1. DeepH-2: Enhancing deep-learning electronic structure via an equivariant local-coordinate transformer
- Author
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Wang, Yuxiang, Li, He, Tang, Zechen, Tao, Honggeng, Wang, Yanzhen, Yuan, Zilong, Chen, Zezhou, Duan, Wenhui, and Xu, Yong
- Subjects
Physics - Computational Physics ,Condensed Matter - Materials Science - Abstract
Deep-learning electronic structure calculations show great potential for revolutionizing the landscape of computational materials research. However, current neural-network architectures are not deemed suitable for widespread general-purpose application. Here we introduce a framework of equivariant local-coordinate transformer, designed to enhance the deep-learning density functional theory Hamiltonian referred to as DeepH-2. Unlike previous models such as DeepH and DeepH-E3, DeepH-2 seamlessly integrates the simplicity of local-coordinate transformations and the mathematical elegance of equivariant neural networks, effectively overcoming their respective disadvantages. Based on our comprehensive experiments, DeepH-2 demonstrates superiority over its predecessors in both efficiency and accuracy, showcasing state-of-the-art performance. This advancement opens up opportunities for exploring universal neural network models or even large materials models.
- Published
- 2024