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.gitignore

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 *.pyc
 .coverage
 *.egg-info/
+.venv/

LICENSE

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+MIT License
+
+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

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-# 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.
+# BayBE One more Time - Exploring Corrosion Inhibitors for Materials Design 
+
+[![](https://github.com/AC-BO-Hackathon/project-surface-science-syndicate/blob/main/img/project-cover.png)](https://youtu.be/kIRxGdwmLSY?si=BVYsl7kGDRsUhewH)
+
+This project focuses on exploring the capabilities of Bayesian optimization, specifically employing BayBE, in the discovery of novel corrosion inhibitors for materials design. Initially, we work with a randomly chosen subset from a comprehensive database of electrochemical responses of small organic molecules for aluminum alloys. Our goal is to assess how Bayesian optimization can speed up the screening process across the design space to identify promising compounds. We compare different strategies for incorporating chemical information, while optimizing the experimental parameters with respect to the inhibitive performance of the screened compounds. Finally, we explore the potential of transfer learning to accelerate corrosion inhibitor discovery for other base materials as well.
+
+
+## References
+- Galvão, T.L.P., Ferreira, I., Kuznetsova, A. _et al._ [CORDATA: an open data management web application to select corrosion inhibitors](https://doi.org/10.1038/s41529-022-00259-9). _npj Mater Degrad_ **6**, 48 (2022).
+- Özkan, C., Sahlmann, L., Feiler, C. _et al._ [Laying the experimental foundation for corrosion inhibitor discovery through machine learning](https://doi.org/10.1038/s41529-024-00435-z). _npj Mater Degrad_ **8**, 21 (2024).
+- Würger, T., Mei, D., Vaghefinazari, B. _et al._ [Exploring structure-property relationships in magnesium dissolution modulators](https://doi.org/10.1038/s41529-020-00148-z). _npj Mater Degrad_ **5**, 2 (2021).