• Random forest (RF): A robust ensemble learning algorithm that aggregates the predictions of multiple decision trees, typically leading to enhanced generalisation performance by reducing overfitting and variance in predictive modeling tasks . • Kernel Ridge Regression (KRR): KRR integrates ridge regression with the kernel trick, enabling it to perform nonlinear regression in high-dimensional feature spaces while controlling for model complexity through regularisation . • Crystal Graph Convolutional Neural Network (CGCNN): CGCNN represents crystalline materials as graphs, where atoms serve as nodes and bonds as edges, and learns material properties by applying convolutional operations over the graphs .