DeepH-pack: A general-purpose neural network package for deep-learning electronic structure calculations

TL;DR

DeepH-pack is a unified software framework that combines first-principles calculations with deep learning, enabling fast, accurate, and generalizable electronic structure predictions across diverse materials.

Contribution

It introduces a comprehensive package integrating physics-informed neural networks for scalable and transferable electronic structure calculations.

Findings

Achieves orders-of-magnitude speedup in calculations.

Ensures robustness and generalizability across materials.

Maintains first-principles accuracy in predictions.

Abstract

In computational physics and materials science, first-principles methods, particularly density functional theory, have become central tools for electronic structure prediction and materials design. Recently, rapid advances in artificial intelligence (AI) have begun to reshape the research landscape, giving rise to the emerging field of deep-learning electronic structure calculations. Despite numerous pioneering studies, the field remains in its early stages; existing software implementations are often fragmented, lacking unified frameworks and standardized interfaces required for broad community adoption. Here we present DeepH-pack, a comprehensive and unified software package that integrates first-principles calculations with deep learning. By incorporating fundamental physical principles into neural-network design, such as the nearsightedness principle and the equivariance principle, DeepH-pack achieves robust cross-scale and cross-material generalizability. This allows models trained on small-scale structures to generalize to large-scale and previously unseen materials. The toolkit preserves first-principles accuracy while accelerating electronic structure calculations by several orders of magnitude, establishing an efficient and intelligent computational paradigm for large-scale materials simulation, high-throughput materials database construction, and AI-driven materials discovery.