Empirical equations, thermodynamics frameworks, and neural network modeling have been developed to predict steel martensite start temperature, $M_s$, 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 $M_s$ temperature for steels. A total of 1119 steels with $M_s$ ranging from 153 K to 938 K are examined. The model has a high degree of accuracy and stability, contributing to fast low-cost $M_s$ temperature estimations.

中文翻译：

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机器学习钢 ${\mathit{中号}}_s$ 温度

已经开发了经验方程，热力学框架和神经网络模型来预测钢的马氏体起始温度， ${\mathrm{中号}}_s$，但是当成分中包含多种合金元素时，它们可能无法很好地概括。在这项研究中，我们开发了高斯过程回归（GPR）模型，以阐明合金元素与合金之间的关系。${\mathrm{中号}}_s$钢的温度。共有1119种钢${\mathrm{中号}}_s$检查范围从153 K到938K。该模型具有较高的准确性和稳定性，有助于快速实现低成本${\mathrm{中号}}_s$ 温度估算。