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Bayesian optimization is a key technique in chemistry and materials science, particularly useful for tackling complex experiments that are time-consuming and costly. It uses a smart model, often based on Gaussian Processes, to predict where to look next in an experiment to find the best results with minimal trials. This method is especially handy when dealing with experiments that don't have straightforward outcomes or involve a lot of variables, which is often the case in these fields. It's like having a smart assistant that tells you the most promising experiments to run next, helping you save time and resources. Moreover, Bayesian optimization is good at dealing with uncertain results through its smart handling of the exploration-exploitation trade-off. This approach helps speed up the discovery of new materials and chemical processes by making the experimental process more efficient and informed.
Yes! The hackathon is open to everyone, regardless of background or experience. We encourage participants from all career stages, including students, researchers, and professionals, to join us. For code-based projects, we recommend at least beginner Python programming experience[(?)][faq]{:title="Do I need to use Python, or can I use other languages?"} and basic familiarity with git and GitHub. Prior to the hackathon, we also recommend that participants study the basic concepts behind Bayesian optimization. For those looking to learn or refresh their knowledge on these topics, please study the items provided in the resources page.
This is a BYOP ("bring-your-own-package") event 😉. We have some recommendations for newcomers, but we hope to see a diverse set of solutions applied to the benchmark tasks. Likewise, multiple teams using the same package is not a problem, since solutions can remain private during the course of the hackathon.
We recommend using Python, as it is the most common language for scientific computing and has a wide range of libraries for Bayesian optimization. However, you are welcome to use other languages if you prefer.
There is certainly a focus on Bayesian optimization, but you are welcome to use other algorithms. It would be best if there's some kind of adaptive design component to it, though this isn't a strict requirement. If you're unsure, feel free to reach out to the organizers.
Yes, in some sense. Rather than collaborating on a single problem together, you will work either solo or in small teams to accomplish one of three tasks: applying a Bayesian optimization algorithm to one of the provided benchmark tasks, designing a new optimization benchmark, or creating concept-focused instructional tutorials. Submissions will be reviewed by a team of judges[(?)][faq]{:title="What is expected from me if I act as a judge?"}, and top-ranked projects will receive prizes sponsored by Matterhorn Studio.
No, you are allowed to participate as an individual. However, we encourage you to form a team with other participants to work on a project together. This will allow you to share ideas and learn from others! It is up to you to identify and form a team, and you can do so at any time leading up to the hackathon.
We would love to have you contribute to the manuscript! Depending on the level of involvement, you will be offered the opportunity to co-author or be acknowledged in the manuscript. The following are the criteria for co-authorship, which will be evaluated at the discretion of the organizing team:
- Participation in the hackathon: You must have participated in the hackathon to be eligible to contribute to the scholarly article. This is indicated in part by commit history, pull requests, and issues on the team's GitHub repository, as well as other public evidence of participation.
- Complete project submission: You or your team must have submitted a complete project. This includes a complete implementation of a Bayesian optimization algorithm, a new benchmark task, an instructional tutorial, a real-world optimization task proposal, or a general submission based on the provided templates. Additionally, this project must be made publicly available on GitHub and licensed appropriately (e.g., MIT License).
- Completion of a CRediT form: You must complete a CRediT form to be eligible for co-authorship. Additionally, you will need to acknowledge that your submitted work belongs to you, with the appropriate references and acknowledgements made. This form will be sent to you after the hackathon.
Yes, you may participate in multiple projects. For example, one may wish to apply a particular algorithm to a benchmark and create a concept-focused instructional tutorial to accompany it. Each of these would be considered a separate project with its own template. However, we encourage participants to be realistic about the limited time available during the hackathon as well as communicate this to other team members if participating in multiple projects.
During the course of the hackathon, your repository can remain private. However, to be considered for the prizes and to contribute to the scholarly article, your repository must be made public. This helps ensure that the work is accessible to the community and can be reviewed by the judges[(?)][faq]{:title="What is expected from me if I act as a judge?"} during the judging and showcase section. You are also welcome to make your repository public at any time leading up to or during the hackathon.
If you are interested in acting as a judge, please indicate so in the corresponding question when you register. If you are selected as a judge, you are expected to attend the project showcase and judging session on {{ site.event_close_data }}. Prior to this session, you will be provided with a Gavel link which you will use to evaluate projects using the simple method of pairwise comparisons. You are welcome to spend as little or as much time as you'd like on each project, and you are not required or expected to review all projects. If you are a participant in the hackathon, we ask that you use the "skip" button in the Gavel app if your own project is sent to you for evaluation.
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