Lignin
conversion into high value-added chemicals is of great significance
for maximizing the use of renewable energy. Ionic liquids (ILs) have
been widely used for targeted cleavage of the CâO bonds of
lignin due to their high catalytic activity. Studying the cleavage
activity of each IL is impossible and time-consuming, given the huge
number of cations and anions. Currently, the mainstream approach to
determining the cleavage activity of one IL is to calculate the activation
barrier energy (E
a) theoretically via
transition state search, a process that involves the iterative determination
of an appropriate âimaginary frequencyâ. Machine learning
(ML) has been widely used for catalyst design and screening, enabling
accurate mapping from specified descriptors to target properties.
To avoid complicated E
a calculations and
to screen potential candidates, in this study, we selected nearly
103 ILs and guaiacylglycerol-β-guaiacyl ether (GG)
as the lignin model and used the ML technology to train models that
can rapidly predict the cleavage activity of ILs. Taking the easily
accessible bond dissociation energy (BDE) of the βâOâ4
bond in GG as the target, an ML model with r >
0.93
for predicting the catalytic activity of ILs was obtained. The change
tendency of the BDE is consistent with the experimental yield of guaiacol,
reflecting the reliability of the ML model. Finally, [C2MIM]Â[Tyrosine] and [C3MIM]Â[Tyrosine] as the optimal candidates
for future applications were screened out. This is a novel strategy
for predicting the catalytic activity of ILs on lignin without the
need to calculate complicated reaction pathways while reducing time
consumption. It is anticipated that the ML model can be utilized in
future practical applications for targeted cleavage of lignin.