sustainable-processes/summit
Optimising chemical reactions using machine learning
This helps chemical engineers and process chemists quickly find the best conditions for chemical reactions, especially in the fine chemicals industry. It takes information about your reaction's variables (like temperature or stoichiometry) and objectives (like desired yield or purity) and uses machine learning to suggest optimal conditions, leading to better outcomes in fewer experiments. This is for professionals who optimize chemical processes and want to move beyond intuition or traditional Design of Experiments.
141 stars. No commits in the last 6 months. Available on PyPI.
Use this if you need to rapidly optimize chemical reaction parameters to maximize specific outcomes like yield or purity, especially for complex reactions where traditional methods are too slow.
Not ideal if you are working with very simple reactions that can be optimized efficiently with a few manual tests or standard Design of Experiments.
Stars
141
Forks
31
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Sep 03, 2024
Commits (30d)
0
Dependencies
18
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