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