DeepH-2: Enhancing deep-learning electronic structure via an equivariant local-coordinate transformer

Understand

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.