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2 claims from 1 paper

1. Materials Science › Machine Learning in Materials Science

   Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error

   Faber, Hutchison, Huang et al. · Journal of Chemical Theory and Computation · 2017

   The paper compares regressors and molecular representations for ML models of thirteen properties of organic molecules from QM9, using learning curves with up to about 117k training molecules.

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   1. UncheckedML models trained on hybrid DFT data predicted thirteen molecular properties with errors smaller than the typical gap between DFT and experiment.“We present numerical evidence that ML model predictions deviate from DFT less than DFT deviates from experiment for all properties.”
   2. UncheckedThe models' out-of-sample errors against hybrid DFT reference values are on par with, or close to, chemical accuracy, the paper says.“Furthermore, our out-of-sample prediction errors with respect to hybrid DFT reference are on par with, or close to, chemical accuracy.”