New publication - Evaluation of the Degree of Rate Control via Automatic Differentiation
Posted March 07, 2022 at 08:40 AM | categories: news, publication | 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.} }
Copyright (C) 2022 by John Kitchin. See the License for information about copying.
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