Predicting oxidation damage in ultra high-temperature borides: A machine learning approach

Ultra-high temperature (UHT) borides are ceramics materials with melting points above 3000 C for structural applications in extreme environments. However, at temperatures exceeding 1600 C and under oxidizing con-ditions, the material suffers from detrimental degradation. Optimized design and performance of diboride ma-terials under such extreme conditions requires filling the missing composition-microstructure-oxidation gap. This study proposes a computational data-driven framework to connect the processing and microstructure of Ultra-high temperature borides with the oxidation damage. Random Forest Regressor (RFR) model is adopted to forecast the oxide scale thickness developed after oxidation testing based on processing variables and micro -structural features. The model trained on a dataset consisting of 107 samples of experimental data extracted from the literature aims to predict oxidation damage. With proper data manipulation and fine model tuning, the predictor could forecast the oxide scale thickness of UHT diborides with a Mean Absolute Error of 37.45 mu m and an R-square of 0.83. This model could be used as a high-throughput scheme to design and test new UHT diborides materials computationally. A model with larger composition capabilities could also be developed in the future as more experimental data become available.