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Finding Density Functionals with Machine Learning

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2012

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Snyder, John C.
Hansen, Katja
Müller, Klaus-Robert
Burke, Kieron

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https://doi.org/10.1103/PhysRevLett.108.253002
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http://arxiv.org/abs/1112.5441

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Physical Review Letters. American Physical Society (APS). 2012, 108(25), 253002. ISSN 0031-9007. eISSN 1079-7114. Available under: doi: 10.1103/PhysRevLett.108.253002

Zusammenfassung

Machine learning is used to approximate density functionals. For the model problem of the kinetic energy of noninteracting fermions in 1D, mean absolute errors below 1  kcal/mol on test densities similar to the training set are reached with fewer than 100 training densities. A predictor identifies if a test density is within the interpolation region. Via principal component analysis, a projected functional derivative finds highly accurate self-consistent densities. The challenges for application of our method to real electronic structure problems are discussed.

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ISO 690SNYDER, John C., Matthias RUPP, Katja HANSEN, Klaus-Robert MÜLLER, Kieron BURKE, 2012. Finding Density Functionals with Machine Learning. In: Physical Review Letters. American Physical Society (APS). 2012, 108(25), 253002. ISSN 0031-9007. eISSN 1079-7114. Available under: doi: 10.1103/PhysRevLett.108.253002
BibTex
@article{Snyder2012-06-22Findi-52164,
  year={2012},
  doi={10.1103/PhysRevLett.108.253002},
  title={Finding Density Functionals with Machine Learning},
  number={25},
  volume={108},
  issn={0031-9007},
  journal={Physical Review Letters},
  author={Snyder, John C. and Rupp, Matthias and Hansen, Katja and Müller, Klaus-Robert and Burke, Kieron},
  note={Article Number: 253002}
}
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