Machine Learning Steel Ms Temperature

Empirical equations, thermodynamics frameworks, and neural network modeling have been developed to predict steel martensite start temperature, [Formula: see text], but they might not tend to generalize well when composition includes a wide range of alloying elements. In this study, we develop the Gaussian process regression (GPR) model to shed light on the relationship between alloying elements and [Formula: see text] temperature for steels. A total of 1119 steels with [Formula: see text] ranging from 153 K to 938 K are examined. The model has a high degree of accuracy and stability, contributing to fast low-cost [Formula: see text] temperature estimations.