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kjappelbaum/README.md

Hi there 👋

I'm a research group leader in Germany (FSU Jena, Helmholtz Center for Polymers in Energy Applications) looking into how data can be used to expedite materials design and discovery. I've been developing tools for MOFs, but also generally applicable ML systems. For this, I'm also looking into ways to improve data capture and dissemination in experimental chemistry. For example, I've been collecting interesting datasets that we are now using to train a chemistry LLM, in the ChemNLP project, that I co-lead.

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  1. gptchem gptchem Public

    Jupyter Notebook 234 40

  2. awesome-chemistry-datasets awesome-chemistry-datasets Public

    overview of datasets for ML in chemistry

    265 28

  3. pyepal pyepal Public

    Multiobjective active learning with tunable accuracy/efficiency tradeoff and clear stopping criterion.

    Python 38 5

  4. mofdscribe mofdscribe Public

    An ecosystem for digital reticular chemistry

    Python 44 7

  5. OpenBioML/chemnlp OpenBioML/chemnlp Public

    ChemNLP project

    Python 148 46

  6. ml_molsim ml_molsim Public

    Machine learning exercises for the MolSim course (http://www.acmm.nl/molsim/molsim2023/index.html)

    Jupyter Notebook 24 15