Formation energy prediction of material crystal structures using deep learning

Abstract Determining the stability of chemical compounds is essential for advancing material discovery. In this study, we introduce a novel deep neural network model designed to predict a crystal’s formation energy, which identifies its stability property. Our model leverages elemental fractions derived from material composition and incorporates the crystallographic symmetry (space group) and stability labels (ground state(stable), metastable, unstable) as an additional input feature. The materials’ symmetry classifications represent the crystal polymorphs and are crucial for understanding phase transitions in materials. The inclusion of symmetry information markedly enhanced the model’s predictive accuracy. At the same time, the addition of stability labels further improved performance, demonstrating that combining compositional, structural, and thermodynamic descriptors yields a more robust prediction framework. These findings underscore the crucial role of crystallographic symmetry and stability information in enhancing the accuracy and interpretability of deep learning models for predicting materials stability.