Accurate and scalable exchange-correlation with deep learning
Authors: name: Giulia Luise, name: Chin-Wei Huang, name: Thijs Vogels, name: Derk P. Kooi, name: Sebastian Ehlert, name: Stephanie Lanius, name: Klaas J. H. Giesbertz, name: Amir Karton, name: Deniz Gunceler, name: Stefano Battaglia, name: Gregor N. C. Simm, name: P. Bernát Szabó, name: Megan Stanley, name: Wessel P. Bruinsma, name: Lin Huang, name: Xinran Wei, name: José Garrido Torres, name: Abylay Katbashev, name: Rodrigo Chavez Zavaleta, name: Bálint Máté, name: Sékou-Oumar Kaba, name: Roberto Sordillo, name: Yingrong Chen, name: David B. Williams-Young, name: Christopher M. Bishop, name: Jan Hermann, name: Rianne van den Berg, name: Paola Gori-Giorgi
Organization: Microsoft

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.