Abstract
Although hydrogen is an ideal energy carrier, storing and transporting it in gas or liquid form is unsafe and inefficient. Liquid Organic Hydrogen Carriers (LOHC) are promising compounds that can efficiently accommodate hydrogen. However, choosing the optimal LOHC from millions of candidates is difficult because calculating dehydrogenation enthalpy, a key criterion, is computationally expensive. To address this, we propose a new graph neural network-based method called Aggregate With Self-node representation (AWS) that efficiently and accurately predicts dehydrogenation enthalpy. We improve existing graph neural networks and address cases where expressiveness is limited. We also present an ensemble scheme for weighting prediction results. Our experiments on real-world LOHC screening and benchmark datasets demonstrate the superiority of our method in chemical property predictions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Adametz, P., Müller, K., Arlt, W.: Energetic evaluation of hydrogen storage in metal hydrides. Int. J. Energy Res. 40(13), 1820–1831 (2016)
Baek, J., Kang, M., Hwang, S.J.: Accurate learning of graph representations with graph multiset pooling. arXiv preprint arXiv:2102.11533 (2021)
Bui, A.T., et al.: Improving ensemble robustness by collaboratively promoting and demoting adversarial robustness. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 6831–6839 (2021)
Cao, D.S., et al.: In silico toxicity prediction by support vector machine and smiles representation-based string kernel. SAR QSAR Environ. Res. 23(1–2), 141–153 (2012)
Delaney, J.S.: ESOL: estimating aqueous solubility directly from molecular structure. J. Chem. Inf. Comput. Sci. 44(3), 1000–1005 (2004)
Gasteiger, J., Becker, F., Günnemann, S.: GemNet: universal directional graph neural networks for molecules. In: Advances in Neural Information Processing Systems, vol. 34, pp. 6790–6802 (2021)
Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630–645. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_38
Hendrycks, D., Gimpel, K.: Gaussian error linear units (GELUs). arXiv preprint arXiv:1606.08415 (2016)
Hu, W., et al.: Strategies for pre-training graph neural networks. arXiv preprint arXiv:1905.12265 (2019)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Landrum, G.: RDKit documentation. Release 1(1–79), 4 (2013)
Lin, M., Chen, Q., Yan, S.: Network in network. arXiv preprint arXiv:1312.4400 (2013)
Mansimov, E., Mahmood, O., Kang, S., Cho, K.: Molecular geometry prediction using a deep generative graph neural network. Sci. Rep. 9(1), 1–13 (2019)
Mansouri, K., Grulke, C.M., Judson, R.S., Williams, A.J.: Opera models for predicting physicochemical properties and environmental fate endpoints. J. Cheminform. 10(1), 1–19 (2018)
Mansouri, K., Judson, R.S.: In silico study of in vitro GPCR assays by QSAR modeling. In: Benfenati, E. (ed.) In Silico Methods for Predicting Drug Toxicity. MMB, vol. 1425, pp. 361–381. Springer, New York (2016). https://doi.org/10.1007/978-1-4939-3609-0_16
Mendez, D., et al.: ChEMBL: towards direct deposition of bioassay data. Nucleic Acids Res. 47(D1), D930–D940 (2019)
Mentel, L.: Mendeleev documentation (2022)
Mobley, D.L., Guthrie, J.P.: FreeSolv: a database of experimental and calculated hydration free energies, with input files. J. Comput. Aided Mol. Des. 28(7), 711–720 (2014)
Niermann, M., Beckendorff, A., Kaltschmitt, M., Bonhoff, K.: Liquid organic hydrogen carrier (LOHC)-assessment based on chemical and economic properties. Int. J. Hydrogen Energy 44(13), 6631–6654 (2019)
Paragian, K., Li, B., Massino, M., Rangarajan, S.: A computational workflow to discover novel liquid organic hydrogen carriers and their dehydrogenation routes. Mol. Syst. Des. Eng. 5(10), 1658–1670 (2020)
Parr, R.G.: Density functional theory. Annu. Rev. Phys. Chem. 34(1), 631–656 (1983)
Simm, G.N., Hernández-Lobato, J.M.: A generative model for molecular distance geometry. arXiv preprint arXiv:1909.11459 (2019)
Sun, R.: Does GNN pretraining help molecular representation? arXiv preprint arXiv:2207.06010 (2022)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)
Weisfeiler, B., Leman, A.: The reduction of a graph to canonical form and the algebra which appears therein. NTI Ser. 2(9), 12–16 (1968)
Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018)
Xu, K., Li, C., Tian, Y., Sonobe, T., Kawarabayashi, K., Jegelka, S.: Representation learning on graphs with jumping knowledge networks. In: International Conference on Machine Learning, pp. 5453–5462. PMLR (2018)
Xu, M., Luo, S., Bengio, Y., Peng, J., Tang, J.: Learning neural generative dynamics for molecular conformation generation. arXiv preprint arXiv:2102.10240 (2021)
You, J., Ying, Z., Leskovec, J.: Design space for graph neural networks. In: Advances in Neural Information Processing Systems, vol. 33, pp. 17009–17021 (2020)
Zügner, D., Akbarnejad, A., Günnemann, S.: Adversarial attacks on neural networks for graph data. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2847–2856 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Choi, G., Yook, H., Han, J.W., Hong, C. (2023). AWS: GNNs that Aggregate with Self-node Representation for Dehydrogenation Enthalpy Prediction. In: Nguyen, N.T., et al. Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2023. Communications in Computer and Information Science, vol 1863. Springer, Cham. https://doi.org/10.1007/978-3-031-42430-4_14
Download citation
DOI: https://doi.org/10.1007/978-3-031-42430-4_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-42429-8
Online ISBN: 978-3-031-42430-4
eBook Packages: Computer ScienceComputer Science (R0)