Machine Learning Models for Accurately Predicting Properties of CsPbCl3 Perovskite Quantum Dots

TL;DR

This study evaluates various machine learning models for predicting key properties of CsPbCl3 perovskite quantum dots, demonstrating high accuracy especially with Support Vector Regression and Nearest Neighbor Distance.

Contribution

It compares multiple ML models for property prediction of PQDs, highlighting the superior performance of SVR and NND in this context.

Findings

SVR and NND achieved the highest accuracy in property prediction.

All models performed highly accurately, with SVR and NND outperforming others.

ML models can effectively predict PQD properties, aiding nanomaterials design.

Abstract

Perovskite Quantum Dots (PQDs) have a promising future for several applications due to their unique properties. This study investigates the effectiveness of Machine Learning (ML) in predicting the size, absorbance (1S abs) and photoluminescence (PL) properties of $\mathrm{CsPbCl}_3$ PQDs using synthesizing features as the input dataset. the study employed ML models of Support Vector Regression (SVR), Nearest Neighbour Distance (NND), Random Forest (RF), Gradient Boosting Machine (GBM), Decision Tree (DT) and Deep Learning (DL). Although all models performed highly accurate results, SVR and NND demonstrated the best accurate property prediction by achieving excellent performance on the test and training datasets, with high $\mathrm{R}^2$ and low Root Mean Squared Error (RMSE) and low Mean Absolute Error (MAE) metric values. Given that ML is becoming more superior, its ability to understand the QDs field could prove invaluable to shape the future of nanomaterials designing.