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@sparks-baird

Sparks/Baird Materials Informatics

Sterling Baird and Taylor Sparks Materials Informatics Projects

Hi there 👋

🙋‍ Taylor Sparks: Associate Professor of Mat. Sci. & Eng. @ University of Utah and co-host of Materialism podcast

🙋‍ Sterling Baird: Ph.D. candidate (8/2020-8/2023) in the Sparks Group focusing on data-driven materials discovery

The @sparks-baird organization hosts the joint efforts between Taylor and Sterling in driving the acceleration of materials informatics where AI meets materials science. These applications range from materials property prediction and generative models to exploratory materials optimization and autonomous scientific research.

Contributing

Want to get involved? Be sure to click Follow at the top-right to stay up-to-date on the latest developments. If you're having issues using one of the repositories or want to discuss topics related to it, feel free to look through the existing issues and discussions for each repository and add new ones if your problem is different. We'll try to get back to you soon to help you with your problem. If you'd like to contribute to a repository, check out GitHub's guide to project contributions. The same principles apply to suggesting and incorporating changes into our public repositories.

We subscribe to the principle of open-sourcing from the start. This allows for early and iterative community feedback as well as letting others know what we're interested in and what we're working on. As a result, some repositories will be a work-in-progress (WIP). These are some of the best ones to collaborate on! Where appropriate, we are also exploring consolidating repositories and porting functionality into larger "hub" repositories like pymatgen.

Contact

Feel free to drop us a line at:

Pinned

  1. self-driving-lab-demo self-driving-lab-demo Public

    Software and instructions for setting up and running a self-driving lab (autonomous experimentation) demo using dimmable RGB LEDs, an 8-channel spectrophotometer, a microcontroller, and an adaptive…

    Jupyter Notebook 63 7

  2. mat_discover mat_discover Public

    A materials discovery algorithm geared towards exploring high-performance candidates in new chemical spaces.

    Python 35 9

  3. matbench-genmetrics matbench-genmetrics Public

    Generative materials benchmarking metrics, inspired by guacamol and CDVAE.

    Jupyter Notebook 28 2

  4. xtal2png xtal2png Public

    Encode/decode a crystal structure to/from a grayscale PNG image for direct use with image-based machine learning models such as Palette.

    Python 34 3

  5. dist-matrix dist-matrix Public

    Fast Numba-enabled CPU and GPU computations of Earth Mover's (scipy.stats.wasserstein_distance) and Euclidean distances.

    Python 14 3

  6. auto-paper auto-paper Public

    The aim of auto-paper is to give you tips, tricks, and tools to accelerate your publication rate and improve publication quality.

    Mathematica 58 7

Repositories

Showing 10 of 64 repositories
  • matbench-genmetrics Public

    Generative materials benchmarking metrics, inspired by guacamol and CDVAE.

    Jupyter Notebook 28 MIT 2 17 0 Updated May 17, 2024
  • self-driving-lab-demo Public

    Software and instructions for setting up and running a self-driving lab (autonomous experimentation) demo using dimmable RGB LEDs, an 8-channel spectrophotometer, a microcontroller, and an adaptive design algorithm, as well as extensions to liquid- and solid-based color matching demos.

    Jupyter Notebook 63 MIT 7 32 (1 issue needs help) 0 Updated May 17, 2024
  • matsci-opt-benchmarks Public

    A collection of benchmarking problems and datasets for testing the performance of advanced optimization algorithms in the field of materials science and chemistry.

    Jupyter Notebook 9 MIT 1 6 0 Updated Apr 16, 2024
  • mat_discover Public

    A materials discovery algorithm geared towards exploring high-performance candidates in new chemical spaces.

    Python 35 MIT 9 24 5 Updated Nov 6, 2023
  • xtal2png Public

    Encode/decode a crystal structure to/from a grayscale PNG image for direct use with image-based machine learning models such as Palette.

    Python 34 MIT 3 26 3 Updated Oct 4, 2023
  • .github Public
    0 MIT 0 0 0 Updated Aug 7, 2023
  • CBFV Public Forked from Kaaiian/CBFV

    Tool to quickly create a composition-based feature vector

    Python 4 MIT 6 0 0 Updated Jun 27, 2023
  • dist-matrix Public

    Fast Numba-enabled CPU and GPU computations of Earth Mover's (scipy.stats.wasserstein_distance) and Euclidean distances.

    Python 14 MIT 3 0 2 Updated Jun 23, 2023
  • gridrdf Public Forked from CumbyLab/gridrdf

    Code for calculating grouped representation of interatomic distances (GRID) from crystal structures, and applying this in machine learning models.

    Python 0 MIT 3 0 0 Updated Jun 22, 2023
  • chem_wasserstein Public Forked from lrcfmd/ElM2D

    A high performance mapping class to construct ElM2D plots from large datasets of inorganic compositions.

    Python 4 GPL-3.0 3 3 0 Updated Jun 19, 2023

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