Microstructure Maps of Complex Perovskite Materials from Extensive Monte Carlo Sampling Using Machine Learning Enabled Energy Model

2021 ◽  
Vol 12 (14) ◽  
pp. 3591-3599
Author(s):  
Hsin-An Chen ◽  
Ping-Han Tang ◽  
Guan-Jie Chen ◽  
Chien-Cheng Chang ◽  
Chun-Wei Pao
2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Qiuling Tao ◽  
Pengcheng Xu ◽  
Minjie Li ◽  
Wencong Lu

AbstractThe development of materials is one of the driving forces to accelerate modern scientific progress and technological innovation. Machine learning (ML) technology is rapidly developed in many fields and opening blueprints for the discovery and rational design of materials. In this review, we retrospected the latest applications of ML in assisting perovskites discovery. First, the development tendency of ML in perovskite materials publications in recent years was organized and analyzed. Second, the workflow of ML in perovskites discovery was introduced. Then the applications of ML in various properties of inorganic perovskites, hybrid organic–inorganic perovskites and double perovskites were briefly reviewed. In the end, we put forward suggestions on the future development prospects of ML in the field of perovskite materials.


2021 ◽  
Vol 263 ◽  
pp. 107908
Author(s):  
Marco Lazzarin ◽  
Simone Alioli ◽  
Stefano Carrazza
Keyword(s):  

2015 ◽  
Vol 34 (4) ◽  
pp. 1-12 ◽  
Author(s):  
Nima Khademi Kalantari ◽  
Steve Bako ◽  
Pradeep Sen

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