Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/103730
Title: Deep elastic strain engineering of bandgap through machine learning
Authors: Shi, Zhe
Tsymbalov, Evgenii
Dao, Ming
Suresh, Subra
Shapeev, Alexander
Li, Ju
Keywords: Electronic Band Structure
Bandgap Engineering
Engineering::Materials
Issue Date: 2019
Source: Shi, Z., Tsymbalov, E., Dao, M., Suresh, S., Shapeev, A., & Li, J. (2019). Deep elastic strain engineering of bandgap through machine learning. Proceedings of the National Academy of Sciences, 116(10), 4117-4122. doi:10.1073/pnas.1818555116
Series/Report no.: Proceedings of the National Academy of Sciences
Abstract: Nanoscale specimens of semiconductor materials as diverse as silicon and diamond are now known to be deformable to large elastic strains without inelastic relaxation. These discoveries harbinger a new age of deep elastic strain engineering of the band structure and device performance of electronic materials. Many possibilities remain to be investigated as to what pure silicon can do as the most versatile electronic material and what an ultrawide bandgap material such as diamond, with many appealing functional figures of merit, can offer after overcoming its present commercial immaturity. Deep elastic strain engineering explores full six-dimensional space of admissible nonlinear elastic strain and its effects on physical properties. Here we present a general method that combines machine learning and ab initio calculations to guide strain engineering whereby material properties and performance could be designed. This method invokes recent advances in the field of artificial intelligence by utilizing a limited amount of ab initio data for the training of a surrogate model, predicting electronic bandgap within an accuracy of 8 meV. Our model is capable of discovering the indirect-to-direct bandgap transition and semiconductor-to-metal transition in silicon by scanning the entire strain space. It is also able to identify the most energy-efficient strain pathways that would transform diamond from an ultrawide-bandgap material to a smaller-bandgap semiconductor. A broad framework is presented to tailor any target figure of merit by recourse to deep elastic strain engineering and machine learning for a variety of applications in microelectronics, optoelectronics, photonics, and energy technologies.
URI: https://hdl.handle.net/10356/103730
http://hdl.handle.net/10220/49987
ISSN: 0027-8424
DOI: http://dx.doi.org/10.1073/pnas.1818555116
Rights: © 2019 The Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MSE Journal Articles

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