Package Health Score

61 / 100

security
No known security issues
popularity
Small
maintenance
Sustainable
community
Limited

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Popularity

Small

GitHub Stars: 0
Forks: 0
Contributors: 1

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package pykinml, we found that it has been starred ? times.

Security

No known security issues

Security and license risk for latest version

Release Date: May 28, 2024
Direct Vulnerabilities: 0
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0
H
0
M
0
L
Indirect Vulnerabilities: 0
C
0
H
0
M
0
L
License Risk: 0
H
0
M
0
L

All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

Copyright 2024 National Technology & Engineering Solutions of Sandia, LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights in this software. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

Security Policy

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Maintenance

Sustainable

Commit Frequency

Open Issues: 0
Open PR: 0
Last Release: 6 months ago
Last Commit: 5 months ago

Further analysis of the maintenance status of pykinml based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Sustainable.

We found that pykinml demonstrates a positive version release cadence with at least one new version released in the past 12 months.

In the past month we didn't find any pull request activity or change in issues status has been detected for the GitHub repository.

Community

Limited

Readme: Yes
Contributing.md: No
Code of Conduct: No
Contributors: 1
Funding: No

This project has seen only 10 or less contributors.

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Package

Python Versions Compatibility: >=3.8

Age: 6 months
Latest Release: 6 months ago
Dependencies: 2 Direct
Versions: 3
Maintainers: 1
Wheels: OS Independent

Readme

pyKinML: Package for training Neural Net Potential Energy Surfaces

Description

This repository contains the code to train NNPESs and use those models with an ASE calculator.

How to install

This package can be installed with pip or by cloning this repo and installing it locally. First, make sure to have pytorch installed:

https://pytorch.org/

This package relies on pytorch_scatter to sum the atomic contributions to energy. Ensure you have the proper version. Instructions for installing torch_scatter can be found at:

https://github.com/rusty1s/pytorch_scatter

Install with pip:

pip install pykinml

Clone from repo:

git clone git@github.com:sandialabs/pykinml.git

We also highly recomend (required for force training) using the aevmod package for calculation of the aevs and their jacobians: https://github.com/sandialabs/aevmod.git

For transition state optimization, we recomend Sella: https://github.com/zadorlab/sella

This code is designed for the training and running of atomistic neural network potentials. It pulls training data from sql files which should include energies, forces, and atomic corrdinates. The sql files should be named in with the chemical formula of the molecules within them (e.g. C5H5.db, C2H4.db, etc.) and the atomic coordinates and forces should be in the same sequence as thier cooresponding atom type in the database name. The models use ANI style atomic environment vectors (AEVs). For details on AEV descriptors see DOI:https://doi.org/10.1039/C6SC05720A. During training, the AEVs for each structure are computed prior to entering the training loop and saved. This ensures that the AEVs only need to be computed a single time.

pykinml dependencies

aevmod pandas

FAQs

What is pykinml?

Neural Net Potential Energy Surface. Visit Snyk Advisor to see a full health score report for pykinml, including popularity, security, maintenance & community analysis.

Is pykinml popular?

The python package pykinml receives a total of 2,577 weekly downloads. As such, pykinml popularity was classified as small. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is pykinml well maintained?

We found indications that pykinml maintenance is sustainable demonstrating some project activity. We saw a total of 1 open source contributors collaborating on the project. See the full package health analysis to learn more about the package maintenance status.

Is pykinml safe to use?

The python package pykinml was scanned for known vulnerabilities and missing license, and no issues were found. Thus the package was deemed as safe to use. See the full health analysis review.

Last updated on 6 November-2024, at 19:38 (UTC).

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pykinml

Neural Net Potential Energy Surface For more information about how to use this package see README

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Readme

pyKinML: Package for training Neural Net Potential Energy Surfaces

Description

How to install

Install with pip:

Clone from repo:

pykinml dependencies

FAQs

What is pykinml?

Is pykinml popular?

Is pykinml well maintained?

Is pykinml safe to use?

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Package Health Score

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Readme

pyKinML: Package for training Neural Net Potential Energy Surfaces

Description

How to install

Install with pip:

Clone from repo:

pykinml dependencies

FAQs

What is pykinml?

Is pykinml popular?

Is pykinml well maintained?

Is pykinml safe to use?

Build a secure application checklist

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Scan your app for vulnerabilities

Source Code

Open Source

Container

Config

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