Reproducible Container Solutions for Codes and Workflows in Materials Science

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Authors: Dylan Bissuel, Léo Orveillon, Benjamin Arrondeau, João Paulo Almeida de Mendonça, Jonathan Daubin, Irina Piazza, Martin Uhrin, Étienne Polack, Akshay Krishna Ammothum Kandy, David Martin‐Calle, Jonathan Chapignac, Aadhityan Arivazhagan
+7 more Lorenzo Paulatto, Pierre‐Antoine Bouttier, Marie‐Ingrid Richard, Thierry Deutsch, David Rodney, A. Marco Saitta, N. Jakse
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Abstract

A computing solution combining the GNU Guix functional package manager with the Apptainer container system is presented. This approach provides fully declarative and reproducible software environments suitable for computational materials science. Its versatility and performance enable the construction of complete frameworks integrating workflow managers such as AiiDA and Ewoks that can be deployed on HPC infrastructures. The efficiency of the solution is illustrated through several examples: (i) AiiDA workflows for automated dataset construction and analysis as well as path‐integral molecular dynamics based on ab initio calculations; (ii) workflows for the training of machine‐learning interatomic potentials; and (iii) an Ewoks workflow for the automated analysis of coherent X‐ray diffraction data in large‐scale synchrotron facilities. These examples demonstrate that the proposed environment provides a reliable and reproducible basis for computational and data‐driven research in materials science.

Fields of Study & Subjects

Machine Learning in Materials Science Scientific Computing and Data Management Enzyme Structure and Function

Publication details

Journal / source
Advanced Engineering Materials
Publication date
9 April 2026
Language
EN