In this enlightening episode, we welcome Dr. Cheng, a leading researcher in the field of machine learning (ML). We explore the innovative approach of ML in representing molecular-orbital-based (MOB) features for predicting post-Hartree–Fock correlation energies. Though previous applications of MOB-ML using Gaussian Process Regression (GPR) have shown promise, Dr. Cheng addresses the limitations of this method, particularly its computational constraints, when dealing with large datasets.

Dr. Cheng introduces us to an advanced approach, employing a clustering/regression/classification model of MOB-ML. He elaborates on the three-step process: partitioning the training data using regression clustering (RC), independently regressing each cluster using either linear regression (LR) or GPR, and training a random forest classifier (RFC) for predict ...