Issue 9, 2024

PerQueue: managing complex and dynamic workflows

Abstract

Workflow managers play a critical role in the efficient planning and execution of complex workloads. A handful of these already exist within the world of computational materials discovery, but their dynamic capabilities are somewhat lacking. The PerQueue workflow manager is the answer to this need. By utilizing modular and dynamic building blocks to define a workflow explicitly before starting, PerQueue can give a better overview of the workflow while allowing full flexibility and high dynamism. To exemplify its usage, we present four use cases at different scales within computational materials discovery. These encapsulate high-throughput screening with Density Functional Theory, using active learning to train a Machine-Learning Interatomic Potential with Molecular Dynamics and reusing this potential for kinetic Monte Carlo simulations of extended systems. Lastly, it is used for an active-learning-accelerated image segmentation procedure with a human-in-the-loop.

Graphical abstract: PerQueue: managing complex and dynamic workflows

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Article information

Article type
Paper
Submitted
15 May 2024
Accepted
07 Aug 2024
First published
08 Aug 2024
This article is Open Access
Creative Commons BY-NC license

Digital Discovery, 2024,3, 1832-1841

PerQueue: managing complex and dynamic workflows

B. H. Sjølin, W. S. Hansen, A. A. Morin-Martinez, M. H. Petersen, L. H. Rieger, T. Vegge, J. M. García-Lastra and I. E. Castelli, Digital Discovery, 2024, 3, 1832 DOI: 10.1039/D4DD00134F

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