Machine learning for chemistry and materials.
This package is written in Python 3, and intends to offer modern and rich features to perform machine learning workflows for chemical physics.
A list of features and methods 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:
- Distributed training in a data parallelism paradigm (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:
You can read the documentation at https://ml4chem.dev where you can get started. 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.
ML4Chem: Machine Learning for Chemistry and Materials (ML4Chem) Copyright (c) 2019, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved.
If you have questions about your rights to use or distribute this software, please contact Berkeley Lab's Intellectual Property Office at IPO@lbl.gov.
NOTICE. This Software was developed under funding from the U.S. Department of Energy and the U.S. Government consequently retains certain rights. As such, the U.S. Government has been granted for itself and others acting on its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the Software to reproduce, distribute copies to the public, prepare derivative works, and perform publicly and display publicly, and to permit other to do so.
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