基于变分能量最小化的神经网络密度泛函理论
Neural-network Density Functional Theory Based on Variational Energy Minimization
AI总结：
针对深度学习密度泛函理论依赖数据驱动监督学习、与DFT发展割裂的问题，本研究提出统一神经网络优化与DFT变分计算的理论框架，开发融合物理信息的可微分DFT代码，实现无监督学习，为该领域提供新范式。
摘要：
深度学习密度泛函理论（DFT）有望大幅加速材料发现，甚至可能革新材料研究领域。但目前该领域研究主要依赖数据驱动的监督学习，使得神经网络与DFT的发展相互割裂。本研究提出神经网络DFT的理论框架，将神经网络优化与DFT变分计算相统一，实现了融合物理信息的无监督学习。此外，我们开发了集成深度学习DFT哈密顿量的可微分DFT代码，将自动微分与反向传播算法引入DFT领域，验证了神经网络DFT的可行性。这种融合物理信息的神经网络架构不仅在精度和效率上超越传统方法，还为深度学习DFT方法的发展提供了新范式。
英文摘要：
Deep-learning density functional theory (DFT) shows great promise to significantly accelerate material discovery and potentially revolutionize materials research. However, current research in this field primarily relies on data-driven supervised learning, making the developments of neural networks and DFT isolated from each other. In this work, we present a theoretical framework of neural-network DFT, which unifies the optimization of neural networks with the variational computation of DFT, enabling physics-informed unsupervised learning. Moreover, we develop a differential DFT code incorporated with deep-learning DFT Hamiltonian, and introduce algorithms of automatic differentiation and backpropagation into DFT, demonstrating the capability of neural-network DFT. The physics-informed neural-network architecture not only surpasses conventional approaches in accuracy and efficiency, but also offers a new paradigm for developing deep-learning DFT methods.
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