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Data and software: Stress and heat flux via automatic differentiation

Authors: Langer, Marcel Florin; Frank, J. Thorben; Knoop, Florian;

Data and software: Stress and heat flux via automatic differentiation

Abstract

glp-archive Code and Data for "Stress and heat flux with automatic differentiation" This repository contains data, code, and related artefacts supporting the following publication (preprint: Stress and heat flux via automatic differentiation by Marcel F. Langer, J. Thorben Frank, and Florian Knoop arXiv:2305.01401 This repository is available at https://github.com/sirmarcel/glp-archive. Selected versions are archived on Zenodo, under doi:10.5281/zenodo.7852530. Overview Each subfolder in this repository contains a README.md with additional information. The subfolders are: results/: Data and code that produced the figures in the manuscript work/: Computational workflows, models, etc. infra/: Project-specific infrastructure code meta/: Scripts for assembling this archive; can be ignored but is retained for transparency. Related external code The work in this repository relies on a few tools that the authors maintain separately: glp implements the quantities discussed in the manuscript mlff implements the so3krates model tools.mlff provides tools for the equation of state experiments These tools were developed during the work in the manuscript. The following versions/tags reflect what was used to obtain results: glp @ v0.1.0 (tag) mlff @ v1.0 (branch) mlff.tools @ v0.0.1 We additionally note that the GK-MD functionality has been factored out into gkx. Versions v1.0: arXiv submission v1

Keywords

machine learning, materials science, automatic differentiation, molecular dynamics

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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.
BIP!Impulse provided by BIP!
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