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

arXiv: https://arxiv.org/abs/1806.03146 · HTML: https://mlsanity.com/paper/1806.03146/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2018.

## Built on by (most cited first, 21 of 21)

- [Graph neural networks for materials science and chemistry](https://mlsanity.com/paper/2208.09481/) (2022)
- [Protein Representation Learning by Geometric Structure Pretraining](https://mlsanity.com/paper/2203.06125/) (2022)
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- [Rotation Invariant Graph Neural Networks using Spin Convolutions](https://mlsanity.com/paper/2106.09575/) (2021)
- [Spherical Channels for Modeling Atomic Interactions](https://mlsanity.com/paper/2206.14331/) (2022)
- [Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations](https://mlsanity.com/paper/2203.09697/) (2022)
- [DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing](https://mlsanity.com/paper/2306.01794/) (2023)
- [Calibrated Uncertainty for Molecular Property Prediction using Ensembles of Message Passing Neural Networks](https://mlsanity.com/paper/2107.06068/) (2021)
- [Equivariant Message Passing Neural Network for Crystal Material Discovery](https://mlsanity.com/paper/2302.00485/) (2023)
- [Vector Field Oriented Diffusion Model for Crystal Material Generation](https://mlsanity.com/paper/2401.05402/) (2023)
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- [Graph Neural Network-State Predictive Information Bottleneck (GNN-SPIB) approach for learning molecular thermodynamics and kinetics](https://mlsanity.com/paper/2409.11843/) (2024)
- [Unified Model for Crystalline Material Generation](https://mlsanity.com/paper/2306.04510/) (2023)
- [Artificial Intelligence based Autonomous Molecular Design for Medical Therapeutic: A Perspective](https://mlsanity.com/paper/2102.06045/) (2021)
- [Band-gap regression with architecture-optimized message-passing neural networks](https://mlsanity.com/paper/2309.06348/) (2023)
- [Enhanced sampling of Crystal Nucleation with Graph Representation Learnt Variables](https://mlsanity.com/paper/2310.07927/) (2023)
- [Training speedups via batching for geometric learning: an analysis of static and dynamic algorithms](https://mlsanity.com/paper/2502.00944/) (2025)
- [Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineering](https://mlsanity.com/paper/2209.11234/) (2022)
- [On the Interplay of Subset Selection and Informed Graph Neural Networks](https://mlsanity.com/paper/2306.10066/) (2023)
- [Substitutional Alloying Using Crystal Graph Neural Networks](https://mlsanity.com/paper/2306.10766/) (2023)
- [Optimized Crystallographic Graph Generation for Material Science](https://mlsanity.com/paper/2307.05380/) (2023)

## Abstract (arXiv)

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.