Metadata-Version: 2.1
Name: pysipfenn
Version: 0.10.0
Summary: Easily extensible Python package for running Structure-Informed Prediction of Formation Energy using Neural Networks (SIPFENN)
Author-email: Adam Krajewski <ak@psu.edu>, Jonathan Siegel <jwsiegel@tamu.edu>
Project-URL: Research Page, https://phaseslab.com/sipfenn
Project-URL: Homepage, https://github.com/PhasesResearchLab/pySIPFENN
Project-URL: Bug Tracker, https://github.com/PhasesResearchLab/pySIPFENN/issues
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pymatgen (>=2022.1.9)
Requires-Dist: torch (>=1.11.0)
Requires-Dist: onnx2torch (>=1.5.2)
Requires-Dist: onnx (>=1.9.0)
Requires-Dist: wget (>=3.2)
Requires-Dist: numpy (>=1.22.0)
Requires-Dist: tqdm (>=4.63.0)
Requires-Dist: natsort (>=8.0.0)
Requires-Dist: pymongo (>=4.2)
Requires-Dist: dnspython

# pySIPFENN
This repository contains SIPFENN software to use Python all the way from atomic structure to the resulting energy, not relying on 3rd party non-Python tools 
for featurization or analysis. It is also designed to allow repid deployment by a researcher with minimal coding experience.
