Addition/Correction Cite This: J. Phys. Chem. Lett. 2019, 10, 2066−2067
pubs.acs.org/JPCL
Correction to “Fast and Accurate Molecular Property Prediction: Learning Atomic Interactions and Potentials with Neural Networks” Masashi Tsubaki* and Teruyasu Mizoguchi* J. Phys Chem. Lett., 2018, 9 (19), 5733−5741. DOI: 10.1021/acs.jpclett.8b01837.
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n our original Letter,1 we reported the results in the unit of “Hartree” not “eV” for the following predicted molecular properties: energies (U0, U, H, and G), HOMO, LUMO, and HOMO−LUMO gap. Therefore, we re-evaluated the proposed model in the unit of “eV” on the QM9 data set.
Corrected prediction errors in the unit of eV are shown in Table 1. Note that we used Hartree instead of eV in our original Letter; however, our new results are “not 27 times” worse because of the careful tuning of our model hyperparameters. As shown in Table 1, while the predictions of energies (e.g., U0) are not accurate, the predictions of HOMO, LUMO, and HOMO−LUMO gap are more accurate than or equivalent to the reported results in our original Letter. Furthermore, we re-evaluated the extrapolation for HOMO and LUMO in the unit of eV. In the evaluation setting, we trained a model with small molecules (N ≤ 15) and tested it with large molecules (N > 15). We first show the learning curves in Figure 1. To describe the learning curves, we use the following hyperparameters: vector dimensionality is 50; number of layers is 6; batch size is
Table 1. Prediction Errors (eV) on the Test Data Set property
unit
original
new
U0 U H G ϵHOMO ϵLUMO Δϵ
eV eV eV eV eV eV eV
0.005 0.005 0.005 0.005 0.138 0.069 0.091
0.049 0.053 0.053 0.051 0.088 0.076 0.102
Figure 1. Learning curves of HOMO and LUMO and the performances of extrapolation on HOMO and LUMO. The unit is eV.
© 2018 American Chemical Society
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DOI: 10.1021/acs.jpclett.9b00301 J. Phys. Chem. Lett. 2019, 10, 2066−2067
The Journal of Physical Chemistry Letters
Addition/Correction
4; initial learning rate is 1 × 10−3; decay of learning rate is 0.95; decay interval is 10; number of iteration epochs is 1000. The test (i.e., extrapolation) results are shown in Figure 1. We found that the extrapolation can work well, i.e., the model can achieve reasonable performance on the slightly larger molecules (e.g., N = 22). However, the models generally show poor performance on large molecules for all property predictions. We believe that the extrapolation is an important challenge in materials informatics. We leave this to future work. We uploaded the source code of the implemented software on the following GitHub page: https://github.com/ masashitsubaki/QuantumGNN_molecules. This software is easy-to-use; the only requirement is PyTorch. Preprocessing a data set and learning a model on the QM9 data set can be done by only two commands (see “Usage” on the GitHub page).
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REFERENCES
(1) Tsubaki, M.; Mizoguchi, T. J. Phys. Chem. Lett. 2018, 9, 5733− 5741.
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DOI: 10.1021/acs.jpclett.9b00301 J. Phys. Chem. Lett. 2019, 10, 2066−2067
