Date: Friday, March 16, 2018
Location: 1084 East Hall (4:10 PM to 5:00 PM)
Title: Learning exchangecorrelation potentials from electron densities
Abstract: Density functional theory (DFT) provides a formally exact way of reducing the intractable manyelectron problem to an effective single electron problem, where the quantum manyelectron interactions are encapsulated into a meanfield, called the exchangecorrelation (xc). Although known to be unique functionals of the groundstate electronic charge density, rho(r), the exact form of these functionals  expressed either as energy (E_{xc}[rho(r)]) or potential (v_xc[rho(r)])  are unknown, necessitating the use of approximate functionals. The existing xc functionals, despite their success in predicting a wide range of materials properties, exhibit certain notable failures  underpredicted bandgaps, incorrect bonddissociation curves, wrong chargetransfer excitations, to name a few. Typically, these approximations are constructed through semiempirical parameter fitting in model systems, thereby making systematic improvement and conformity to certain known exact conditions difficult. We attempt to address this through datadriven modeling of xc functionals. This involves, generating a training data set comprising of rho(r) to v_xc(r) map, and then, use of machine learning algorithms to learn the functional form of vxc[ρ(r)] (and Exc[ρ(r)]), conforming to the exact conditions.
In this talk, I will present the details regarding generating the rho(r) to v_xc(r) map and then sketch out the basic idea behind a machinelearned xc functionals.
Files:
Speaker: Bikash Kanungo
Institution: University of Michigan
Event Organizer: Audra McMillan amcm@umich.edu
