Wang Juan, Wang Xinzhong, Feng Shun, Miao Zongcheng
Xi'an Key Laboratory of Advanced Photo-Electronics Materials and Energy Conversion Device, School of Electronic Information, Xijing University, Xi'an 710123, China.
School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi'an 710072, China.
Molecules. 2024 Jun 22;29(13):2974. doi: 10.3390/molecules29132974.
As an important photovoltaic material, organic-inorganic hybrid perovskites have attracted much attention in the field of solar cells, but their instability is one of the main challenges limiting their commercial application. However, the search for stable perovskites among the thousands of perovskite materials still faces great challenges. In this work, the energy above the convex hull values of organic-inorganic hybrid perovskites was predicted based on four different machine learning algorithms, namely random forest regression (RFR), support vector machine regression (SVR), XGBoost regression, and LightGBM regression, to study the thermodynamic phase stability of organic-inorganic hybrid perovskites. The results show that the LightGBM algorithm has a low prediction error and can effectively capture the key features related to the thermodynamic phase stability of organic-inorganic hybrid perovskites. Meanwhile, the Shapley Additive Explanation (SHAP) method was used to analyze the prediction results based on the LightGBM algorithm. The third ionization energy of the B element is the most critical feature related to the thermodynamic phase stability, and the second key feature is the electron affinity of ions at the X site, which are significantly negatively correlated with the predicted values of energy above the convex hull (E). In the screening of organic-inorganic perovskites with high stability, the third ionization energy of the B element and the electron affinity of ions at the X site is a worthy priority. The results of this study can help us to understand the correlation between the thermodynamic phase stability of organic-inorganic hybrid perovskites and the key features, which can assist with the rapid discovery of highly stable perovskite materials.
作为一种重要的光伏材料，有机-无机杂化钙钛矿在太阳能电池领域备受关注，但其不稳定性是限制其商业应用的主要挑战之一。然而，在数千种钙钛矿材料中寻找稳定的钙钛矿仍然面临巨大挑战。在这项工作中，基于四种不同的机器学习算法，即随机森林回归（RFR）、支持向量机回归（SVR）、XGBoost回归和LightGBM回归，预测了有机-无机杂化钙钛矿凸包值以上的能量，以研究有机-无机杂化钙钛矿的热力学相稳定性。结果表明，LightGBM算法具有较低的预测误差，能够有效捕捉与有机-无机杂化钙钛矿热力学相稳定性相关的关键特征。同时，基于LightGBM算法，采用Shapley加性解释（SHAP）方法对预测结果进行分析。B元素的第三电离能是与热力学相稳定性相关的最关键特征，第二个关键特征是X位点离子的电子亲和能，它们与凸包以上能量（E）的预测值显著负相关。在筛选高稳定性有机-无机钙钛矿时，B元素的第三电离能和X位点离子的电子亲和能是值得优先考虑的。本研究结果有助于我们理解有机-无机杂化钙钛矿的热力学相稳定性与关键特征之间的相关性，可协助快速发现高稳定性的钙钛矿材料。
