文献类型：期刊文献

英文题名：SSIA: A sensitivity-supervised interlock algorithm for high-performance microkinetic solving

作者：Chen, Jianfu[1,2];Jia, Menglei[1,2];Lai, Zhuangzhuang[1,2];Hu, Peijun[1,2,3];Wang, Haifeng[1,2]

机构：[1]East China Univ Sci & Technol, Ctr Computat Chem, Key Lab Adv Mat, 130 Meilong Rd, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Res Inst Ind Catalysis, 130 Meilong Rd, Shanghai 200237, Peoples R China;[3]Queens Univ Belfast, Sch Chem & Chem Engn, Belfast BT9 5AG, Antrim, North Ireland

年份：2021

卷号：154

期号：2

外文期刊名：JOURNAL OF CHEMICAL PHYSICS

收录：;EI(收录号：20210409807710);WOS:【SCI-EXPANDED(收录号:WOS:000609827500002)】；

基金：This project was supported by the National Key R&D Program of China (Grant No. 2018YFA0208602), the NSFC (Grant Nos. 21873028, 91945302, and 21703067), the National Ten Thousand Talent Program for Young Top-notch Talents in China, Shanghai ShuGuang project (Grant No. 17SG30), and the Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：Rate constants - Newton-Raphson method

摘要：Microkinetic modeling has drawn increasing attention for quantitatively analyzing catalytic networks in recent decades, in which the speed and stability of the solver play a crucial role. However, for the multi-step complex systems with a wide variation of rate constants, the often encountered stiff problem leads to the low success rate and high computational cost in the numerical solution. Here, we report a new efficient sensitivity-supervised interlock algorithm (SSIA), which enables us to solve the steady state of heterogeneous catalytic systems in the microkinetic modeling with a 100% success rate. In SSIA, we introduce the coverage sensitivity of surface intermediates to monitor the low-precision time-integration of ordinary differential equations, through which a quasi-steady-state is located. Further optimized by the high-precision damped Newton's method, this quasi-steady-state can converge with a low computational cost. Besides, to simulate the large differences (usually by orders of magnitude) among the practical coverages of different intermediates, we propose the initial coverages in SSIA to be generated in exponential space, which allows a larger and more realistic search scope. On examining three representative catalytic models, we demonstrate that SSIA is superior in both speed and robustness compared with its traditional counterparts. This efficient algorithm can be promisingly applied in existing microkinetic solvers to achieve large-scale modeling of stiff catalytic networks.