ausblenden:
Schlagwörter:
Density functional theory; Laser Doppler velocimetry; Ground states; Manifolds; Dissociation energies; Bond cleavage; Machine learning; Interpolation; Molecular dynamics
Zusammenfassung:
Using a one-dimensional model, we explore the ability of machine learning to approximate the non-interacting kinetic energy density functional of diatomics. This nonlinear interpolation between Kohn-Sham reference calculations can (i) accurately dissociate a diatomic, (ii) be systematically improved with increased reference data and (iii) generate accurate self-consistent densities via a projection method that avoids directions with no data. With relatively few densities, the error due to the interpolation is smaller than typical errors in standard exchange-correlation functionals.
