Building Machine Learning systems for multi-atoms structures: CH3NH3PbI3 perovskite nanoparticles

Abstract

In this study, we built a variety of Machine Learning (ML) systems over 23 different sizes of CH3NH3PbI3 perovskite nanoparticles (NPs) to predict the atoms in the NPs from their geometric locations. Our findings show that a specific type of ML algorithms, tree-based models which are Random Forest (RF), Extreme Gradient Boosting (XGBoost), Decision Trees (DT), can perfectly learn CH3NH3PbI3 perovskite NPs. Surprisingly, some popular ML algorithms such as Naive Bayes (NB), Support Vector Machines (SVM), Partial Least Squares (PLS), Regularized Logistic Regression (LR), Neural Networks (NN), Stacked Auto-Encoder Deep Neural Network (DNN), K-Nearest Neighbor (KNN) fail to learn CH3NH3PbI3 perovskite NPs.