PerQueue: Managing Complex and Dynamic Workflows

16 May 2024, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


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.


Materials modelling
Autonomous workflows
Multiscale modelling
Materials Acceleration Platform
Density Functional Theory


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