earticle

영어

Recent advancements in data-driven methodologies have brought significant attention to the computational prediction of material properties. Traditional machine learning (ML) approaches have struggled to achieve high accuracy due to the complex relationships between a material's structure and its properties. To address this challenge, in this work, we present an ML framework for predicting the stability of silicon (Si) and Si-based alkaline metal alloys with reduced error. This emphasizes the model transferability to discover new silicon alloys with diverse electronic configurations and structures. We explore the effectiveness of two atomic structural descriptors including X-ray diffraction (XRD) and sine coulomb matrix (SCM). The dynamic ensemble learning (DEL) model is trained and evaluated using 750 Si alloys from the materials project database (MPD) and optimized via ensemble learning. The results indicate that the XRD descriptor with DEL performs most reliably for formation energy, total energy and packaging fraction prediction, showing the model robustness and transferability for ultimate efficient silicon anode’s material synthesis.