Review on Perovskite-Type Compound Using Machine Learning-Reference-Cited by-同舟云学术

Review on Perovskite-Type Compound Using Machine Learning

Published:2022-06-01 Issue:6 Volume:14 Page:1001-1017
ISSN:1947-2935
Container-title:Science of Advanced Materials
language:en
Short-container-title:sci adv mater

Author:

Zhang Taohong¹,Guo Xueqiang¹,Zheng Han²,Liu Yun³,Wulamu Aziguli¹,Chen Han¹,Guo Xuxu¹,Zhang Zhizhuo⁴

Affiliation:

1. Department of Computer, School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing, 100083, China

2. Key Laboratory of AI and Information Processing (Hechi University), Education Department of Guangxi Zhuang Autonomous Region, HeChi University, Hechi, 546300, Guangxi, China

3. Department of Traditional Chinese Medicine, Beijing Haidian Hospital, Beijing, 100080, China

4. School of Economics and Management, University of Science and Technology Beijing (USTB), Beijing, 100083, China

Abstract

Perovskite is a kind of promising class of materials nowadays because of its exciting performance in energy, catalysis, semiconductor, and many other areas. Machine learning is a potential method by using big data to mine the deep hidden laws of the data and make some predictions of the new data. Applying machine learning method in perovskite is a meaningful attempt to explore the new material with new properties and to predict the properties of new materials. This review shows recent progress of perovskite using machine learning, and these attempts show the success of combining big data technique and material science which give us the new direction to explore the application of machine learning method and the new tools for material science.

Publisher

American Scientific Publishers

Subject

General Materials Science

Reference115 articles.

1. Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

2. Perovskite-perovskite tandem photovoltaics with optimized band gaps

3. Machine Learning for Predicting the Band Gaps of ABX3 Perovskites from Elemental Properties

4. Screening stable and metastable ABO3 perovskites using machine learning and the materials project

5. Searching for Hidden Perovskite Materials for Photovoltaic Systems by Combining Data Science and First Principle Calculations

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Machine-learning-based prediction of cubic perovskite formation energy and magnetism;SCIENTIA SINICA Technologica;2023-08-01

2. Optical Properties of Cu-Doped Perovskite Nanoplatelets;Journal of Nanoelectronics and Optoelectronics;2023-01-01