# Graph Dynamical Networks for Unsupervised Learning of Atomic Scale Dynamics in Materials

arXiv: https://arxiv.org/abs/1902.06836 · HTML: https://mlsanity.com/paper/1902.06836/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2019.

## Built on by (most cited first, 13 of 13)

- [GraphVAMPNet, using graph neural networks and variational approach to markov processes for dynamical modeling of biomolecules](https://mlsanity.com/paper/2201.04609/) (2022)
- [Materials Representation and Transfer Learning for Multi-Property Prediction](https://mlsanity.com/paper/2106.02225/) (2021)
- [HiLAB: A Hybrid Inverse-Design Framework](https://mlsanity.com/paper/2505.17491/) (2025)
- [CKNet: A Convolutional Neural Network Based on Koopman Operator for Modeling Latent Dynamics from Pixels](https://mlsanity.com/paper/2102.10205/) (2021)
- [Using pretrained graph neural networks with token mixers as geometric featurizers for conformational dynamics](https://mlsanity.com/paper/2409.19838/) (2024)
- [Few-Sample Traffic Prediction with Graph Networks using Locale as Relational Inductive Biases](https://mlsanity.com/paper/2203.03965/) (2022)
- [Self-Supervised Decomposition, Disentanglement and Prediction of Video Sequences while Interpreting Dynamics: A Koopman Perspective](https://mlsanity.com/paper/2110.00547/) (2021)
- [Internal-Coordinate Density Modelling of Protein Structure: Covariance Matters](https://mlsanity.com/paper/2302.13711/) (2023)
- [Hierarchical geometric deep learning enables scalable analysis of molecular dynamics](https://mlsanity.com/paper/2512.06520/) (2025)
- [From Natural Language to Materials Discovery:The Materials Knowledge Navigation Agent](https://mlsanity.com/paper/2602.11123/) (2026)
- [Prediction of transport property via machine learning molecular movements](https://mlsanity.com/paper/2203.03103/) (2022)
- [Dictionary-based Manifold Learning](https://mlsanity.com/paper/2302.00263/) (2023)
- [Elucidating Interfacial Dynamics of Ti-Al Systems Using Molecular Dynamics Simulation and Markov State Modeling](https://mlsanity.com/paper/2306.14568/) (2023)

## Abstract (arXiv)

Understanding the dynamical processes that govern the performance of functional materials is essential for the design of next generation materials to tackle global energy and environmental challenges. Many of these processes involve the dynamics of individual atoms or small molecules in condensed phases, e.g. lithium ions in electrolytes, water molecules in membranes, molten atoms at interfaces, etc., which are difficult to understand due to the complexity of local environments. In this work, we develop graph dynamical networks, an unsupervised learning approach for understanding atomic scale dynamics in arbitrary phases and environments from molecular dynamics simulations. We show that important dynamical information can be learned for various multi-component amorphous material systems, which is difficult to obtain otherwise. With the large amounts of molecular dynamics data generated everyday in nearly every aspect of materials design, this approach provides a broadly useful, automated tool to understand atomic scale dynamics in material systems.