Transformer-generated atomic embeddings to enhance prediction accuracy of crystal properties with machine learning.

ABSTRACT: Accelerating the discovery of novel crystal materials by machine learning is crucial for advancing various technologies from clean energy to information processing. The machine-learning models for prediction of materials properties require embedding atomic information, while traditional methods have limited effectiveness in enhancing prediction accuracy. Here, we proposed an atomic embedding strategy called universal atomic embeddings (UAEs) for their broad applicability as atomic fingerprints, and generated the UAE tensors based on the proposed CrystalTransformer model. By performing experiments on widely-used materials database, our CrystalTransformer-based UAEs (ct-UAEs) are shown to accurately capture complex atomic features, leading to a 14% improvement in prediction accuracy on CGCNN

SUBMITTER: Jin L 

PROVIDER: S-EPMC11782585 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature