Explainable machine learning for 2D material layer group prediction with automated descriptor selection

Crystal symmetry is a fundamental aspect of material properties and plays a pivotal role in the discovery and design of new materials. Existing approaches for predicting the symmetries of two-dimensional (2D) materials typically focus on space groups, which often overlook the distinct in-plane and out-of-plane symmetries inherent to 2D systems. To address this, we present Au2LaP (Automated Descriptor Selection Enhanced 2D Material Layer Group Predictor), the first machine learning framework designed to predict layer groups of 2D materials directly from their chemical composition. Au2LaP integrates Light Gradient Boosting Machine (LightGBM) algorithm with SHapley Additive exPlanations (SHAP) for automated descriptor selection, optimizing both predictive accuracy and model interpretability. The explainability of Au2LaP ensures transparency by highlighting the most significant chemical descriptors contributing to layer group classification, thereby enhancing its utility for material discovery and design. Au2LaP outperforms seven state-of-the-art models based on chemical composition, achieving a top-1 accuracy of 0.8102 and a top-3 accuracy of 0.9048. Remarkably, it delivers superior performance using only 20 key descriptors, outperforming models trained with descriptor sets as large as 546. Furthermore, we demonstrate that Au2LaP can effectively predict polymorph structures by identifying multiple possible layer groups for given compositions, further advancing the predictive modelling of new material phases. This work sets a new benchmark for 2D material symmetry prediction, paving the way for more efficient crystal structure prediction, polymorph studies, and material design.