Dissolution
of polysulfide intermediates into electrolytes has
been a major bottleneck in the development of the Al–S battery.
MXenes can be promising anchoring materials, even though finding the
most suitable candidates from a vast search space in a short span
of time is challenging. Herein, a combined density functional theory
and machine learning (ML) approach has been implemented to predict
suitable M1M2XT2-type MXene materials that can optimally
anchor the polysulfide intermediates. By employing various ML algorithms,
the trained XGBR model is found to exhibit remarkable precision in
predicting the anchoring effect of MXenes. 42 promising candidates
have been identified to show optimum anchoring across the Al–S
intermediates. The F and O terminal groups are found to majorly exhibit
the optimum anchoring effect toward the possible polysulfide intermediates.
This work provides crucial insights into the development of next-generation
Al–S batteries accelerated by the ML approach, contributing
to the advancement of energy storage technologies.