# Accelerating Hierarchical ZSM‑5 Engineering via Bayesian Optimization-Guided Discovery

## TL;DR

This paper uses Bayesian optimization to speed up the creation of a special type of zeolite with a balanced pore structure.

## Contribution

The study introduces a data-driven workflow using Bayesian optimization to efficiently design hierarchical ZSM-5 structures.

## Key findings

- The optimized ZSM-5 sample achieved a hierarchy factor of 0.17 with improved microporosity.
- NaOH and TPAOH mixture synergistically created uniform mesopores while preserving crystallinity.
- Sensitivity analysis identified TPAOH fraction and temperature as key factors affecting the hierarchy factor.

## Abstract

Bayesian optimization
(BO) was used to accelerate the synthesis
of hierarchical ZSM-5 with a balanced micro–mesoporous structure.
Using mesoporosity and microporosity as dual objectives, a Gaussian
process regression surrogate guided three successive BO iterations
informed by 15 initial experiments. The optimized sample (HZ-R0.55T50t2) exhibited the highest hierarchy
factor (0.17), featuring similar mesoporosity (0.44) but higher microporosity
(0.38) than the benchmark, indicating reduced diffusional resistance
with preserved framework. Characterizations revealed that NaOH induced
framework dissolution, whereas TPAOH moderated desilication; their
mixture synergistically created uniform mesopores while maintaining
crystallinity. 27Al and 29Si NMR confirmed realumination
and silanol generation, and acidity analysis showed redistributed
acid sites. Sensitivity analysis identified the TPAOH fraction and
temperature as the dominant factors influencing the hierarchy factor.
This study establishes a data-driven workflow for zeolite design,
demonstrating that BO effectively accelerates hierarchical structure
optimization while minimizing the experimental effort.