Machine learning for deep elastic strain engineering of semiconductor electronic band structure and effective mass
<jats:title>Abstract</jats:title><jats:p>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 an...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Springer Science and Business Media LLC
2021
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| Online Access: | https://hdl.handle.net/1721.1/135630 |
| _version_ | 1826206707616841728 |
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| author | Tsymbalov, Evgenii Shi, Zhe Dao, Ming Suresh, Subra Li, Ju Shapeev, Alexander |
| author2 | Massachusetts Institute of Technology. Department of Materials Science and Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Materials Science and Engineering Tsymbalov, Evgenii Shi, Zhe Dao, Ming Suresh, Subra Li, Ju Shapeev, Alexander |
| author_sort | Tsymbalov, Evgenii |
| collection | MIT |
| description | <jats:title>Abstract</jats:title><jats:p>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 <jats:bold>k</jats:bold>-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.</jats:p> |
| first_indexed | 2024-09-23T13:37:02Z |
| format | Article |
| id | mit-1721.1/135630 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T13:37:02Z |
| publishDate | 2021 |
| publisher | Springer Science and Business Media LLC |
| record_format | dspace |
| spelling | mit-1721.1/1356302023-03-15T20:35:16Z Machine learning for deep elastic strain engineering of semiconductor electronic band structure and effective mass Tsymbalov, Evgenii Shi, Zhe Dao, Ming Suresh, Subra Li, Ju Shapeev, Alexander Massachusetts Institute of Technology. Department of Materials Science and Engineering Massachusetts Institute of Technology. Department of Nuclear Science and Engineering <jats:title>Abstract</jats:title><jats:p>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 <jats:bold>k</jats:bold>-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.</jats:p> 2021-10-27T20:24:21Z 2021-10-27T20:24:21Z 2021 2021-08-10T17:22:14Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/135630 en 10.1038/s41524-021-00538-0 npj Computational Materials Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Springer Science and Business Media LLC Nature |
| spellingShingle | Tsymbalov, Evgenii Shi, Zhe Dao, Ming Suresh, Subra Li, Ju Shapeev, Alexander Machine learning for deep elastic strain engineering of semiconductor electronic band structure and effective mass |
| title | 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_short | 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 |
| url | https://hdl.handle.net/1721.1/135630 |
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