AI-Driven Defect Engineering for Advanced Thermoelectric Materials
Journal
Advanced Materials
ISSN
15214095
09359648
Date Issued
2025
Author(s)
Fu, Chu Liang
Cheng, Mouyang
Rha, Eunbi
Chen, Zhantao
Okabe, Ryotaro
Carrizales, Denisse Cordova
Mandal, Manasi
Cheng, Yongqiang
Li, Mingda
Abstract
Thermoelectric materials offer a promising pathway to directly convert waste heat to electricity. However, achieving high performance remains challenging due to intrinsic trade-offs between electrical conductivity, the Seebeck coefficient, and thermal conductivity, which are further complicated by the presence of defects. This review explores how artificial intelligence (AI) and machine learning (ML) are transforming thermoelectric materials design. Advanced ML approaches including deep neural networks, graph-based models, and transformer architectures, integrated with high-throughput simulations and growing databases, effectively capture structure-property relationships in a complex multiscale defect space and overcome the “curse of dimensionality”. This review discusses AI-enhanced defect engineering strategies such as composition optimization, entropy and dislocation engineering, and grain boundary design, along with emerging inverse design techniques for generating materials with targeted properties. Finally, it outlines future opportunities in novel physics mechanisms and sustainability, highlighting the critical role of AI in accelerating the discovery of thermoelectric?materials.
Subjects
Artificial Intelligence
Defect Engineering
Machine Learning
Thermoelectrics
Deep Neural Networks
Defect Engineering
Design
Grain Boundaries
Inverse Problems
Learning Systems
Sustainable Development
Thermal Conductivity
Thermoelectric Equipment
Thermoelectricity
Electrical Conductivity
Machine-learning
Performance
Seebeck
Thermal
Thermo-electric Materials
Thermoelectric
Thermoelectric Material
Trade Off
Economic And Social Effects
SDGs
Publisher
John Wiley and Sons Inc
Type
review article