Advancing metal halide perovskite development with machine learning

2021 ◽  
Author(s):  
Meghna Srivastava ◽  
John M. Howard ◽  
Tao Gong ◽  
Erica Lee ◽  
Qiong Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Metal Halide ◽  
Metal Halide Perovskite ◽  
Halide Perovskite

scholarly journals Robot-Accelerated Perovskite Investigation and Discovery (RAPID): 1. Inverse Temperature Crystallization

2019 ◽  
Author(s):  
Zhi Li ◽  
Mansoor Ani Najeeb ◽  
Liana Alves ◽  
Alyssa Sherman ◽  
Peter Cruz Parrilla ◽  
...  
Keyword(s):  
Machine Learning ◽  
Single Crystal ◽  
High Throughput ◽  
Metal Halide ◽  
Crystal Formation ◽  
Inverse Temperature ◽  
High Quality ◽  
Metal Halide Perovskite ◽  
Halide Perovskite ◽  
Temperature Crystallization

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.

scholarly journals Robot-Accelerated Perovskite Investigation and Discovery (RAPID): 1. Inverse Temperature Crystallization

2019 ◽  
Author(s):  
Zhi Li ◽  
Mansoor Ani Najeeb ◽  
Liana Alves ◽  
Alyssa Sherman ◽  
Peter Cruz Parrilla ◽  
...  
Keyword(s):  
Machine Learning ◽  
Single Crystal ◽  
High Throughput ◽  
Metal Halide ◽  
Crystal Formation ◽  
Inverse Temperature ◽  
High Quality ◽  
Metal Halide Perovskite ◽  
Halide Perovskite ◽  
Temperature Crystallization

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.

Hollow Metal Halide Perovskite Nanocrystals with Efficient Blue Emissions

2019 ◽  
Author(s):  
Michael Worku ◽  
Yu Tian ◽  
Chenkun Zhou ◽  
Haoran Lin ◽  
Maya Chaaban ◽  
...  
Keyword(s):  
Precursor Solution ◽  
Visible Spectral Region ◽  
Metal Halide ◽  
Synthetic Control ◽  
Perovskite Nanocrystals ◽  
Highly Efficient ◽  
Quantum Confinement Effects ◽  
Metal Halide Perovskite ◽  
Lead Bromide ◽  
Halide Perovskite

Metal halide perovskite nanocrystals (NCs) have emerged as a new generation light emitting materials with narrow emissions and high photoluminescence quantum efficiencies (PLQEs). Various types of perovskite NCs, e.g. platelets, wires, and cubes, have been discovered to exhibit tunable emissions across the whole visible spectral region. Despite remarkable advances in the field of metal halide perovskite NCs over the last few years, many nanostructures in inorganic NCs have yet been realized in metal halide perovskites and producing highly efficient blue emitting perovskite NCs remains challenging and of great interest. Here we report for the first time the discovery of highly efficient blue emitting cesium lead bromide perovskite (CsPbBr3) NCs with hollow structures. By facile solution processing of cesium lead bromide perovskite precursor solution containing additional ethylenediammonium bromide and sodium bromide, in-situ formation of hollow CsPbBr3 NCs with controlled particle and pore sizes is realized. Synthetic control of hollow nanostructures with quantum confinement effects results in color tuning of CsPbBr3 NCs from green to blue with high PLQEs of up to 81 %.<br><div><br></div>

Recent progress on defect modulation for highly efficient metal halide perovskite light-emitting diodes

2021 ◽  
Vol 22 ◽  
pp. 100946
Author(s):  
Le Ma ◽  
Boning Han ◽  
Fengjuan Zhang ◽  
Leimeng Xu ◽  
Tao Fang ◽  
...  
Keyword(s):  
Recent Progress ◽  
Light Emitting Diodes ◽  
Metal Halide ◽  
Light Emitting ◽  
Highly Efficient ◽  
Metal Halide Perovskite ◽  
Halide Perovskite

Core/Shell Metal Halide Perovskite Nanocrystals for Optoelectronic Applications

2021 ◽  
pp. 2100438
Author(s):  
Chengxi Zhang ◽  
Jiayi Chen ◽  
Lingmei Kong ◽  
Lin Wang ◽  
Sheng Wang ◽  
...  
Keyword(s):  
Metal Halide ◽  
Core Shell ◽  
Perovskite Nanocrystals ◽  
Metal Halide Perovskite ◽  
Halide Perovskite ◽  
Optoelectronic Applications

scholarly journals Author Correction: Controllable deposition of organic metal halide perovskite films with wafer-scale uniformity by single source flash evaporation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Woocheol Lee ◽  
Jonghoon Lee ◽  
Hyeon-Dong Lee ◽  
Junwoo Kim ◽  
Heebeom Ahn ◽  
...  
Keyword(s):  
Metal Halide ◽  
Single Source ◽  
Organic Metal ◽  
Flash Evaporation ◽  
Wafer Scale ◽  
Metal Halide Perovskite ◽  
Halide Perovskite

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Efficient All-inorganic Perovskite Light-emitting Diodes Enabled by Manipulating Crystal Orientation

2021 ◽  
Author(s):  
Wenjing Feng ◽  
Kebin Lin ◽  
Wenqiang Li ◽  
Xiangtian Xiao ◽  
Jianxun Lu ◽  
...  
Keyword(s):  
Active Layer ◽  
Crystal Orientation ◽  
Light Emitting Diodes ◽  
Metal Halide ◽  
Device Performance ◽  
Light Emitting ◽  
Inorganic Perovskite ◽  
Metal Halide Perovskite ◽  
Halide Perovskite

Metal halide perovskite light-emitting diodes (PeLEDs) are promising in lighting and display application, and the corresponding device performance is highly dependent on the film quality of the active layer. However,...

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