Accelerating the Discovery of Hybrid Perovskites with Targeted Band Gaps via Interpretable Machine Learning

Chao Yang; Xiaoyu Chong; Mingyu Hu; Wei Yu; Jingjin He; Yalan Zhang; Jing Feng; Yuanyuan Zhou; Lin-Wang Wang

doi:10.1021/acsami.3c06392

Accelerating the Discovery of Hybrid Perovskites with Targeted Band Gaps via Interpretable Machine Learning

ACS Appl Mater Interfaces. 2023 Aug 30;15(34):40419-40427. doi: 10.1021/acsami.3c06392. Epub 2023 Aug 18.

Authors

Chao Yang¹, Xiaoyu Chong¹, Mingyu Hu², Wei Yu¹, Jingjin He¹, Yalan Zhang², Jing Feng¹, Yuanyuan Zhou², Lin-Wang Wang³

Affiliations

¹ Faculty of Materials Science and Engineering, Kunming University of Science and Technology, Kunming 650093, China.
² Department of Physics, Hong Kong Baptist University, Kowloon, Hong Kong 999077, China.
³ Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, P. R. China.

PMID: 37594363
DOI: 10.1021/acsami.3c06392

Abstract

The band gap of hybrid organic-inorganic perovskites (HOIPs) is a key factor affecting the light absorption characteristics and thus the performance of perovskite solar cells (PSCs). However, band gap engineering, using experimental trial and error and high-throughput density functional theory calculations, is blind and costly. Therefore, it is critical to statistically identify the multiple factors influencing band gaps and to rationally design perovskites with targeted band gaps. This study combined feature engineering, the gradient-boosted regression tree (GBRT) algorithm, and the genetic algorithm-based symbolic regression (GASR) algorithm to develop an interpretable machine learning (ML) strategy for predicting the band gap of HOIPs accurately and quantitatively interpreting the factors affecting the band gap. Seven best physical features were selected to construct a GBRT model with a root-mean-square error of less than 0.060 eV, and the most important feature is the electronegativity difference between the B-site and the X-site (χ_B-X). Further, a mathematical formula (E_g = χ_B-X² + 0.881χ_B-X) was constructed with GASR for a quantitative interpretation of the band gap influence patterns. According to the ML model, the HOIP MA_0.23FA_0.02Cs_0.75Pb_0.59Sn_0.41Br_0.24I_2.76 was obtained, with a suitable band gap of 1.39 eV. Our proposed interpretable ML strategy provides an effective approach for developing HOIP structures with targeted band gaps, which can also be applied to other material fields.

Keywords: GBRT; band gap; machine learning; perovskite solar cell; symbolic regression.