  An essential aspect of extending safe operation of the active nuclear
reactors is understanding and predicting the embrittlement that occurs in the
steels that make up the Reactor pressure vessel (RPV). In this work we
integrate state of the art machine learning methods using ensembles of neural
networks with unprecedented data collection and integration to develop a new
model for RPV steel embrittlement. The new model has multiple improvements over
previous machine learning and hand-tuned efforts, including greater accuracy
(e.g., at high-fluence relevant for extending the life of present reactors),
wider domain of applicability (e.g., including a wide-range of compositions),
uncertainty quantification, and online accessibility for easy use by the
community. These improvements provide a model with significant new
capabilities, including the ability to easily and accurately explore
compositions, flux, and fluence effects on RPV steel embrittlement for the
first time. Furthermore, our detailed comparisons show our approach improves on
the leading American Society for Testing and Materials (ASTM) E900-15 standard
model for RPV embrittlement on every metric we assessed, demonstrating the
efficacy of machine learning approaches for this type of highly demanding
materials property prediction.
