Predicting the synthesizability of inorganic crystals by bridging crystal graphs and phonon dynamics

Accurately predicting the synthesizability of inorganic crystal materials serves as a pivotal tool for the efficient screening of viable candidates which can substantially minimize the costs associated with extensive experimental trial-and-error processes. However, existing methods, limited by static structural descriptors such as chemical composition and lattice parameters, fail to account for atomic vibrations, which may introduce spurious correlations and limit predictive reliability. Here, we propose IGDS, a deep learning model for predicting the synthesizability of inorganic crystals. IGDS employs graph representation learning to construct crystal graphs that precisely capture the static structures of crystals and integrates phonon spectra features extracted from pre-trained machine learning interatomic potentials to capture the dynamic properties. Our model exhibits outstanding performance in predicting the synthesizability of low-energy unsynthesizable crystals across 41 material systems, with precision and recall values of 0.916/0.863 for ternary compounds. By capturing both static structural descriptors and dynamic features, IGDS provides a physics-informed method for predicting the synthesizability of inorganic crystals. This approach bridges the gap from theoretical design concepts to their practical and tangible implementation, thereby streamlining the development cycle of new materials and enhancing overall research efficiency.