FAIR AI Models in High Energy Physics

Duarte, J.; Li, Haoyang; Roy, A.; Huerta, E. A.; Díaz, D.; Harris, P.; Kansal, Raghav; Katz, Daniel S.; Kavoori, Ishaan H.; Kindratenko, Volodymyr; Mokhtar, F.; Neubauer, M. S.; Park, Sang Eon; Quinnan, M.; Rusack, R.; Zhao, Z. G.

doi:10.48550/arxiv.2212.05081

Cited by 2 publications

(1 citation statement)

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“…Similar approaches have been mirrored in science and engineering in recent years. These efforts are now being formalized through FAIR (findable, accessible, interoperable and reusable) initiatives [55,56] in the context of scientific datasets [57], research software [58] and AI models [8,59]. This study represents yet another significant step in this direction.…”

Section: Discussionmentioning

confidence: 99%

End-to-end AI framework for interpretable prediction of molecular and crystal properties

Park

Zhu

Huerta

et al. 2023

Mach. Learn.: Sci. Technol.

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We introduce an end-to-end computational framework that allows for hyperparameter optimization using the DeepHyper library, accelerated model training, and interpretable AI inference. The framework is based on state-of-the-art AI modelsincluding CGCNN, PhysNet, SchNet, MPNN, MPNN-transformer, and TorchMD-NET. We employ these AI models along with the benchmark QM9, hMOF, and MD17 datasets to showcase how the models can predict user-specified material properties within modern computing environments. We demonstrate transferable applications in the modeling of small molecules, inorganic crystals and nanoporous metal organic frameworks with a unified, standalone framework. We have deployed and tested this frameworkin the ThetaGPU supercomputer at the Argonne Leadership Computing Facility, and in the Delta supercomputer at the National Center for Supercomputing Applications to provide researchers with modern tools to conduct accelerated AI-driven discovery in leadership-class computing environments. We release these digital assets as open source scientific software in GitLab, and ready-to-use Jupyter notebooks in Google Colab.

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Section: Discussionmentioning

confidence: 99%

End-to-end AI framework for interpretable prediction of molecular and crystal properties

Park

Zhu

Huerta

et al. 2023

Mach. Learn.: Sci. Technol.

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FAIR for AI: An interdisciplinary and international community building perspective

et al. 2023

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A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles were also meant to apply to other digital assets, at a high level, and over time, the FAIR guiding principles have been re-interpreted or extended to include the software, tools, algorithms, and workflows that produce data. FAIR principles are now being adapted in the context of AI models and datasets. Here, we present the perspectives, vision, and experiences of researchers from different countries, disciplines, and backgrounds who are leading the definition and adoption of FAIR principles in their communities of practice, and discuss outcomes that may result from pursuing and incentivizing FAIR AI research. The material for this report builds on the FAIR for AI Workshop held at Argonne National Laboratory on June 7, 2022.

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FAIR AI Models in High Energy Physics

Cited by 2 publications

References 0 publications

End-to-end AI framework for interpretable prediction of molecular and crystal properties

End-to-end AI framework for interpretable prediction of molecular and crystal properties

FAIR for AI: An interdisciplinary and international community building perspective

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