The Critical Role of Tilting Descriptors in Data‐Driven Property Prediction of Orthorhombic Perovskites

Abstract

Accurate prediction of the properties of all relevant polymorphs is necessary for rational design of stable, high‐performance perovskite materials. While the discovery of cubic perovskites has been hastened by machine learning (ML), the orthorhombic (Pnma) phase needed for device performance at room temperature has been little studied due to its complicated structural distortions. This work addresses this gap by developing a specialized ML framework for orthorhombic ABX3 halide perovskites. We assembled a curated dataset of 3000 Pnma structures and developed a set of 67 descriptors specifically describing octahedral tilting, anisotropic distortion, and electrical effects. A thorough benchmark of twelve algorithms revealed XGBoost to be the best model, yielding credible predictions for formation energy (test R2 = 0.939) and band gap (test R2 = 0.619). Interestingly, the recursive feature removal showed that a low‐dimensional electronic subspace controls formation energy, while the prediction of the band gap requires a complicated interplay of structural and electronic features. In contrast, prediction of thermodynamic stability (energy above hull) remained a challenge, revealing the limitations of existing compositional descriptors. This work introduces a verified and interpretable pathway for high‐throughput screening of orthorhombic perovskites and provides fundamental insight into the descriptor‐property connections governing different perovskite polymorphs.