were extracted from the dataset (see supplementary Table. 1). Local features were further expanded using different statistical measures (e.g., mean, and median, etc.) to get an overall representation for each configuration in the dataset leading to a total of 221 feature vectors. These vectors could potentially contain many redundant and irrelevant features for the specific prediction task. To alleviate this issue, an mRMR (Maximum Relevance and Minimum Redundance) algorithm was used to sort features based on their initial relevance score. A certain number of subset features (150 in our case) were selected and passed onto section 2, where a representative set of ML models are used for property prediction. We evaluate 8 different ML models, Linear regression (LR), K-Nearest Neighbor regression (KNN) , Support Vector Regression (SVR) , Random Forest (RF) model, Kernel Ridge Regression (KRR) , Gradient Boosting Method (GBM) , Gaussian Process Regression (GPR) and Multilayer Perceptron (MLP), for the benchmarking task. Additionally, a Graph neural network (GNN) CGCNN 7 was trained on the ranked feature space. All the ML models were implemented using the python-based library Scikit-Learn . Finally for all the trained models, the feature importance of each model was computed using a SHAP workflow in sections 3.