# Speeding up astrochemical reaction networks with autoencoders and neural ODEs

arXiv: https://arxiv.org/abs/2312.06015 · HTML: https://mlsanity.com/paper/2312.06015/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 10 Dec 2023.

## Builds on (most cited first, 5 of 5)

- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [On Neural Differential Equations](https://mlsanity.com/paper/2202.02435/) (2022)
- [Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations](https://mlsanity.com/paper/1710.10121/) (2017)
- [Operator Learning with Neural Fields: Tackling PDEs on General Geometries](https://mlsanity.com/paper/2306.07266/) (2023)
- [Neural networks: solving the chemistry of the interstellar medium](https://mlsanity.com/paper/2211.15688/) (2022)

## Built on by (most cited first, 6 of 6)

- [SPIN-ODE: Stiff Physics-Informed Neural ODE for Chemical Reaction Rate Estimation](https://mlsanity.com/paper/2505.05625/) (2025)
- [CODES: Benchmarking Coupled ODE Surrogates](https://mlsanity.com/paper/2410.20886/) (2024)
- [3D-PDR Orion dataset and NeuralPDR: Neural Differential Equations for Photodissociation Regions](https://mlsanity.com/paper/2412.00758/) (2024)
- [NeuralPDR: Neural Differential Equations as surrogate models for Photodissociation Regions](https://mlsanity.com/paper/2506.14270/) (2025)
- [LiLaN: A Linear Latent Network as the Solution Operator for Real-Time Solutions to Stiff Nonlinear Ordinary Differential Equations](https://mlsanity.com/paper/2501.08423/) (2025)
- [MCMC Informed Neural Emulators for Uncertainty Quantification in Dynamical Systems](https://mlsanity.com/paper/2603.10987/) (2026)

## Abstract (arXiv)

In astrophysics, solving complex chemical reaction networks is essential but computationally demanding due to the high dimensionality and stiffness of the ODE systems. Traditional approaches for reducing computational load are often specialized to specific chemical networks and require expert knowledge. This paper introduces a machine learning-based solution employing autoencoders for dimensionality reduction and a latent space neural ODE solver to accelerate astrochemical reaction network computations. Additionally, we propose a cost-effective latent space linear function solver as an alternative to neural ODEs. These methods are assessed on a dataset comprising 29 chemical species and 224 reactions. Our findings demonstrate that the neural ODE achieves a 55x speedup over the baseline model while maintaining significantly higher accuracy by up to two orders of magnitude reduction in relative error. Furthermore, the linear latent model enhances accuracy and achieves a speedup of up to 4000x compared to standard methods.