anthony-wang/BestPractices
Things that you should (and should not) do in your Materials Informatics research.
This project provides practical guidance for materials scientists on applying machine learning to their research. It offers a structured approach for creating machine learning models, from input data like chemical composition and temperature to predictions such as heat capacity of materials. Materials scientists and researchers in computational materials science would use this to develop robust and reliable predictive models.
201 stars. No commits in the last 6 months.
Use this if you are a materials scientist looking for a clear, example-driven guide on how to correctly set up and execute a machine learning project for materials discovery or property prediction.
Not ideal if you are an experienced machine learning practitioner already familiar with best practices, or if you are not working within the field of materials science.
Stars
201
Forks
80
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Nov 17, 2023
Commits (30d)
0
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