Machine learning prediction errors better than DFT accuracy

  title={Machine learning prediction errors better than DFT accuracy},
  author={F. Faber and Luke Hutchison and B. Huang and J. Gilmer and S. Schoenholz and G. Dahl and Oriol Vinyals and Steven M. Kearnes and P. Riley and O. A. V. Lilienfeld},
  journal={arXiv: Chemical Physics},
  • F. Faber, Luke Hutchison, +7 authors O. A. V. Lilienfeld
  • Published 2017
  • Chemistry, Physics
  • arXiv: Chemical Physics
  • We investigate the impact of choosing regressors and molecular representations for the construction of fast machine learning (ML) models of thirteen electronic ground-state properties of organic molecules. The performance of each regressor/representation/property combination is assessed using learning curves which report out-of-sample errors as a function of training set size with up to $\sim$117k distinct molecules. Molecular structures and properties at hybrid density functional theory (DFT… CONTINUE READING
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