Evaluation of the degree of rate control via automatic differentiation
Evaluation of the degree of rate control via automatic differentiation
复制标题
通过自动微分评估速率控制程度
DOI：
10.1002/aic.17653
复制
发表时间：
2022
期刊：
影响因子：
3.7
通讯作者：
Kitchin, John R.
中科院分区：
文献类型：
--
作者：
Yang, Yilin;Achar, Siddarth K.;Kitchin, John R.
速率控制度(DRC)定量地识别复杂反应网络的动力学相关(有时称为速率限制)步骤。这一概念依赖于通常以数值方式实现的导数，例如，具有有限差分(FD)。数字衍生品的实现很繁琐，而且可能会出现问题，而且不稳定或不可靠。在这项研究中，我们演示了自动区分(AD)在DRC评估中的应用。通过现代机器学习框架，广告库越来越多地可用。与FDS相比，AD以更低的计算代价提供了更高精度的解。我们展示了它在稳态和暂态动力学中的应用。此外，我们还说明了一种局部-全局混合灵敏度分析方法--局部灵敏度分析的分布式评估，以评估不确定空间上的动力学参数的重要性。该方法也得益于AD算法能够高效地获得高质量的结果。
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.