globalchange  > 气候变化与战略
DOI: 10.1073/pnas.1818555116
论文题名:
Deep elastic strain engineering of bandgap through machine learning
作者: Shi Z.; Tsymbalov E.; Dao M.; Suresh S.; Shapeev A.; Li J.
刊名: Proceedings of the National Academy of Sciences of the United States of America
ISSN: 0027-8424
出版年: 2019
卷: 116, 期:10
起始页码: 4117
结束页码: 4122
语种: 英语
英文关键词: Bandgap engineering ; Calculation ; Electronic band structure ; First-principles ; Neural network ; Semiconductor materials
Scopus关键词: ab initio calculation ; accuracy ; Article ; artificial intelligence ; bandgap ; deep elastic strain engineering ; energy ; engineering ; machine learning ; priority journal
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.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/163595
Appears in Collections:气候变化与战略

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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

Recommended Citation:
Shi Z.,Tsymbalov E.,Dao M.,et al. Deep elastic strain engineering of bandgap through machine learning[J]. Proceedings of the National Academy of Sciences of the United States of America,2019-01-01,116(10)
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