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

Abstract

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 furthermore » 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.« less

Authors:

Tsymbalov, Evgenii; Shi, Zhe ;

• Search DOE PAGES for author "Shi, Zhe"
• Search DOE PAGES for ORCID "0000-0001-9282-8881"

Dao, Ming ;

• Search DOE PAGES for author "Dao, Ming"
• Search DOE PAGES for ORCID "0000-0001-5372-385X"

Suresh, Subra; Li, Ju ;

• Search DOE PAGES for author "Li, Ju"
• Search DOE PAGES for ORCID "0000-0002-7841-8058"

Shapeev, Alexander

Publication Date:

Fri May 28 00:00:00 EDT 2021

Sponsoring Org.:

USDOE

OSTI Identifier:

1785138

Resource Type:

Published Article

Journal Name:

npj Computational Materials

Additional Journal Information:

Journal Name: npj Computational Materials Journal Volume: 7 Journal Issue: 1; Journal ID: ISSN 2057-3960

Publisher:

Nature Publishing Group

Country of Publication:

United Kingdom

Language:

English