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

by   Weiqi Ji, et al.

The inference of chemical reaction networks is an important task in understanding the chemical processes in life sciences and environment. Yet, only a few reaction systems are well-understood due to a large number of important reaction pathways involved but still unknown. Revealing unknown reaction pathways is an important task for scientific discovery that takes decades and requires lots of expert knowledge. This work presents a neural network approach for discovering unknown reaction pathways from concentration time series data. The neural network denoted as Chemical Reaction Neural Network (CRNN), is designed to be equivalent to chemical reaction networks by following the fundamental physics laws of the Law of Mass Action and Arrhenius Law. The CRNN is physically interpretable, and its weights correspond to the reaction pathways and rate constants of the chemical reaction network. Then, inferencing the reaction pathways and the rate constants are accomplished by training the equivalent CRNN via stochastic gradient descent. The approach precludes the need for expert knowledge in proposing candidate reactions, such that the inference is autonomous and applicable to new systems for which there is no existing empirical knowledge to propose reaction pathways. The physical interpretability also makes the CRNN not only capable of fitting the data for a given system but also developing knowledge of unknown pathways that could be generalized to similar chemical systems. Finally, the approach is applied to several chemical systems in chemical engineering and biochemistry to demonstrate its robustness and generality.



There are no comments yet.


page 9

page 10

page 12


Autonomous Kinetic Modeling of Biomass Pyrolysis using Chemical Reaction Neural Networks

Modeling the burning processes of biomass such as wood, grass, and crops...

Reverse Engineering Chemical Reaction Networks from Time Series Data

The automated inference of physically interpretable (bio)chemical reacti...

Data-driven discovery of multiscale chemical reactions governed by the law of mass action

In this paper, we propose a data-driven method to discover multiscale ch...

Rxn Hypergraph: a Hypergraph Attention Model for Chemical Reaction Representation

It is fundamental for science and technology to be able to predict chemi...

Autonomous discovery in the chemical sciences part II: Outlook

This two-part review examines how automation has contributed to differen...

Dataset Bias in the Natural Sciences: A Case Study in Chemical Reaction Prediction and Synthesis Design

Datasets in the Natural Sciences are often curated with the goal of aidi...

Automated Deep Abstractions for Stochastic Chemical Reaction Networks

Predicting stochastic cellular dynamics as emerging from the mechanistic...

Code Repositories


Chemical Reaction Neural Network

view repo


Alpha preview of the code for the paper

view repo
This week in AI
