Predicting Many Crystal Properties via an Adaptive Transformer-based Framework

Understand

Machine learning has revolutionized many fields, including materials science.

• However, predicting properties of crystalline materials using machine learning faces challenges in input encoding, output versatility, and interpretability.
• We introduce CrystalBERT, an adaptable transformer-based framework integrating space group, elemental, and unit cell information.
• This novel structure can seamlessly combine diverse features and accurately predict various physical properties, including topological properties, superconducting transition temperatures, dielectric constants, and more.