MLfreq | calculate vibrational frequency from numerical energy

by aikinogard Python Version: Current License: GPL-3.0

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kandi X-RAY | MLfreq Summary

MLfreq is a Python library. MLfreq has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. However MLfreq build file is not available. You can download it from GitHub.

Machine learning density functional will predict energy with small fluctuations due to the interpolation nature of machine learning model. This module will calculate the vibrational frequency from numerical energy. energy input unit is kcal/mol mass input unit is atomic unit (electron mass = 1) x0 input unit is Angstrom (1e-10 meter).

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Support

MLfreq has a low active ecosystem.

It has 0 star(s) with 0 fork(s). There are no watchers for this library.

It had no major release in the last 6 months.

MLfreq has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of MLfreq is current.

Quality

MLfreq has no bugs reported.

Security

MLfreq has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

MLfreq is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

MLfreq releases are not available. You will need to build from source code and install.

MLfreq has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

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MLfreq Key Features

No Key Features are available at this moment for MLfreq.

MLfreq Examples and Code Snippets

No Code Snippets are available at this moment for MLfreq.

Community Discussions

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Vulnerabilities

No vulnerabilities reported

Install MLfreq

You can download it from GitHub.
You can use MLfreq like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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