Metadata-Version: 2.1
Name: deeperwin
Version: 0.0.3
Summary: A tensorflow based framework to calculate solutions to the Schrödinger equation
Home-page: UNKNOWN
Author: Rafael Reisenhofer, Michael Scherbela, Leon Gerard
Author-email: rafael.reisenhofer@univie.ac.at
License: UNKNOWN
Description: # DeepErwin
        
        DeepErwin is python package that implements and optimizes TF 2.x wave function models for numerical solutions to the multi-electron Schrödinger equation.
        
        In particular DeepErwin supports:
        - Optimizing a wavefunction for a single nuclear geometry
        - Optimizing wavefunctions for multiple nuclear geometries in parallel, while sharing neural network weights across these wavefunctions to speed-up optimization
        - Use pre-trained weights of a network to speed-up optimization for entirely new wavefunctions
        
        A detailed description of our method and the corresponding results can be found in our recent [arxiv publication](https://arxiv.org/pdf/2105.08351.pdf). Please cite this paper, whenever you use any parts of DeepErwin.
        
        ## Getting Started
        
        The quickest way to get started with DeepErwin is to have a look at our documentation. It has a detailed description of our python codebase and will also guide you through several [examples](examples), which should help you to quickly get up-and-running using DeepErwin.
        
        ## About
        
        DeepErwin is a collaborative effort of Rafael Reisenhofer, Philipp Grohs, Philipp Marquetand, Michael Scherbela, and Leon Gerard (University of Vienna).
        For questions regarding this code, freel free to reach out via [e-mail](mailto:rafael.reisenhofer@univie.ac.at).
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Provides-Extra: full
