Open Access

REVIEW

Machine Learning-Based Methods for Materials Inverse Design: A Review

Yingli Liu^1,2, Yuting Cui^1,2, Haihe Zhou^1,2, Sheng Lei³, Haibin Yuan³, Tao Shen^1,2,*, Jiancheng Yin^4,*

1 Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650093, China
2 Yunnan Key Laboratory of Computer Technologies Application, Kunming University of Science and Technology, Kunming, 650500, China
3 Yunnan Tin Company Limited Tin Industry Branch, Gejiu, 661000, China
4 Faculty of Materials Science and Engineering, Kunming University of Science and Technology, Kunming, 650093, China

* Corresponding Authors: Tao Shen. Email: ; Jiancheng Yin. Email:

(This article belongs to the Special Issue: Advanced Computer Technology for Materials Characterization, Properties Prediction, Design and Discovery)

Computers, Materials & Continua 2025, 82(2), 1463-1492. https://doi.org/10.32604/cmc.2025.060109

Received 24 October 2024; Accepted 26 December 2024; Issue published 17 February 2025

Abstract

Finding materials with specific properties is a hot topic in materials science. Traditional materials design relies on empirical and trial-and-error methods, requiring extensive experiments and time, resulting in high costs. With the development of physics, statistics, computer science, and other fields, machine learning offers opportunities for systematically discovering new materials. Especially through machine learning-based inverse design, machine learning algorithms analyze the mapping relationships between materials and their properties to find materials with desired properties. This paper first outlines the basic concepts of materials inverse design and the challenges faced by machine learning-based approaches to materials inverse design. Then, three main inverse design methods—exploration-based, model-based, and optimization-based—are analyzed in the context of different application scenarios. Finally, the applications of inverse design methods in alloys, optical materials, and acoustic materials are elaborated on, and the prospects for materials inverse design are discussed. The authors hope to accelerate the discovery of new materials and provide new possibilities for advancing materials science and innovative design methods.

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