While density functional theory (DFT) serves as a prevalent computational approach in electronic structure calculations, its computational demands and scalability limitations persist. Recently, leveraging neural networks to parameterize the Kohn-Sham DFT Hamiltonian has emerged as a promising avenue for accelerating electronic structure computations. Despite advancements, challenges such as the necessity for computing extensive DFT training data to explore new systems and the complexity of establishing accurate ML models for multi-elemental materials still exist. Addressing these hurdles, this study introduces a universal electronic Hamiltonian model trained on Hamiltonian matrices obtained from first-principles DFT calculations of nearly all crystal structures on the Materials Project. We demonstrate its generality in predicting electronic structures across the whole periodic table, including complex multi-elemental systems. By offering a reliable efficient framework for computing electronic properties, this universal Hamiltonian model lays the groundwork for advancements in diverse fields related to electronic structures.

翻译：尽管密度泛函理论（DFT）在电子结构计算中被广泛采用，但其计算需求与可扩展性局限依然存在。近年来，利用神经网络参数化Kohn-Sham DFT哈密顿量已成为加速电子结构计算的前沿方向。然而，当前仍面临双重挑战：探索新体系时需计算海量DFT训练数据，以及为多元素材料构建精确机器学习模型的复杂性。针对这些难题，本研究提出一种通用电子哈密顿模型，该模型基于Materials Project中几乎所有晶体结构的第一性原理DFT哈密顿矩阵训练而成。我们证明了该模型在预测整个元素周期表（包括复杂多元素系统）电子结构层面的泛化能力。该通用哈密顿模型为电子性质计算提供了可靠高效的框架，为电子结构相关领域的突破奠定了重要基础。