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The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate scientific discovery. Generalizing these principles to research software and other digital products is an active area of research. Machine learning models—algorithms that have been trained on data without being explicitly programmed—and more generally, artificial intelligence (AI) models, are an important target for this because of the ever-increasing pace with which AI is transforming scientific domains, such as experimental high energy physics (HEP). In this paper, we propose a practical definition of FAIR principles for AI models in HEP and describe a template for the application of these principles. We demonstrate the template’s use with an example AI model applied to HEP, in which a graph neural network is used to identify Higgs bosons decaying to two bottom quarks. We report on the robustness of this FAIR AI model, its portability across hardware architectures and software frameworks, and its interpretability.

Electrochemical CO₂reduction offers a compelling route to mitigate atmospheric CO₂concentration and store intermittent renewable energy in chemical bonds. Beyond C₁, C₂₊feedstocks are more desirable due to their higher energy density and more significant market need. However, the CO₂‐to‐C₂₊reduction suffers from significant barriers of CC coupling and complex reaction pathways. Due to remarkable tunability over morphology/pore architecture along with great feasibility of functionalization to modify the electronic and geometric structures, carbon materials, serving as active components, supports, and promoters, provide exciting opportunities to tune both the adsorption properties of intermediates and the local reaction environment for the CO₂reduction, offering effective solutions to enable CC coupling and steer C₂₊evolution. However, general design principles remain ambiguous, causing an impediment to rational catalyst refinement and application thrusts. This review clarifies insightful design principles for advancing carbon materials. First, the current performance status and challenges are discussed and effective strategies are outlined to promote C₂₊evolution. Further, the correlation between the composition, structure, and morphology of carbon catalysts and their catalytic behavior is elucidated to establish catalytic mechanisms and critical factors determining C₂₊performance. Finally, future research directions and strategies are envisioned to inspire revolutionary advancements.