(304e) Development of "Surrogate" Hybrid Functionals Based on Electron Density Convolutions

In this work we explore the potential of a new data-driven approach to the design of exchange-correlation (XC) functionals. The approach, inspired by "surrogate" functions from optimization and convolutional filters in computer vision, utilizes convolutions of the electron density in order to form a feature space to represent local electronic environments. These features are orbital free, and provide a systematic route to including information at various length scales. Dimensional reduction and regression models are utilized to quantitatively connect these orbital-free descriptors to the exchange-correlation energies and potentials extracted from hybrid functionals. In this talk the approach will be introduced and demonstrated for the construction of a PBE-B3LYP surrogate functional that is tested for small hydrocarbon molecules. The results suggest that this is a promising new route for designing exchange-correlation functionals and embedding higher level physics into density functional theory.