Machine Learning Models for Accurately Predicting Properties of CsPbCl3 Perovskite Quantum Dots
This work addresses the need for accurate property prediction in nanomaterials design, but it is incremental as it applies existing ML methods to a specific dataset.
This study tackled the problem of predicting size, absorbance, and photoluminescence properties of CsPbCl3 perovskite quantum dots using machine learning models, with SVR and NND achieving high accuracy as indicated by high R² and low RMSE and MAE values.
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.