# Recurrent Neural Network-based Model for Accelerated Trajectory Analysis in AIMD Simulations

arXiv: https://arxiv.org/abs/1909.10124 · HTML: https://mlsanity.com/paper/1909.10124/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2019.

## Built on by (most cited first, 4 of 4)

- [A Note on Learning Rare Events in Molecular Dynamics using LSTM and Transformer](https://mlsanity.com/paper/2107.06573/) (2021)
- [Accelerating Long-Term Molecular Dynamics with Physics-Informed Time-Series Forecasting](https://mlsanity.com/paper/2510.01206/) (2025)
- [Molecular Dynamics of Polymer-lipids in Solution from Supervised Machine Learning](https://mlsanity.com/paper/2203.00151/) (2022)
- [Long Short-Term Memory to predict 3D Amino acids Positions in GPCR Molecular Dynamics](https://mlsanity.com/paper/2207.05682/) (2022)

## Abstract (arXiv)

The presented work demonstrates the training of recurrent neural networks (RNNs) from distributions of atom coordinates in solid state structures that were obtained using ab initio molecular dynamics (AIMD) simulations. AIMD simulations on solid state structures are treated as a multi-variate time-series problem. By referring interactions between atoms over the simulation time to temporary correlations among them, RNNs find patterns in the multi-variate time-dependent data, which enable forecasting trajectory paths and potential energy profiles. Two types of RNNs, namely gated recurrent unit and long short-term memory networks, are considered. The model is described and compared against a baseline AIMD simulation on an iridium oxide slab. Findings demonstrate that both networks can potentially be harnessed for accelerated statistical sampling in computational materials research.