A Materials Discovery Method Considering the Trade-Off Phenomenon in Machine Learning Prediction Capabilities between Interpolation and Extrapolation: Case Study on Multi-Objective Mg-Zn-Al Alloy Design

The exploration of high-performance materials presents a fundamental challenge in materials science, particularly in predicting properties for materials beyond the known range of target property values (extrapolation). This study formally investigated the interpolation-extrapolation trade-off phenomenon in the prediction capabilities of machine learning (ML) models. A new ML scheme was proposed, featuring a newly developed ML model and forward cross-validation-based hyperparameter optimization, which demonstrated superior extrapolation prediction across multiple materials datasets. Based on this ML scheme, multi-objective optimization was performed to systematically identify lightweight Mg-Zn-Al alloys with both high bulk modulus and high Debye temperature. Subsequently, the designed alloys were validated through density functional theory calculations. Furthermore, a three-category classification strategy was summarized through the dual-driven approach combining domain knowledge and data, emphasizing their synergistic potential for materials discovery. The practical framework developed in this study provides a novel research perspective for exploring high-performance materials.