%0 DATA
%A Jian-Fu, Chen
%A Yu, Mao
%A Hai-Feng, Wang
%A P., Hu
%D 2016
%T Reversibility Iteration Method for Understanding Reaction
Networks and for Solving Microkinetics in Heterogeneous Catalysis
%U https://acs.figshare.com/articles/Reversibility_Iteration_Method_for_Understanding_Reaction_Networks_and_for_Solving_Microkinetics_in_Heterogeneous_Catalysis/3846552
%R 10.1021/acscatal.6b02405.s001
%2 https://acs.figshare.com/ndownloader/files/6014181
%K iteration steps
%K reversibility iteration method
%K microkinetic solvers
%K Reaction Networks
%K Reversibility Iteration Method
%K understanding catalysis
%K Heterogeneous Catalysis
%K arbitrary-precision arithmetic
%K reaction networks
%K macroscopic reaction rates
%K Newton iteration method
%K reaction rate
%K RIM
%K NIM
%X Solving microkinetics of catalytic
systems, which bridges microscopic
processes and macroscopic reaction rates, is currently vital for understanding
catalysis *in silico*. However, traditional microkinetic
solvers possess several drawbacks that make the process slow and unreliable
for complicated catalytic systems. In this paper, a new approach,
the so-called reversibility iteration method (RIM), is developed to
solve microkinetics for catalytic systems. Using the chemical potential
notation we previously proposed to simplify the kinetic framework,
the catalytic systems can be analytically illustrated to be logically
equivalent to the electric circuit, and the reaction rate and coverage
can be calculated by updating the values of reversibilities. Compared
to the traditional modified Newton iteration method (NIM), our method
is not sensitive to the initial guess of the solution and typically
requires fewer iteration steps. Moreover, the method does not require
arbitrary-precision arithmetic and has a higher probability of successfully
solving the system. These features make it ∼1000 times faster
than the modified Newton iteration method for the systems we tested.
Moreover, the derived concept and the mathematical framework presented
in this work may provide new insight into catalytic reaction networks.