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Machine learning (ML) has recently gained attention as a means to developmore accurate exchange-correlation (XC) functionals for density functionaltheory, but functionals developed thus far need to be improved on severalmetrics, including accuracy, numerical stability, and transferability acrosschemical space. In this work, we introduce a set of nonlocal features of thedensity called the CIDER formalism, which we use to train a Gaussian processmodel for the exchange energy that obeys the critical uniform scaling rule forexchange. The resulting CIDER exchange functional is significantly moreaccurate than any semi-local functional tested here, and it has goodtransferability across main-group molecules. This work therefore serves as aninitial step toward more accurate exchange functionals, and it also introducesuseful techniques for developing robust, physics-informed XC models via ML.

CIDER: An Expressive, Nonlocal Feature Set for Machine Learning Density Functionals with Exact Constraints