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Conference Spotlight
Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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Latest News
Atomic Canyon partners with INL on AI benchmarks
As interest and investment grows around AI applications in nuclear power plants, there remains a gap in standardized benchmarks that can quantitatively compare and measure the quality and reliability of new products.
Nuclear-tailored AI developer Atomic Canyon is moving to fill that gap by entering into a new strategic partnership with Idaho National Laboratory to develop and release the “first comprehensive benchmark suite for evaluating retrieval-augmented generation (RAG) and large language models (LLMs) in nuclear applications.”
N. V. Kornilov, S. M. Grimes, A. Voinov
Nuclear Science and Engineering | Volume 172 | Number 3 | November 2012 | Pages 278-286
Technical Paper | doi.org/10.13182/NSE11-61
Articles are hosted by Taylor and Francis Online.
The variations of ˜14-MeV (n, p), (n, ), and (n, 2n) reaction cross sections with A and Z have been analyzed. We tried to answer a rather interesting question: Can a simple parameterization be useful in comparing with nuclear reaction model calculations? In addition, we checked several approaches for parameterization. Simple systematics gave a better prediction than model calculation for the (n, 2n) reaction at A > 120. At a low mass number, the difference between experimental data and calculated or fitted results may be connected with the structure of levels for residual nuclei. We saw better agreement between experimental and fitted data in comparison with results of model calculation in particular for the (n, ) reaction for A < 110. Both approaches failed to predict (n, p) cross sections inside experimental uncertainties for A < 110 and the (n, ) cross section for A > 110. This failure may be connected with low accuracy of experimental data or with some unknown physical effect that provides an additional splitting of experimental data.