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Keyword: organic compounds ×Clear all

1 claim from 1 paper

1. Materials Science › Machine Learning in Materials Science

   ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

   Smith, Isayev and Roitberg · Chemical Science · 2017

   The authors train a neural network potential, ANI-1, on DFT data for small organic molecules and report that it predicts energies accurately for much larger molecules than it was trained on.

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   1. UncheckedThe ANI-1 neural network matched reference DFT energies on molecules of up to 54 atoms, far larger than its training set, with errors as low as 0.56 kcal/mol.“Through a series of case studies, we show that ANI-1 is chemically accurate compared to reference DFT calculations on much larger molecular systems (up to 54 atoms) than those included in the training data set, with root mean square errors as low as 0.56 kcal/mol.”