Accelerated design and fabrication of thermal protection coating via high-throughput experiments and machine learning

For the engineering applications of thermal protection materials (TPMs), the demand targets are often multiple such as high-temperature oxidation, hot corrosion, high temperature/speed ablation, etc., which is a challenging problem for developing a tailored property by an efficient and economical method. In this work, using modified silicide-based coating as the model materials, a hybrid model for the design of TPMS with tailored properties is developed through the high-throughput experiments combined with the machine learning (ML) method. Among the four data sets, XGboost model has the smallest bias in the predicted values, the largest coefficient of determination (R2) value, and the smallest mean square error (MSE). Our work exemplifies the potential of high-throughput experiments and ML-assisted design in advancing the discovery and development of novel TPMS.