deeperwin 0.0.4

A tensorflow based framework to calculate solutions to the Schrödinger equation

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. 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, 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.