Predicting the Formability of Hybrid Organic–Inorganic Perovskites via an Interpretable Machine Learning Strategy
Shilin Zhang, Tian Lu, Pengcheng Xu, Qiuling Tao, Minjie Li, Wencong Lu
Abstract
Predicting the formability of perovskite structure for hybrid organic–inorganic perovskites (HOIPs) is a prominent challenge in the search for the required materials from a huge search space. Here, we propose an interpretable strategy combining machine learning with a shapley additive explanations (SHAP) approach to accelerate the discovery of potential HOIPs. According to the prediction of the best classification model, top-198 nontoxic candidates with a probability of formability (Pf) of >0.99 are screened from 18560 virtual samples. The SHAP analysis reveals that the radius and lattice constant of the B site (rB and LCB) are positively related to formability, while the ionic radius of the A site (rA), the tolerant factor (t), and the first ionization energy of the B site (I1B) have negative relations. The significant finding is that stricter ranges of t (0.84–1.12) and improved tolerant factor τ (critical value of 6.20) do exist for HOIPs, which are different from inorganic perovskites, providing a simple and fast assessment in the design of materials with an HOIP structure.