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ML4Chem is a package to deploy machine learning for chemistry and materials science. It is written in Python 3, and intends to offer modern and rich features to perform machine learning (ML) workflows for chemical physics.

A list of features and ML algorithms are shown below.

  • PyTorch backend.

  • Completely modular. You can use any part of this package in your project.

  • Free software <3. No secrets! Pull requests and additions are more than welcome!

  • Documentation (work in progress).

  • Explicit and idiomatic: ml4chem.get_me_a_coffee().

  • Distributed training in a data parallel paradigm aka mini-batches.

  • Scalability and distributed computations are powered by Dask.

  • Real-time tools to track status of your computations.

  • Messagepack serialization.


If you find this software useful, please use this DOI to cite it:

DOI: 10.5281/zenodo.3471761


To get started, read the documentation at It is arranged in a way that you can go through the theory as well as some code snippets to understand how to use this software. Additionally, you can dive through the module index to get more information about different classes and functions of ML4Chem.


Note: This package is under development.