Improving the Accuracy and Efficiency of Nonlocal Exchange Functionals via Machine Learning

Machine Learning (ML) is a promising approach to improve the accuracy of exchange-correlation (XC) functionals for Density Functional Theory (DFT). In this talk, we summarize recent developments in the CIDER formalism, an approach for developing ML exchange functionals that leverages nonlocal information about the density while allowing physical constraints on the functional to be enforced. In particular, we discuss how the features used in the model have been adjusted to improve their transferability and minimize spurious dependence of the valence electrons on the core electron distribution. We also investigate how the accuracy and transferability of the Gaussian Process ML models can be optimized by training to both the exchange energy densities and the total exchange energies of molecules. Lastly, we explore the efficient implementation of CIDER models in plane-wave DFT codes through an auxiliary basis approach similar to that used for van der Waals functionals.