Machine learning (ML) methods have been widely explored for predicting material properties. However, due to the rapid development of ML techniques and the diversity of available models, performance comparisons between traditional and graph-based machine learning models remain limited. Therefore, we evaluate 11 conventional ML models alongside the graph neural network-based Crystal Graph Convolutional Neural Network (CGCNN) for predicting three key properties—formation energy (E_f), band gap (E_g), and energy above hull (E_h)—across a dataset comprising single perovskites, double perovskites, and their combined structures. The results demonstrate that for single perovskites, CGCNN exhibits gains of over 20% in the root mean square error (RMSE) relative to the second-best model (Gradient Boosting Regression), achieving values of 0.205 eV/atom (E_f), 0.718 eV (E_g), and 0.167 eV/atom (E_h). Prediction accuracy for double perovskites is significantly enhanced by training CGCNN on a combined dataset, particularly for E_h, where the coefficient of determination (R²) improves approximately 68.1-fold compared to models trained exclusively on double-perovskite data. Feature importance analysis via one-shot, permutation-based, and recursive feature elimination (RFE) methods reveals that optimal model performance requires retention of at least the top 20 critical features. Furthermore, feature utilization patterns of CGCNN across different prediction tasks are visualized. This work provides actionable guidelines for model selection and feature engineering in perovskite property prediction, establishing a benchmark for future ML-driven materials discovery.

Mental nanomaterial-mediated photothermal therapy (PTT) offers a prospective therapeutic strategy for solid tumors. However, its clinical application is limited due to insufficient photothermal conversion efficiency for deep-seated tumor and potential cytotoxic. Few photothermal therapy studies have been reported for tumour cells of different morphologies. In this study, CdTeSe/ZnS quantum dots (QDs) generating photoluminescence at 562 nm and 640 nm under 365 nm excitation are presented. The proliferation activity of RAW264.7 cells phagocytosed QDs exhibited a dose-dependent decline, as expected. In vitro, a liquid core fiber loaded with these QDs was utilized for PTT targeting high-grade and low-grade brain glioma cells. In the culture medium of cultured brain glioma cells, the maximum heat around the QDs-fibers under 365 nm laser excitation reached 64.9℃. High-grade U87 glioma cells treated with PTT displayed greater sensitive towards organelle damage compared to low-grade U251 glioma cells. Notably, U87 cells showed significant induction of apoptosis. Therefore, these findings represent further progress towards the applications of QDs-fibers in human solid tumor treatment.