ML4Chem
===========
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**ML4Chem** is machine learning for chemistry.
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.
- Atom-centered Neural Networks, and Kernel Ridge Regression for the prediction
of total energies.
- PyTorch backend.
- GPU support.
- ASE interface.
- 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!
- Good documentation (I hope!).
- Explicit and idiomatic: `ml4chem.get_me_a_coffee()`.
- Distributed training in a data parallelism paradigm (mini-batches).
- Scalability and distributed computations are powered by Dask <3.
- Real-time tools to track status of your computations.
- [Messagepack serialization](https://msgpack.org/index.html).
## Dask dashboard
![](https://raw.githubusercontent.com/muammar/ml4chem/master/docs/source/_static/dask_dashboard.png)
Note: This package is under development.
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ml4chem-0.0.0.tar.gz (29个子文件)
ml4chem-0.0.0
PKG-INFO 2KB
ml4chem
models
loss.py 2KB
kernelridge.py 17KB
se3net.py 2KB
neuralnetwork.py 17KB
__init__.py 0B
autoencoders.py 18KB
utils.py 3KB
data
preprocessing.py 5KB
serialization.py 817B
__init__.py 0B
visualization.py 4KB
handler.py 5KB
potentials.py 11KB
__init__.py 69B
backends
operations.py 2KB
available.py 1020B
__init__.py 0B
fingerprints
cartesian.py 9KB
gaussian.py 26KB
__init__.py 141B
cutoff.py 690B
ml4chem.egg-info
PKG-INFO 2KB
SOURCES.txt 729B
top_level.txt 8B
dependency_links.txt 1B
setup.cfg 38B
setup.py 773B
README.md 1KB
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