New Directions for Thermoelectrics: A Roadmap from High-Throughput Materials Discovery to Advanced Device Manufacturing
Kaidong Song, A. N. M. Tanvir, Md Omarsany Bappy, and Yanliang Zhang

TL;DR
This paper reviews recent progress in high-throughput discovery and advanced manufacturing of thermoelectric materials and devices, highlighting new methods to accelerate development and scalability for energy applications.
Contribution
It provides a comprehensive overview of integrating high-throughput techniques and machine learning with manufacturing advances for thermoelectric materials and devices.
Findings
High-throughput methods accelerate thermoelectric material discovery.
Machine learning enhances screening and optimization processes.
Advanced manufacturing enables scalable, low-cost thermoelectric device production.
Abstract
Thermoelectric materials, which can convert waste heat into electricity or act as solid-state Peltier coolers, are emerging as key technologies to address global energy shortages and environmental sustainability. However, discovering materials with high thermoelectric conversion efficiency is a complex and slow process. The emerging field of high-throughput material discovery demonstrates its potential to accelerate the development of new thermoelectric materials combining high efficiency and low cost. The synergistic integration of high-throughput material processing and characterization techniques with machine learning algorithms can form an efficient closed-loop process to generate and analyze broad data sets to discover new thermoelectric materials with unprecedented performances. Meanwhile, the recent development of advanced manufacturing methods provides exciting opportunities to…
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Taxonomy
TopicsMachine Learning in Materials Science · Advanced Thermoelectric Materials and Devices
