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Keyword: atomic energy ×Clear all

2 claims from 1 paper

1. Materials Science › Machine Learning in Materials Science

   Quantum-chemical insights from deep tensor neural networks

   Schütt, Arbabzadah, Chmiela, Müller and Tkatchenko · Nature Communications · 2017

   The authors build a deep tensor neural network for molecules that predicts quantum-mechanical properties and gives chemically resolved insights, such as a stability classification of aromatic rings.

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   1. UncheckedCombining many-body Hamiltonian ideas with deep tensor neural networks gives size-extensive predictions accurate to 1 kcal/mol for medium-sized molecules.“We unify concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks (DTNN), which leads to size-extensive and uniformly accurate (1 kcal/mol) predictions in compositional and configurational chemical space for molecules of intermediate size.”
   2. UncheckedA deep tensor neural network trained on molecular data is reported to sort aromatic rings by stability, a property not labelled in its training data.“As an example of chemical relevance, the DTNN model reveals a classification of aromatic rings with respect to their stability -- a useful property that is not contained as such in the training dataset.”