Supporting Information forA machine learning approach to single garnet geothermometry and application to tracing the fingerprint of superdeep diamonds Table S1 Major and minor element compositions of olivine-orthopyroxene-clinopyroxene-garnet from well-equilibrated peridotites (N = 1308). Table S2 Calculation spreadsheet for Mn-in-garnet thermometer calibrated in this study, assuming a fixed olivine composition. The example data are the garnets from the well-equilibrated peridotite dataset. Table S3 Key hyperparameters used for ML model determined by Bayesian optimisation approach. Table S4 Results of 200 times 5-fold cross validation of the XGBoost model using a training set with TTA98 ranging from 700 to 1450 °C and with TTA98 ranging from 900 to 1400 °C. Table S5 Normalised feature importance after 1000 runs of the XGBoost model using a training set with TTA98 ranging from 700 to 1450 °C and with TTA98 ranging from 900 to 1400 °C. Table S6 Comparison of temperatures calculated by the Ni-in-garnet thermometer (TNM24, Nimis et al. (2024)), the Mn-in-garnet thermometer, and the ML-based garnet thermometer. Table S7 Percentage of garnet types from 25 kimberlites across the Slave Craton and Kaapvaal Craton. Table S8 Major and minor element compositions and temperatures calculated by ML-based thermometer of garnet xenocrysts from the Slave and Kaapvaal Craton