论文标题
E（3）的一般框架 - 密度功能理论哈密顿的神经网络表示
General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian
论文作者
论文摘要
深度学习和从头算计算的结合在彻底改变未来的科学研究方面表现出了巨大的希望，但是如何设计包含先验知识和对称要求的神经网络模型是一个关键的挑战。在这里，我们提出了一个E（3） - 等级深度学习框架，以表示密度功能理论（DFT）Hamiltonian作为材料结构的函数，即使在存在自旋轨道耦合的情况下，它也可以自然地保留欧几里得对称性。我们的DEEPH-E3方法通过从小型结构的DFT数据中学习，可以使大规模超级细胞（$> 10^4 $原子）的常规研究可行，从而实现了非常有效的电子结构计算。值得注意的是，该方法可以在高训练效率下达到子MEV预测的准确性，在我们的实验中显示出最先进的性能。这项工作不仅对深度学习方法开发具有一般意义，而且还为材料研究创造了新的机会，例如构建MoiréTwist的材料数据库。
Combination of deep learning and ab initio calculation has shown great promise in revolutionizing future scientific research, but how to design neural network models incorporating a priori knowledge and symmetry requirements is a key challenging subject. Here we propose an E(3)-equivariant deep-learning framework to represent density functional theory (DFT) Hamiltonian as a function of material structure, which can naturally preserve the Euclidean symmetry even in the presence of spin-orbit coupling. Our DeepH-E3 method enables very efficient electronic-structure calculation at ab initio accuracy by learning from DFT data of small-sized structures, making routine study of large-scale supercells ($> 10^4$ atoms) feasible. Remarkably, the method can reach sub-meV prediction accuracy at high training efficiency, showing state-of-the-art performance in our experiments. The work is not only of general significance to deep-learning method development, but also creates new opportunities for materials research, such as building Moiré-twisted material database.
