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.github/workflows/classroom.yml

@@ -16,8 +16,11 @@ jobs:
       uses: actions/checkout@v4
     - name: Hello world test
       id: hello-world-test
-      uses: education/autograding-python-grader@v1
+      uses: education/autograding-command-grader@v1
       with:
+        test-name: Hello world test
+        setup-command: sudo -H pip3 install pytest
+        command: pytest
         timeout: 5
         max-score: 5
     - name: Autograding Reporter

LICENSE

@@ -0,0 +1,22 @@
+MIT License
+
+Copyright (c) 2024 Dieter Plessers
+Copyright (c) 2024 AC BO Hackathon
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,15 +1,41 @@
-# Autograding Example: Python
-This example project is written in Python, and tested with pytest.
-
-## The assignment
-The tests are failing right now because the method isn't outputting the correct string. Fixing this up will make the tests green.
-
-## Setup command
-
-See `postCreateCommand` from [`devcontainer.json`](.devcontainer/devcontainer.json).
-
-## Run command
-`pytest`
-
-## Notes
-- pip's install path is not included in the PATH var by default, so without installing via `sudo -H`, pytest would be unaccessible.
+# Bayesian Optimisation for Zeolite Synthesis <img src="images/BO_Zeolite.png" alt="Zeolite" width="100"/>
+
+## Overview
+<img src="images/Poster_BO_Zeolite.png" alt="Summarizing poster" width="800"/>
+
+## Scope
+This repository was initiated as an entry for the [Bayesian Optimization Hackathon for Chemistry and Materials](https://ac-bo-hackathon.github.io/), 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](https://www.youtube.com/watch?v=4lFEUixwkE8).
+
+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](./zeolite_synthesis_bo_introduction.md) on zeolites, zeolite synthesis and Bayesian optimization
+In [zeolite_synthesis_bo_introduction.md](./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](./zeolite_synthesis_bo_introduction.pdf).
+
+## [Notebook](./demo_zeolite_synthesis_bo.ipynb) tutorial on Bayesian optimization for zeolite synthesis (with [Ax](https://ax.dev/)) <a target="_blank" href="https://colab.research.google.com/github/DieterPlessers/test_md/blob/main/demo_zeolite_synthesis_bo.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
+Within the [demo_zeolite_synthesis_bo.ipynb](./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](https://pubs.acs.org/doi/suppl/10.1021/acs.chemmater.9b03738/suppl_file/cm9b03738_si_001.pdf#page=10) and [Table S3](https://pubs.acs.org/doi/suppl/10.1021/jacs.1c07590/suppl_file/ja1c07590_si_001.pdf#page=9) in the Supporting Information of respectively [*Chem. Mater.* **2020**, *32*, 273–285](https://pubs.acs.org/doi/abs/10.1021/acs.chemmater.9b03738) and [*J. Am. Chem. Soc.* **2021**, *143*, 16243–16255](https://pubs.acs.org/doi/10.1021/jacs.1c07590).  
+
+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 Fe<sup>2+</sup> 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**.
+
+
+
+
+
+