Brief

Researchers have developed a new method called Derivative Informed XC-Loss (DI-Loss) to improve the accuracy of machine-learned exchange-correlation functionals in computational chemistry. This technique incorporates information from the first and second derivatives of energy, leading to a significant reduction in energy errors and faster self-consistent field iterations. The improved functionals also show better performance in predicting excited states in downstream calculations. AI