Correction to: npj Computational Materials https://doi.org/10.1038/s41524-021-00650-1, published online 15 November 2021
The original version of this Article contained errors in values of ALIGNN data in Table 5. As a result, the following changes have been made to the original version of this Article:
In Table 5, the data for “OrbNetens5” column were removed and values for “ALIGNN” column were updated. The correct version of Table 5 appears below:
Which replaces the previous incorrect version:
Consequently, the last sentence in Abstract was stated “ALIGNN can outperform some previously reported GNN models on atomistic prediction tasks by up to 85% in accuracy with better or comparable model training speed.” In the corrected version, “by up to 85% in accuracy” was omitted.
The first sentence of the last paragraph of the “Model Performance” section originally stated “Next, we evaluate the ALIGNN model on QM9 molecular property dataset (130,829 molecules) and compare it with other well-known models such as SchNet10, MatErials Graph Network (MEGNet)16, OrbNet18, and DimeNet++20 as shown in Table 5.” In the corrected version, “OrbNet18,” was omitted.
A new sentence “The results from models other than ALIGNN are reported as given in corresponding papers, not necessarily reproduced by us.” was inserted after the first sentence of the last paragraph of the “Model Performance” section.
The fourth sentence of the last paragraph of the “Model Performance” section originally stated “ALIGNN outperforms competing methods on 9 out of 11 tasks, suggesting that it can be uniformly applied to both molecular as well as solid-state systems. On the QM9 U0 regression target, ALIGNN outperforms SchNet, MEGNet, DimeNet++ and OrbNet by 85%, 82.3%, 66.8%, and 46.2% respectively.” In the corrected version, the sentence stated “ALIGNN outperforms competing methods for HOMO and dipole moment tasks while other accuracies are similar to the SchNet model.”.
The changes have been made to both the PDF and HTML versions of the Article.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Choudhary, K., DeCost, B. Author Correction: Atomistic Line Graph Neural Network for improved materials property predictions. npj Comput Mater 8, 221 (2022). https://doi.org/10.1038/s41524-022-00913-5
Published:
DOI: https://doi.org/10.1038/s41524-022-00913-5
