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.

翻译：密度泛函理论（DFT）是现代计算化学和材料科学的重要基础。然而，由于必须近似未知的交换关联（XC）泛函，DFT对实验可测性质的预测可靠性始终受到根本性限制。传统提高精度的范式依赖于日益复杂的手工构建泛函形式。这种方法导致了计算效率与精度之间长期存在的权衡，至今仍不足以对实验室实验进行可靠的预测建模。本文提出Skala——一种基于深度学习的交换关联泛函，在主族化学基准集GMTKN55上以2.8 kcal/mol的误差超越最先进的杂化泛函精度，同时保持半局域DFT的低计算成本特征。这种对历史精度-效率权衡的突破性背离，源于直接从数据中学习电子结构的非局域表示，从而规避了对日益昂贵的手工特征工程的需求。利用基于波函数方法的前所未有的大规模高精度参考数据，我们证实：随着训练数据集的扩展，现代深度学习能够实现系统性可改进的神经交换关联模型，使第一性原理模拟趋向更具预测性。