气候变化领域集成服务门户(CCPortal): Deep elastic strain engineering of bandgap through machine learning

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DOI	10.1073/pnas.1818555116
	Deep elastic strain engineering of bandgap through machine learning
	Shi Z.; Tsymbalov E.; Dao M.; Suresh S.; Shapeev A.; Li J.
发表日期	2019
ISSN	0027-8424
起始页码	4117
结束页码	4122
卷号	116 期号:10
英文摘要	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. © 2019 National Academy of Sciences. All Rights Reserved.
英文关键词	Bandgap engineering; Calculation; Electronic band structure; First-principles; Neural network; Semiconductor materials
语种	英语
scopus关键词	ab initio calculation; accuracy; Article; artificial intelligence; bandgap; deep elastic strain engineering; energy; engineering; machine learning; priority journal
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/160402
作者单位	Shi, Z., Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, United States, Department of Nuclear Science Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, United States; Tsymbalov, E., Skolkovo Institute of Science and Technology, Moscow, 121205, Russian Federation; Dao, M., Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, United States; Suresh, S., Nanyang Technological University, Singapore, 639798, Singapore; Shapeev, A., Skolkovo Institute of Science and Technology, Moscow, 121205, Russian Federation; Li, J., Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, United States, Department of Nuclear Science Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, United States
推荐引用方式 GB/T 7714	Shi Z.,Tsymbalov E.,Dao M.,et al. Deep elastic strain engineering of bandgap through machine learning[J],2019,116(10).
APA	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 of the United States of America,116(10).
MLA	Shi Z.,et al."Deep elastic strain engineering of bandgap through machine learning".Proceedings of the National Academy of Sciences of the United States of America 116.10(2019).