Machine-learning-enhanced Arrhenius constitutive modeling, microstructural evolution and deformation mechanisms of Gd during hot compression

This dataset contains the source data, training code and trained model files for the machine-learning flow-stress models developed for high-purity gadolinium (Gd, 99.86 wt.%) under hot compression. Flow-stress curves were obtained by isothermal uniaxial compression on a Gleeble-3500 thermomechanical simulator at 500-800 °C and strain rates of 0.01-10 s^-1, to a height reduction of 60%. All curves were corrected for friction and deformation heating before modelling. Three models are included: (1) BP-E, a direct-stress back-propagation neural network ensemble using true strain, a temperature descriptor and log10(strain rate) as inputs; (2) an Arrhenius-residual BP ensemble trained on the residual between the experimental stress and a fixed seventh-order strain-compensated Arrhenius baseline; (3) AR-FHE, a functional heterogeneous ensemble combining functional Gaussian process regression, functional gradient boosting regression and the Arrhenius-residual BP model, in which residual curves are represented by cubic B-splines and reduced by principal component analysis, with ensemble weights estimated from out-of-fold predictions under leave-one-curve-out cross-validation. Contents: training scripts, requirements files, reproducibility instructions, the 25 training curves, intermediate Arrhenius baseline tables, trained model weights, and prediction outputs. External-test isolation: the experimental curves at 650 °C and strain rates of 0.01, 0.1, 1 and 10 s^-1 were excluded from model training, hyperparameter optimization and ensemble-weight estimation. They are provided only as ground truth for the final external evaluation.