Applying a Well-Defined Energy Density for Machine-Learned Density Functionals

The recent integration of machine learning techniques in density functional theory (DFT) has established a powerful framework for developing next generation density functionals. While robust modelling of the exchange-correlation requires a well-defined energy density, conventional training sets usually rely on global quantities. We propose the application of the local slope in the non-interacting limit of the adiabatic connection approach in DFT [1]. The talk will elucidate the methods for an efficient implementation of this quantity, with a focus on its spin-resolved components and its regularized version. Furthermore, we will highlight the potential of this strategy in paving the way for the next generation of machine-learned local dynamic hybrid functionals. Our results show a marked improvement in the prediction of observables while also maintaining computational efficiency.

References:

[1] S. Vuckovic, T. J. P. Irons, A. Savin, A. M. Teale, and P. Gori-Giorgi, “Exchange–Correlation Functionals via Local Interpolation along the Adiabatic Connection”, Journal of Chemical Theory and Computation 12, 2598–2610 (2016).