A Benchmark Framework for the Fast and Reliable Evaluation of Zeolite Surface Stability

Accurately identifying the external surface structure
and crystal
morphology of zeolites is essential for establishing structure–performance
relationships. Surface free energy serves as a quantitative descriptor
of surface stability, which is a prerequisite for theoretical investigations
of surface properties. However, a standard computational protocol
that effectively balances accuracy and efficiency has yet to be established.
To address this, we propose an efficient computational workflow developed
through systematic benchmarking. This workflow integrates two key
components: an analytical model developed using the SISSO machine
learning approach to replace expensive frequency calculations and
a configuration screening process based on machine learning potentials
to account for the vast number of possible surface terminations. Using
this workflow, we efficiently calculate the surface free energies
of MFI and FAU zeolites and explore their morphological evolution
under dynamic conditions. This study establishes a general and efficient
theoretical framework for investigating the thermodynamic properties
of zeolite surfaces.