Machine learning: XGBoost

XGBoost, an advanced machine learning algorithm based on gradient boosting, excels in predicting material properties in the field of materials science. By leveraging large-scale experimental and computational data, XGBoost constructs highly accurate surrogate models that capture complex relationships between material features (composition, structure, processing conditions) and their properties (mechanical, thermal, electrical, etc.). Its efficiency, flexibility, and ability to handle large datasets make it an ideal tool for material design, screening, and optimization, significantly enhancing the discovery and development of novel materials.

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