Just as circuit theory breaks complex electrical circuits into simpler components, a new framework does the same for chemical reaction networks, providing tools to simplify analysis of molecule and energy flow.

We consider the dynamics of chemical reaction networks under the assumption of mass-action kinetics. We show that there exist reaction networks R for which the reaction rate constants are not uniquely identifiable, even if we are given complete information on the dynamics of concentrations for all chemical species of R. Also, we show that there exist reaction networks R1 6= R2 such that their d...

Chemical Reactions networks are widely studied in a variety of fields, including Systems Biology, Medicine, and Chemical Engineering. Analyzing chemical reaction networks is a widely studied problem going back to Guldberg and Waage in the 19th century. Despite being well-studied, many chemical reaction networks still aren’t very well understood, partly due to the complexity of naturally occurin...

This paper proposed a chemical substance detection method using the Long Short-Term Memory of Recurrent Neural Networks (LSTM-RNN). The chemical substance data was collected using a mass spectrometer which is a time-series data. The classification accuracy using the LSTM-RNN classifier is 96.84%, which is higher than 75.07% of the ordinary feed forward neural networks. The experimental results ...

Recently Tao and Lan [Phys. Rev. E. 72, 041508 (2005)] experimentally reported that the rotation of a dielectric particle can reduce significantly the attracting interparticle force between the rotating dielectric particle and a stationary one in argon gas. We develop the Gu-Yu-Hui theory of relaxation [J. Chem. Phys. 116, 24 (2002)] to account for the Tao-Lan observations. Excellent agreement ...