电负性
钙钛矿(结构)
带隙
机器学习
人工智能
随机森林
材料科学
理论(学习稳定性)
钙钛矿太阳能电池
支持向量机
算法
计算机科学
物理
化学
光电子学
结晶学
量子力学
出处
期刊:Solar Energy
[Elsevier]
日期:2021-11-01
卷期号:228: 689-699
被引量:23
标识
DOI:10.1016/j.solener.2021.09.030
摘要
Perovskite solar cells have risen since 2013, which are urgently longing for lead-free perovskite materials discovery. Here, we propose a machine learning framework to investigate thermodynamic stability and band gap of lead-free halide double perovskites at high speed and high precision, analyze the importance of selected features and provide directions for discovering potential lead-free perovskites. Four different machine-learning algorithms are utilized, including random forest, ridge regression, support vector regression and XGBoost. XGBoost provides the highest predictive performance (R2:0.9935 and MAE:0.0126) for thermodynamic stability. Random forest provides the highest prediction performance (R2:0.9410 and MAE:0.1492) for band gap. Key features are extracted for exploring hidden structure-properties relationships. Thermodynamic stability and the most important feature of electronegativity are linearly correlated, and XGBoost performs best. Band gap and the extracted features of highest occupied energy level (hoe_b1) and cubic phase are non-linearly correlated, and random forest can well capture the non-linearly. This work demonstrates a great potential of machine learning for accelerating perovskite solar-cell materials discovery.
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