# Autonomous Discovery of Unknown Reaction Pathways from Data by Chemical Reaction Neural Network

arXiv: https://arxiv.org/abs/2002.09062 · HTML: https://mlsanity.com/paper/2002.09062/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2020.

## Built on by (most cited first, 27 of 27)

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- [Autonomous Kinetic Modeling of Biomass Pyrolysis using Chemical Reaction Neural Networks](https://mlsanity.com/paper/2105.11397/) (2021)
- [Bayesian polynomial neural networks and polynomial neural ordinary differential equations](https://mlsanity.com/paper/2308.10892/) (2023)
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- [SPIN-ODE: Stiff Physics-Informed Neural ODE for Chemical Reaction Rate Estimation](https://mlsanity.com/paper/2505.05625/) (2025)
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- [Maximum-likelihood Estimators in Physics-Informed Neural Networks for High-dimensional Inverse Problems](https://mlsanity.com/paper/2304.05991/) (2023)
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- [Using (Not-so) Large Language Models to Generate Simulation Models in a Formal DSL: A Study on Reaction Networks](https://mlsanity.com/paper/2503.01675/) (2025)
- [Kolmogorov-Arnold Chemical Reaction Neural Networks for learning pressure-dependent kinetic rate laws](https://mlsanity.com/paper/2511.07686/) (2025)
- [Data-driven discovery of multiscale chemical reactions governed by the law of mass action](https://mlsanity.com/paper/2101.06589/) (2021)
- [Constructing interpretable principal curve using Neural ODEs](https://mlsanity.com/paper/2311.09274/) (2023)
- [Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations](https://mlsanity.com/paper/2502.19397/) (2025)
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- [Learning continuous state of charge dependent thermal decomposition kinetics for Li-ion cathodes using Kolmogorov-Arnold Chemical Reaction Neural Networks (KA-CRNNs)](https://mlsanity.com/paper/2512.15628/) (2025)
- [Task Aware Modulation Using Representation Learning for Upsaling of Terrestrial Carbon Fluxes](https://mlsanity.com/paper/2603.09974/) (2026)
- [An Invariant Compiler for Neural ODEs in AI-Accelerated Scientific Simulation](https://mlsanity.com/paper/2603.23861/) (2026)
- [Generative Modeling of Discrete Latent Structures via Dynamic Policy Gradients](https://mlsanity.com/paper/2606.07400/) (2026)
- [Entropy-Constrained Machine Learning with Residual Data Augmentation for Modeling Chemical Kinetics](https://mlsanity.com/paper/2607.09582/) (2026)
- [NEMSim: Learning Control-Conditioned Multi-Event Physical Dynamics via Executable Event-Mechanism Priors](https://mlsanity.com/paper/2609.30718/) (2026)
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## Abstract (arXiv)

Chemical reactions occur in energy, environmental, biological, and many other natural systems, and the inference of the reaction networks is essential to understand and design the chemical processes in engineering and life sciences. Yet, revealing the reaction pathways for complex systems and processes is still challenging due to the lack of knowledge of the involved species and reactions. Here, we present a neural network approach that autonomously discovers reaction pathways from the time-resolved species concentration data. The proposed Chemical Reaction Neural Network (CRNN), by design, satisfies the fundamental physics laws, including the Law of Mass Action and the Arrhenius Law. Consequently, the CRNN is physically interpretable such that the reaction pathways can be interpreted, and the kinetic parameters can be quantified simultaneously from the weights of the neural network. The inference of the chemical pathways is accomplished by training the CRNN with species concentration data via stochastic gradient descent. We demonstrate the successful implementations and the robustness of the approach in elucidating the chemical reaction pathways of several chemical engineering and biochemical systems. The autonomous inference by the CRNN approach precludes the need for expert knowledge in proposing candidate networks and addresses the curse of dimensionality in complex systems. The physical interpretability also makes the CRNN capable of not only fitting the data for a given system but also developing knowledge of unknown pathways that could be generalized to similar chemical systems.