Bayesian error estimation functional (BEEF)ΒΆ

Who:Jess, Keld

We use methods inspired from Bayesian statistics and a large number of datasets of molecular, surface chemical, and solid state materials properties to construct new exchange-correlation density functionals. An essential feature of these functionals is an ensemble of functionals around the optimum one, which allows an estimate of the computational error to be easily calculated in a non-self-consistent fashion.

We have recently designed the BEEF-vdW, a GGA with vdW-DF2 type nonlocal correlation. It is fully implemented in GPAW, as is the ensemble error estimate.

Presently and in the future we will expand on this work by considering:

  • metaGGA density functionals, which us the klinetic energy density
  • self-interaction correction methods, e.g., +U, non-Koopman corrections, etc.

Most of the GPAW code related to this project is in bee.py and ensemble_gga.c.