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.

这项研究使用机器学习模型，通过使用合成特征作为输入数据集，对CsPbCl3 PQDs的尺寸、吸收性和光致发光性进行预测。虽然所有模型都表现出高准确性的结果，但支持向量回归（SVR）和最近邻距离（NND）在测试和训练数据集上表现出最佳的属性预测性能，具有较高的R2值、较低的均方根误差（RMSE）和较低的平均绝对误差（MAE）度量值。鉴于机器学习的优越性愈发凸显，其在领域中的理解能力可能对纳米材料设计的未来产生重要影响。

准确预测CsPbCl3钙钛矿量子点性质的机器学习模型