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.
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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
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