An integrated machine learning model for accurate and robust prediction of superconducting critical temperature

Machine learning enabled exploration of multicomponent metal oxides for catalyzing oxygen reduction in alkaline media

“…The reason for selecting XGBoost 45 and LightGBM 46 was their This journal is © The Royal Society of Chemistry 2024 high performance across various ML tasks. 58,59 Hyperparameters were optimized using 10-fold cross-validation to prevent overtting, with the details shown in the ESI. † The 10fold cross-validation results are shown in Fig.…”

Application of Machine Learning in Material Synthesis and Property Prediction

“…ML-guided iterative experi mentation may outperform standard high-throughput screening for discovering break through materials in high-temperature superconductors [115,116]. Zhang et al [117] de veloped an integrated ML model to accurately and robustly predict the critical tempera ture (Tc) of superconducting materials (Figure 13a). They used open-source materials data ML models, and data mining methods to explore the correlation between chemical fea tures and Tc values.…”

Accurate and efficient machine learning models for predicting hydrogen evolution reaction catalysts based on structural and electronic feature engineering in alloys

“…7,8 However, trial-and-error experiments and density functional theory (DFT) calculations are challenging due to their time-consuming processes to explore the entire chemical composition of alloys. 9 Over the past decade, datadriven machine learning (ML) models have provided alternative approaches to material design in various research fields, such as superconducting materials, 10,11 thermoelectric materials, 12,13 and electrochemical catalysts. 14,15 Moreover, ML models and feature analysis were utilized to predict the ΔG H* values of alloys, providing new perspectives and accelerating the exploration of potential HER catalysts.…”