A repository for various introductory tutorials on deep learning for chemistry.


There is often a gap between code written in classes (computer science, chemistry, etc.) and code required to conduct research. Most classes now support Jupyter Notebooks or Google Colab enviornments that have simple install, setup, and often require running only small blocks of code. While very useful and didactic, we find there is also a need to explain how students can structure repositories for new research projects that enable them to organize experiments, try different model settings, and move quickly. This repository is an opinionated attempt to show several ways to structure these repositories for basic tasks we expect any researcher at the intersection of machine learning and chemistry to implement.

See the GitHub repo for links to:

  • code
  • running instructions