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This ICML 2026 paper introduces Derivative Informed XC-Loss (DI-Loss), a training approach for machine-learned exchange-correlation functionals that incorporates first and second derivative supervision on the Grassmannian of density matrices. Across four architectures, DI-Loss reduces total-energy MAE by 66% compared to energy and density supervision alone, and improves excited-state predictions in TDDFT calculations.
Microsoft Research releases Skala, a deep-learning exchange-correlation functional for DFT that achieves 2.8 kcal/mol accuracy on GMTKN55 at semi-local cost, outperforming traditional functionals across broad chemistry benchmarks.