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Data-Driven Identification of the Reaction Network in Oxidative Coupling of the Methane Reaction via Experimental Data
journal contribution
posted on 2020-01-17, 14:07 authored by Itsuki Miyazato, Shun Nishimura, Lauren Takahashi, Junya Ohyama, Keisuke TakahashiIdentifying
details of chemical reactions is a challenging matter
for both experiments and computations. Here, the reaction pathway
in oxidative coupling of methane (OCM) is investigated using a series
of experimental data and data science techniques in which data are
analyzed using a variety of visualization techniques. Data visualization,
pairwise correlation, and machine learning unveil the relationships
between experimental conditions and the selectivities of CO, CO2, C2H4, C2H6,
and H2 in the OCM reaction. More importantly, the reaction
network for the OCM reaction is constructed on the basis of the scores
provided by machine learning and experimental data. In particular,
the proposed reaction map not only contains the chemical compound
but also contains experimental conditions. Thus, data-driven identification
of chemical reactions can be achieved in principle via a series of
experimental data, leading to more efficient experimental design and
catalyst development.