PerQueue: managing complex and dynamic workflows
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View abstract on PubMed
Summary
This summary is machine-generated.PerQueue enhances computational materials discovery workflows with modular, dynamic building blocks for better overview and flexibility. This workflow manager improves efficiency in tasks like high-throughput screening and active learning.
Area Of Science
- Computational Materials Science
- Workflow Management Systems
- Scientific Computing
Background
- Existing workflow managers for computational materials discovery lack dynamic capabilities.
- Efficient planning and execution of complex workloads are crucial in materials science.
Purpose Of The Study
- Introduce PerQueue, a novel workflow manager designed for dynamic capabilities in computational materials discovery.
- To provide a better overview, flexibility, and high dynamism in defining and executing workflows.
Main Methods
- Utilizing modular and dynamic building blocks to explicitly define workflows.
- Demonstrating PerQueue's application through four diverse use cases in computational materials discovery.
- Implementing high-throughput screening with Density Functional Theory (DFT).
Main Results
- PerQueue successfully managed complex workflows, including active learning for Machine-Learning Interatomic Potentials (MLIPs) with Molecular Dynamics (MD).
- Demonstrated reuse of MLIPs for kinetic Monte Carlo (kMC) simulations.
- Showcased active-learning-accelerated image segmentation with human-in-the-loop integration.
Conclusions
- PerQueue offers a flexible and dynamic solution for computational materials discovery workflows.
- The modular approach of PerQueue enhances the management of complex scientific computing tasks.
- PerQueue facilitates advanced applications from DFT screening to active learning and image analysis.
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