Video URL

https://pirsa.org/16080014

Finding density functionals with machine-learning

Burke, K. (2016). Finding density functionals with machine-learning. Perimeter Institute for Theoretical Physics. https://pirsa.org/16080014

Burke, Kieron. Finding density functionals with machine-learning. Perimeter Institute for Theoretical Physics, Aug. 11, 2016, https://pirsa.org/16080014 

          @misc{ scivideos_PIRSA:16080014,
            doi = {10.48660/16080014},
            url = {https://pirsa.org/16080014},
            author = {Burke, Kieron},
            keywords = {Quantum Matter},
            language = {en},
            title = {Finding density functionals with machine-learning},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2016},
            month = {aug},
            note = {PIRSA:16080014 see, \url{https://scivideos.org/pirsa/16080014}}
          }

Kieron Burke University of California System

DOI 10.48660/16080014
Source Repository PIRSA
Collection
Talk Type Conference
Subject

Abstract

Density functional theory (DFT) is an extremely popular approach to electronic structure problems in both materials science and chemistry and many other fields. Over the past several years, often in collaboration with Klaus Mueller at TU Berlin, we have explored using machine-learning to find the density functionals that must be approximated in DFT calculations. I will summarize our results so far, and report on two new works.