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PyFraME: Python tools for Fragment-based Multiscale Embedding

Authors: Olsen, Jógvan Magnus Haugaard;

PyFraME: Python tools for Fragment-based Multiscale Embedding

Abstract

This archive contains the source of PyFraME. Project is hosted on GitLab (https://gitlab.com/FraME-projects/PyFraME). PyFraME is a Python package that provides tools for setting up and running fragment-based multiscale embedding calculations. The aim is to provide tools that can automatize the workflow of such calculations in a flexible manner. The typical workflow is as follows: a part of the total molecular system is chosen as the core region which is typically treated a high level of theory the remainder is split into a number of regions each of which can be treated at different levels of theory each region (except the core) is divided into fragments that consist of either small molecules or parts of larger molecules that have been fragmented into smaller computationally manageable fragments a calculation is run on each fragment to obtain fragment parameters (if necessary) all fragment parameters of all regions are assembled and constitute the embedding potential a final calculation is run on the core region using the embedding potential to model the effect from the remainder of the molecular system

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Keywords

Computational Chemistry, Quantum Chemistry, Polarizable Embedding, QM/MM, Multiscale Modeling

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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