Data and software: Stress and heat flux via automatic differentiation

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Dataset information

Country of origin
Updated
2023.05.03 00:00
Created
2023.04.21
Available languages
English
Keywords
materials science, automatic differentiation, machine learning, molecular dynamics
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Dataset description

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
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