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Machine learning predicts parallel chemistry application runtime parameters

By PulseAugur Editorial · [1 sources] ·

Researchers have developed two machine learning approaches to predict runtime parameters for parallel chemistry applications. These methods combine active and generative learning with gradient boosted regression trees, achieving a mean absolute percentage error as low as 0.023 and a coefficient of determination of 99.9% on Coupled-Cluster with Singles and Doubles computations. When active learning is used to address limited training data, the models achieve a MAPE of approximately 0.2 with only 20-25% of the original dataset. AI

RANK_REASON The cluster contains an academic paper detailing a new research methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Tanzila Tabassum, Omer Subasi, Ajay Panyala, Epiya Ebiapia, Gerald Baumgartner, Erdal Mutlu, P Sadayappan, Karol Kowalski ·

    Prediction of Runtime Parameters of Parallel Chemistry Applications via Active and Generative Learning

    arXiv:2606.16226v1 Announce Type: new Abstract: In this work, we develop two main Machine Learning based approaches to predict the runtime parameters of highly scalable parallel chemistry computations.These approaches employ active and generative learning together with the empiri…

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