基于深度学习的精确可扩展交换关联泛函

摘要

密度泛函理论（DFT）是现代计算化学与材料科学的重要基石。然而，由于需要对未知的交换关联（XC）泛函进行近似处理，DFT对实验可测量性质的预测可靠性始终存在根本性局限。传统提升精度的方法依赖于日益复杂的手工构建泛函形式，这种范式长期面临计算效率与精度之间的权衡困境，至今仍无法实现对实验室实验的可靠预测建模。本文提出基于深度学习的Skala交换关联泛函，其在主族化学基准集GMTKN55上以2.8 kcal/mol的误差超越最先进的混合泛函精度，同时保持半局域DFT的低计算成本特性。这种突破历史性精度-效率权衡的关键在于直接从数据中学习电子结构的非局域表示，规避了成本日益高昂的人工设计特征。通过利用波函数方法产生的前所未有的大规模高精度参考数据，我们证实现代深度学习能够随着训练数据集的扩展实现系统性可改进的神经交换关联模型，使第一性原理模拟逐步具备更强的预测能力。

English

Density Functional Theory (DFT) underpins much of modern computational chemistry and materials science. Yet, the reliability of DFT-derived predictions of experimentally measurable properties remains fundamentally limited by the need to approximate the unknown exchange-correlation (XC) functional. The traditional paradigm for improving accuracy has relied on increasingly elaborate hand-crafted functional forms. This approach has led to a longstanding trade-off between computational efficiency and accuracy, which remains insufficient for reliable predictive modelling of laboratory experiments. Here we introduce Skala, a deep learning-based XC functional that surpasses state-of-the-art hybrid functionals in accuracy across the main-group chemistry benchmark set GMTKN55 with an error of 2.8 kcal/mol, while retaining the lower computational cost characteristic of semi-local DFT. This demonstrated departure from the historical trade-off between accuracy and efficiency is enabled by learning non-local representations of electronic structure directly from data, bypassing the need for increasingly costly hand-engineered features. Leveraging an unprecedented volume of high-accuracy reference data from wavefunction-based methods, we establish that modern deep learning enables systematically improvable neural exchange-correlation models as training datasets expand, positioning first-principles simulations to become progressively more predictive.

基于深度学习的精确可扩展交换关联泛函

Accurate and scalable exchange-correlation with deep learning

摘要

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