Exploration of high-temperature oxidation resistance laws in ultra-high temperature boride ceramics through data-driven approaches
Source abstract
Abstract
This study employed machine learning that leverage historical experimental data to construct predictive models capable of estimating the oxidation resistance of ultra-high-temperature diboride. The support vector machine regression (SVR) model exhibited superior prediction metrics with R 2 of 0.88. The Shapley-Additive-exPlanations and genetic algorithm symbolic regression models revealed that SiC content plays a crucial role in the antioxidant damage of UHT borides, and the results suggested that the critical addition amount of SiC in the ZrB 2 -SiC system is 13.8 vol%, and the optimal addition amount is 23.5 vol%. The trained SVR model promptly identified optimal formulas exhibiting exceptional oxidation resistance.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Machine Learning in Materials Science · Nuclear Materials and Properties · Advanced materials and composites
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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