Identifying Pb-free perovskites for solar cells by machine learning

• Published in: npj computational materials Vol. 5; no. 1
• Main Authors: Im, Jino, Lee, Seongwon, Ko, Tae-Wook, Kim, Hyun Woo, Hyon, YunKyong, Chang, Hyunju
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 26.03.2019; Nature Publishing Group
• Subjects: 639/301/1034/1037; 639/301/1034/1038; Algorithms; Artificial intelligence; Characterization and Evaluation of Materials; Chemistry and Materials Science; Computational Intelligence; Datasets; Heat of formation; Lead free; Learning algorithms; Machine learning; Material properties; Materials information; Materials Science; Mathematical and Computational Engineering; Mathematical and Computational Physics; Mathematical Modeling and Industrial Mathematics; Perovskites; Photovoltaic cells; Predictions; Problem solving; Regression analysis; Solar cells; Statistical analysis; Theoretical
• ISSN: 2057-3960; 2057-3960
• DOI: 10.1038/s41524-019-0177-0

Recent advances in computing power have enabled the generation of large datasets for materials, enabling data-driven approaches to problem-solving in materials science, including materials discovery. Machine learning is a primary tool for manipulating such large datasets, predicting unknown material properties and uncovering relationships between structure and property. Among state-of-the-art machine learning algorithms, gradient-boosted regression trees (GBRT) are known to provide highly accurate predictions, as well as interpretable analysis based on the importance of features. Here, in a search for lead-free perovskites for use in solar cells, we applied the GBRT algorithm to a dataset of electronic structures for candidate halide double perovskites to predict heat of formation and bandgap. Statistical analysis of the selected features identifies design guidelines for the discovery of new lead-free perovskites.