Author Correction: Graph neural networks for an accurate and interpretable prediction of the properties of polycrystalline materials (npj Computational Materials, (2021), 7, 1, (103), 10.1038/s41524-021-00574-w)

Abstract

The authors became aware that the message passing (via the term (Formula presented.)) between neighboring nodes was not implemented in the layer-wise update function (Equation (1)) due to an error in the original code of the graph neural network (GNN) model. After code modification, the following changes have been made to the original version of this Article: Figure 4 depicts the property prediction by the trained GNN model. The correct version of Figure 4 appears below: The sixth sentence of the first paragraph of the “Property prediction by the GNN model” section originally stated “The MARE for these data is as low as 8.05%”. In the corrected version, “as low as 8.05%” is replaced by “as low as 8.24%”. The tenth sentence of the first paragraph of the “Property prediction by the GNN model” section originally stated “As shown in Fig. 4c, the average value of the MARE quickly decreases from 17% to ~10%”. In the corrected version, “17% to ~10%” is replaced by “~16% to ~ 12%”.