Papers › Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials
Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials
Peter Bjørn Jørgensen, Karsten Wedel Jacobsen, Mikkel N. Schmidt
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.
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Code
Syntology Ran 4 of 22 code samples harvested from 3 repositories linked to this paper; 18 have no recorded run. Of those that ran: 2 ran · honoured contract; 1 ran · violated contract; 1 ran · our draft was wrong.
By repository: community (archive-listed): 18 samples from 3 repositories, 0 ran; 4 identical to code first harvested elsewhere. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
22 samples harvested; 4 ran; 2 honoured the contract we drafted; 18 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Formation Energy | Materials Project | SchNet-edge-update | MAE | 22.7 | #4 of 9 | Archive leaderboard | report |
| Formation Energy | Materials Project | SchNet | MAE | 31.8 | #6 of 9 | Archive leaderboard | report |
| Formation Energy | QM9 | SchNet-edge-update | MAE | 0.242 | #12 of 18 | Archive leaderboard | report |
| Formation Energy | QM9 | SchNet | MAE | 0.314 | #16 of 18 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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