https://github.com/lab-cosmo/nice

https://github.com/lab-cosmo/nice

Last synced: 14 days ago
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• Host: GitHub
• URL: https://github.com/lab-cosmo/nice
• Owner: lab-cosmo
• License: mit
• Created: 2020-07-03T08:47:41.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2024-04-15T14:39:34.000Z (10 months ago)
• Last Synced: 2024-04-19T18:37:08.667Z (9 months ago)
• Language: Mathematica
• Size: 11.9 MB
• Stars: 13
• Watchers: 6
• Forks: 3
• Open Issues: 3
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE.txt

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• best-of-atomistic-machine-learning - GitHub - 66% open · ⏱️ 15.04.2024): (Representation Engineering)

README

        
.. role:: bash(code)

   :language: bash

NICE

====

NICE (N-body Iteratively Contracted Equivariants) is a set of tools designed for the calculation of 

invariant and covariant atomic structure representations. It allows for

automatic selection of the most informative combinations of high order spectrum elements

and performs their efficient computation using recurrence relations. 

Although it is designed specifically for atomistic machine learning, NICE in principle 

can be applied to other machine learning tasks, such as those which involve signals in a ball or on a sphere, all which require invariant or covariant outputs. 

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Installation

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1. Install `librascal `_

2. git clone or download archive with nice and unpack

3. cd to root nice directory and run :bash:`pip3 install .`

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Documentation

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Documentation can be found `here `_

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References

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If you are using NICE, please cite `this article `_. 

[1] Jigyasa Nigam, Sergey Pozdnyakov, and Michele Ceriotti. "Recursive evaluation and iterative contraction of N-body equivariant features." The Journal of Chemical Physics 153.12 (2020): 121101.