Derivative Informed Learning of Exchange-Correlation Functionals

In three linesNew training method for machine-learned exchange-correlation functionals in quantum chemistry. DI-Loss supervises first and second energy derivatives to improve predictions. Results: 66% reduction in total-energy MAE, 19-35% improvement on excited-state predictions in TDDFT.

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