Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials

Research output: ResearchPaper – Annual report year: 2018

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Neural message passing on molecular graphs is one of the most promising methods for predicting formation energy and other properties of molecules and materials. In this work we extend the neural message passing model with an edge update network which allows the information exchanged between atoms to depend on the hidden state of the receiving atom. We benchmark the proposed model on three publicly available datasets (QM9, The Materials Project and OQMD) and show that the proposed model yields superior prediction of formation energies and other properties on all three datasets in comparison with the best published results. Furthermore we investigate different methods for constructing the graph used to represent crystalline structures and we find that using a graph based on K-nearest neighbors achieves better prediction accuracy than using maximum distance cutoff or the Voronoi tessellation graph.
Original languageEnglish
Publication date2018
Number of pages10
StateAccepted/In press - 2018
Event32nd Conference on Neural Information Processing Systems - Montreal, Canada
Duration: 2 Dec 20188 Dec 2018

Conference

Conference32nd Conference on Neural Information Processing Systems
CountryCanada
CityMontreal
Period02/12/201808/12/2018
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