Enhancing crystal structure prediction by leveraging computational and experimental data: a combination of deep learning and optimization algorithms
The determination of material crystal structures has long been a central focus in disciplines such as physics, chemistry, and materials science. In this study, we introduce an efficient crystal structure prediction method that combines physics-inspired heuristics with advanced deep learning models, utilizing extensive quantum mechanics material databases and experimental data. By incorporating both enthalpy of formation and synthesizability, our approach enables more accurate predictions of crystal structures that exhibit structural stability and are more likely to be successfully synthesized in experiments. In parallel, we integrate intelligent optimization algorithms to enhance the model's efficiency in searching for potential structures within the vast chemical space. This comprehensive approach, which merges advanced deep learning models with optimization algorithms while considering factors such as thermodynamic stability and synthesizability, holds significant promise for substantially improving the predictive performance of crystal structures.