Open AccessAccelerating Materials Discovery Based on Generalized Low-dimensional Conformation Performance Relationships
Author(s) -
Yuanyuan Qu,
Xiangying Meng,
Zhenhong Jia,
Xiaofeng Liu,
Lingxin Chen,
Shanshan Li,
Fang Bian
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/746/1/012020
Subject(s) - perspective (graphical) , reliability (semiconductor) , function (biology) , set (abstract data type) , rational design , semiconductor , core (optical fiber) , computer science , key (lock) , knowledge management , nanotechnology , engineering , artificial intelligence , materials science , biology , telecommunications , physics , power (physics) , computer security , quantum mechanics , evolutionary biology , electrical engineering , programming language
The construction of conformation-performance relationships (CPRs) is the core issue of efficient material design. Different from traditional CPRs originating from experience summarizations or rational model cognitions, a strategy of generalized low-dimensional conformation-performance relationships (GLD-CPRs) for rapid material discovery has been established in this work by the aid of material information technology, providing us with a new perspective to material gene (MG) and its function on performance. From a pure computing perspective, the strategy has been concreted by a case study of semiconductor bandgap engineering. Based on more than 20,000 items of self-produced semiconductor bandgap and attributes data, a set of CPRs for bandgap regulation towards arbitrary semiconductor of the same kind has be generated using GLD-CPRs strategy. By comparing with the reported studies, the reliability of the strategy is confirmed. Finally, future improvements needed by GLD-CPRs are addressed.