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PAL – parallel active learning for machine-learned potentials
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AI
DOI：10.1039/D5DD00073D.png)
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Abstract
En 中文
Constructing datasets representative of the target domain is essential for training effective machine learning models. Active learning (AL) is a promising method that iteratively extends training data to enhance model performance while minimizing data acquisition costs. However; current AL workflows often require human intervention and lack parallelism; leading to inefficiencies and underutilization of modern computational resources. In this work; we introduce PAL; an automated; modular; and parallel active learning library that integrates AL tasks and manages their execution and communication on shared- and distributed-memory systems using the Message Passing Interface (MPI). PAL provides users with the flexibility to design and customize all components of their active learning scenarios; including machine learning models with uncertainty estimation; oracles for ground truth labeling; and strategies for exploring the target space. We demonstrate that PAL significantly reduces computational overhead and improves scalability; achieving substantial speed-ups through asynchronous parallelization on CPU and GPU hardware. Applications of PAL to several real-world scenarios – including ground-state reactions in biomolecular systems; excited-state dynamics of molecules; simulations of inorganic clusters; and thermo-fluid dynamics – illustrate its effectiveness in accelerating the development of machine learning models. Our results show that PAL enables efficient utilization of high-performance computing resources in active learning workflows; fostering advancements in scientific research and engineering applications.
Keywords:
active learning
parallel computing
machine learning
high-performance computing
uncertainty estimation
Journal
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5.6
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981
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1.7K
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