Materials Science

Frameworks

General-Purpose

•   sklearn : The go-to standard for "conventional" (i.e. not graph-related) classification, regression, and clustering machine learning algorithms. This is the package you should start with when getting familiar with machine learning.
•   PyTorch : A Python library for training and running neural networks. This is the deep learning framework we use in the group.
•   PyTorch Geometric : PyTorch-based library for graph neural networks.
•   DGL : A library for doing deep learning on graphs, which is framework-agnostic (i.e. it is interoperable with PyTorch, TensorFlow, or MXNet). This is an excellent resource if you are building new deep learning models.
•  PySR : Symbolic regression, for when having an equation is desirable.

NLP Tools

•   Marvin : Build natural language processing interfaces in practical applications.
•  Paper QA : Use large language models to answer questions from document libraries with citations.