrainengineeringofsemiconductorelectronicbandstructureandeffectivemass AT juli machinelearningfordeepelasticstrainengineeringofsemiconductorelectronicbandstructureandeffectivemass AT alexandershapeev machinelearningfordeepelasticstrainengineeringofsemiconductorelectronicbandstructureandeffectivemass
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Machine learning for deep elastic strain engineering of semiconductor electronic band structure and effective mass

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