A look inside the black box: Using graph-theoretical descriptors to interpret a Continuous-Filter Convolutional Neural Network (CF-CNN) trained on the global and local minimum energy structures of neutral water clusters

The Journal of Chemical Physics
Jenna A BilbreySutanay Choudhury

Abstract

We describe a method for the post-hoc interpretation of a neural network (NN) trained on the global and local minima of neutral water clusters. We use the structures recently reported in a newly published database containing over 5 × 106 unique water cluster networks (H2O)N of size N = 3-30. The structural properties were first characterized using chemical descriptors derived from graph theory, identifying important trends in topology, connectivity, and polygon structure of the networks associated with the various minima. The code to generate the molecular graphs and compute the descriptors is available at https://github.com/exalearn/molecular-graph-descriptors, and the graphs are available alongside the original database at https://sites.uw.edu/wdbase/. A Continuous-Filter Convolutional Neural Network (CF-CNN) was trained on a subset of 500 000 networks to predict the potential energy, yielding a mean absolute error of 0.002 ± 0.002 kcal/mol per water molecule. Clusters of sizes not included in the training set exhibited errors of the same magnitude, indicating that the CF-CNN protocol accurately predicts energies of networks for both smaller and larger sizes than those used during training. The graph-theoretical descriptors w...Continue Reading

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Citations

May 5, 2021·The Journal of Chemical Physics·Michele CeriottiO Anatole von Lilienfeld
Oct 26, 2021·Physical Chemistry Chemical Physics : PCCP·Carine ClavaguéraGilles Ohanessian
Oct 19, 2021·Chemphyschem : a European Journal of Chemical Physics and Physical Chemistry·Jie HuangShiben Li

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