New publication - Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_wB97MV Data Set
Posted December 10, 2023 at 02:53 PM | categories: news, publication | tags:
In this work, we show that we can use large graph neural networks to predict transition metal complex energies. We developed an improved dataset at a higher level of theory, and tested models ranging from GemNet-T (best) to SchNet (worst). The model performance saturates with the size of neutral structures, and improves with increasing size of charged structures. Finally, we showed that a pre-trained model from OC20 was even better than training from scratch. This indicates a degree of transferability from heterogeneous catalyst models to homogeneous molecular catalysts.
@article{garrison-2023-apply-large, author = {Garrison, Aaron G. and Heras-Domingo, Javier and Kitchin, John R. and dos Passos Gomes, Gabriel and Ulissi, Zachary W. and Blau, Samuel M.}, title = {Applying Large Graph Neural Networks To Predict Transition Metal Complex Energies Using the tmQM\_wB97MV Data Set}, journal = {Journal of Chemical Information and Modeling}, volume = 0, number = 0, pages = {null}, year = 2023, doi = {10.1021/acs.jcim.3c01226}, URL = {https://doi.org/10.1021/acs.jcim.3c01226}, eprint = {https://doi.org/10.1021/acs.jcim.3c01226}, note = {PMID: 38049389}, }
Copyright (C) 2023 by John Kitchin. See the License for information about copying.
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