## New publication - Evaluation of the Degree of Rate Control via Automatic Differentiation

| categories: | tags:

Determining which steps in a chemical reaction network are important in controlling the reaction rate is challenging. The degree of rate control is a valuable tool for this, but it requires the derivatives of the reaction rate with respect to rate parameters. In many scenarios we do not have an analytical expression for the reaction rate, and even when we do the derivatives may be tedious to derive and implement. In this work, we show how to use automatic differentiation to address this difficulty, enabling straightforward evaluation of the degree of rate control and sensitivity analysis of complex reaction networks.

@article{yang-2022-evaluat,
author =       {Yang, Yilin and Achar, Siddarth K. and Kitchin, John R.},
title =        {Evaluation of the degree of rate control via automatic
differentiation},
journal =      {AIChE Journal},
volume =       {n/a},
number =       {n/a},
pages =        {e17653},
year =         2022,
keywords =     {catalysis, reaction kinetics},
doi =          {10.1002/aic.17653},
url =          {https://aiche.onlinelibrary.wiley.com/doi/abs/10.1002/aic.17653},
eprint =       {https://aiche.onlinelibrary.wiley.com/doi/pdf/10.1002/aic.17653},
abstract =     {Abstract The degree of rate control (DRC) quantitatively
identifies the kinetically relevant (sometimes known as
rate-limiting) steps of a complex reaction network. This
concept relies on derivatives which are commonly implemented
numerically, for example, with finite differences (FDs).
Numerical derivatives are tedious to implement, and can be
problematic, and unstable or unreliable. In this study, we
demonstrate the use of automatic differentiation (AD) in the
evaluation of the DRC. AD libraries are increasingly available
through modern machine learning frameworks. Compared with the
FDs, AD provides solutions with higher accuracy with lower
computational cost. We demonstrate applications in
steady-state and transient kinetics. Furthermore, we
illustrate a hybrid local-global sensitivity analysis method,
the distributed evaluation of local sensitivity analysis, to
assess the importance of kinetic parameters over an uncertain
space. This method also benefits from AD to obtain
high-quality results efficiently.}
}