AI‐Driven Defect Engineering for Advanced Thermoelectric Materials
Name
Advanced Materials - 2025 - Fu - AI‐Driven Defect Engineering for Advanced Thermoelectric Materials.pdf
Description
Published version
Size
7.29 MB
Format
Adobe PDF
Checksum (MD5)
c46553d12ce1e8e05e760d24a6e66c8f
Author(s)
Fu, Chu‐Liang
Cheng, Mouyang
Hung, Nguyen Tuan
Rha, Eunbi
Chen, Zhantao
Okabe, Ryotaro
Carrizales, Denisse Córdova
Mandal, Manasi
Cheng, Yongqiang
Li, Mingda
Date Issued
June 23, 2025
Journal
Advanced Materials
Publisher
Wiley
Citation
C.-L. Fu, M. Cheng, N. T. Hung, et al. “ AI-Driven Defect Engineering for Advanced Thermoelectric Materials.” Adv. Mater. 37, no. 35 (2025): 37, 2505642.
Version
Final published version
Abstract
Thermoelectric materials offer a promising pathway to directly convertwaste heat to electricity. However, achieving high performance remainschallenging due to intrinsic trade-offs between electrical conductivity, theSeebeck coefficient, and thermal conductivity, which are further complicatedby 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-basedmodels, and transformer architectures, integrated with high-throughputsimulations and growing databases, effectively capture structure-propertyrelationships in a complex multiscale defect space and overcome the “curse ofdimensionality”. This review discusses AI-enhanced defect engineering strate-gies such as composition optimization, entropy and dislocation engineering,and grain boundary design, along with emerging inverse design techniquesfor generating materials with targeted properties. Finally, it outlines futureopportunities in novel physics mechanisms and sustainability, highlightingthe critical role of AI in accelerating the discovery of thermoelectric materials.
MIT Department
Massachusetts Institute of Technology. Department of Nuclear Science and Engineering
Massachusetts Institute of Technology. Center for Computational Science and Engineering
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Massachusetts Institute of Technology. Department of Chemistry
Terms of Use
Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1002/adma.202505642
