Can Machines “Learn” Halide Perovskite Crystal Formation without Accurate Physicochemical Features?

2020 ◽  
Vol 124 (25) ◽  
pp. 13982-13992 ◽  
Author(s):  
Ian M. Pendleton ◽  
Mary K. Caucci ◽  
Michael Tynes ◽  
Aaron Dharna ◽  
Mansoor Ani Najeeb Nellikkal ◽  
...  
2021 ◽  
Author(s):  
Joshua Schrier ◽  
Philip Nega ◽  
Zhi Li ◽  
Victor Ghosh ◽  
Janak Thapa ◽  
...  

2021 ◽  
Vol 119 (4) ◽  
pp. 041903
Author(s):  
Philip W. Nega ◽  
Zhi Li ◽  
Victor Ghosh ◽  
Janak Thapa ◽  
Shijing Sun ◽  
...  

2019 ◽  
Author(s):  
Zhi Li ◽  
Mansoor Ani Najeeb ◽  
Liana Alves ◽  
Alyssa Sherman ◽  
Peter Cruz Parrilla ◽  
...  

Metal halide perovskites are a promising class of materials for next-generation photovoltaic and optoelectronic devices. The discovery and full characterization of new perovskite-derived materials are limited by the difficulty of growing high quality crystals needed for single-crystal X-ray diffraction studies. We present the first automated, high-throughput approach for metal halide perovskite single crystal discovery based on inverse temperature crystallization (ITC) as a means to rapidly identify and optimize synthesis conditions for the formation of high quality single crystals. Using this automated approach, a total of 1928 metal halide perovskite synthesis reactions were conducted using six organic ammonium cations (methylammonium, ethylammonium, n-butylammonium, formamidinium, guanidinium, and acetamidinium), increasing the number of metal halide perovskite materials accessible by ITC syntheses by three and resulting in the formation of a new phase, [C<sub>2</sub>H<sub>7</sub>N<sub>2</sub>][PbI<sub>3</sub>]. This comprehensive dataset allows for a statistical quantification of the total experimental space and of the likelihood of large single crystal formation. Moreover, this dataset enables the construction and evaluation of machine learning models for predicting crystal formation conditions. This work is a proof-of-concept that combining high throughput experimentation and machine learning accelerates and enhances the study of metal halide perovskite crystallization. This approach is designed to be generalizable to different synthetic routes for the acceleration of materials discovery.


2021 ◽  
Author(s):  
Yuiga Nakamura ◽  
Naoyuki Shibayama ◽  
Kunihisa Sugimoto

We observed the crystallization dynamics of halide perovskite crystals (CH3NH3PbI3) by in situ heating WAXS measurements.


2021 ◽  
Vol 2 (4) ◽  
pp. 100395
Author(s):  
Hsin-Hsiang Huang ◽  
Zhiyuan Ma ◽  
Joseph Strzalka ◽  
Yang Ren ◽  
King-Fu Lin ◽  
...  

2018 ◽  
Vol 6 (2) ◽  
pp. 234-241 ◽  
Author(s):  
Lei Zhang ◽  
Lei Xu ◽  
Fengxi Yu ◽  
Jingfa Li

The mechanisms of halide perovskite crystal crosslinking via molecular crosslinking agents are proposed using first principles calculations.


ACS Photonics ◽  
2020 ◽  
Vol 7 (3) ◽  
pp. 845-852 ◽  
Author(s):  
Kentaro Fujiwara ◽  
Shuai Zhang ◽  
Shun Takahashi ◽  
Limeng Ni ◽  
Akshay Rao ◽  
...  

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