AIsteels is a materials intelligence platform designed to assist operators and developers in extreme-environment industries with making critical material decisions. The platform addresses the challenge of costly and slow experimental campaigns commonly used for decisions such as run/retire assessments, coating replacements, and alloy down-selection. Instead of relying solely on traditional experimental data, AIsteels introduces a suite of three physics-based tools—Austenitic, Ferritic, and HEAs—that share a unified Bayesian uncertainty-quantified framework. This approach allows a single experimental campaign to inform all relevant modules at once, with blind predictions made before exposure rather than after. The tools provide physics-informed decision support by ranking available options, quantifying uncertainty, and identifying which subsequent experiment is expected to yield the greatest information gain. The platform emphasizes experimental prioritization and uncertainty tracking, aiming to measurably and audibly reduce the number of experiments required. One of the modules, the Austenitic + coating tool, is described as a physics-first coating lifetime optimizer. It was originally developed for FeCrAl laser cladding on 316L in lead-bismuth eutectic (LBE) nuclear service at 600°C. The core engine behind this module incorporates models such as Arrhenius corrosion kinetics, Miner’s rule for damage accumulation, parabolic oxide growth, the Griffith spallation sigmoid, and Bayesian Sobol uncertainty quantification. The architecture of AIsteels is described as clean and modular, supporting a framework that can adapt from current needs—like optimizing coatings on existing alloys—to future scenarios involving new bulk materials or alloys for even more demanding environments. The evidence does not specify details regarding the delivery platform, user interface, pricing, or licensing. The tool is positioned as a materials intelligence stack for industrial decision support in high-stakes, extreme environments.
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