Bayesian Optimisation for Zeolite Synthesis

Overview

Scope

This repository was initiated as an entry for the Bayesian Optimization Hackathon for Chemistry and Materials, held on March 27-28, 2024, and sponsored by the Acceleration Consortium and Merck KGaA. Here, we propose Bayesian optimization within the field of zeolite synthesis. This concept is also explained in a short video.

Despite their significant industrial applications as catalysts, ion exchangers and adsorbents, the synthesis of zeolites predominantly relies on heuristics, experience and a sprinkle of magic. Employing Bayesian optimization has the potential to swiftly navigate the extensive parameter space in zeolite synthesis research and reduce associated costs.

Introductory text on zeolites, zeolite synthesis and Bayesian optimization

In zeolite_synthesis_bo_introduction.md we provide an overview of the following topics:

1. Zeolites: definition, properties and relevance in industry
2. Zeolite Synthesis: typical procedures, ingredients and equipments
3. Zeolite Synthesis Optimization: the limited literature on active learning for zeolite synthesis is discussed, and considerations regarding BO:
   3.1 Parameter space of a zeolite synthesis
   3.2 Constraints of a zeolite synthesis
   3.3 Objectives that might be pursued in zeolite synthesis
   3.4 Zeolite synthesis datasets that are available in literature
4. Discussion: various aspects of BO in zeolite synthesis are discussed, and how they can be tackled
5. References

While numerous references are provided for further exploration, this document is self-contained and aims to be easily understood. We hope it inspires the reader to consider active learning approaches in their zeolite synthesis endeavors. This introductory text is also provided as pdf.

Notebook tutorial on Bayesian optimization for zeolite synthesis (with Ax)

Within the demo_zeolite_synthesis_bo.ipynb notebook, we illustrate the concepts of the introductory text with code, leveraging real-world literature data acquired through grid search in Table S4 and Table S3 in the Supporting Information of respectively Chem. Mater. 2020, 32, 273–285 and J. Am. Chem. Soc. 2021, 143, 16243–16255.

This notebook is divided into two sections:

1. The first section uses Bayesian optimization to optimize an analytical dummy function using zeolite synthesis parameters, showing a significant improvement compared to grid search.
2. The second section uses Bayesian optimization to propose a new experiment based on existing literature data.

A common overarching objective in zeolite synthesis is to achieve a high yield of the desired zeolite product. In the papers under consideration, a more specialized goal involves maximizing the presence of proximal Al pairs within synthesized CHA zeolites, which is required for stabilizing Fe²⁺ sites (so-called divalent cation capacity, DCC). Upon activation, these sites can selectively oxidize methane to methanol. Accordingly, we will provide examples with synthesis yield, DCC and methanol yield as optimization objectives for the Bayesian optimization process.

The various examples touch upon different aspects of Bayesian optimization, including continuous variables, categorical variables, mixed variable types, parameter constraints, as well as single and multiple objectives.