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Open Access

ARTICLE

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

Shuai Li1, Dongrong Liu1, Shu Li2,*, Minghua Chen2
1 School of Materials Science and Chemical Engineering, Harbin University of Science and Technology, Harbin, China
2 Key Laboratory of Engineering Dielectric and Applications (Ministry of Education), School of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin, China
* Corresponding Author: Shu Li. Email: email
(This article belongs to the Special Issue: Machine Learning Methods in Materials Science)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.075830

Received 09 November 2025; Accepted 23 January 2026; Published online 13 February 2026

Abstract

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.

Keywords

High-performance material exploration; machine learning; interpolation-extrapolation trade-off; Mg-Zn-Al alloy; dual-driven approach

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