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Although density functional theory (DFT) has aided in accelerating thediscovery of new materials, such calculations are computationally expensive,especially for high-throughput efforts. This has prompted an explosion inexploration of machine learning assisted techniques to improve thecomputational efficiency of DFT. In this study, we present a comprehensiveinvestigation of the broader application of Finetuna, an active learningframework to accelerate structural relaxation in DFT with prior informationfrom Open Catalyst Project pretrained graph neural networks. We explore thechallenges associated with out-of-domain systems: alcohol ($C_{>2}$) on metalsurfaces as larger adsorbates, metal-oxides with spin polarization, andthree-dimensional (3D) structures like zeolites and metal-organic-frameworks.By pre-training machine learning models on large datasets and fine-tuning themodel along the simulation, we demonstrate the framework's ability to conductrelaxations with fewer DFT calculations. Depending on the similarity of thetest systems to the training systems, a more conservative querying strategy isapplied. Our best-performing Finetuna strategy reduces the number of DFTsingle-point calculations by 80% for alcohols and 3D structures, and 42% foroxide systems.

Generalization of Graph-Based Active Learning Relaxation Strategies Across Materials