PIRSA:16080014 Conference Condensed Matter

DOI10.48660/16080014

Conference/School: Quantum Machine Learning

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

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

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

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.

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