Machine learning-based garnet thermometer program

Traditional mineral-based thermometers involving garnet either require the compositions of coexisting minerals or costly trace element analysis for Ni.

Here, we developed a machine learning (ML)-based garnet thermometer that utilises the major and minor element compositions of garnets alone, including SiO₂, TiO₂, Al₂O₃, Cr₂O₃, FeO, MgO, MnO, CaO, and Na₂O. These elements can be accurately measured by electron probe microanalysis (EPMA). To construct a predictive model for garnet temperatures, we applied XGBoost (Extreme Gradient Boosting), a widely used, powerful, and scalable ML algorithm.