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Composable neural emulators accelerate thermoelectric generator design.

Airan Li1, Xinzhi Wu1, Longquan Wang1

  • 1Research Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science (NIMS), Tsukuba, Japan.

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Summary

We developed TEGNet, an AI tool that accurately predicts thermoelectric generator performance 10,000 times faster than traditional methods. This accelerates the design of efficient thermoelectric devices using advanced materials.

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Area of Science:

  • Materials Science
  • Artificial Intelligence
  • Energy Conversion

Background:

  • Designing high-performance thermoelectric (TE) devices requires optimal material configurations, which are time-consuming to identify.
  • Current methods for TE device optimization are computationally intensive and resource-limited.

Purpose of the Study:

  • To develop an artificial intelligence (AI) tool, TEGNet, for rapid and accurate prediction of TE generator performance.
  • To demonstrate the AI tool's capability in optimizing diverse TE device architectures and material combinations.

Main Methods:

  • Developed TEGNet, a neural network emulator for predicting TE generator performance.
  • Validated TEGNet's accuracy against commercial finite-element solvers, achieving >99% accuracy.
  • Utilized TEGNet for rapid exploration and experimental optimization of segmented and paired TE generators.

Main Results:

  • TEGNet predicts TE generator performance with >99% accuracy, using only 0.01% of computational time compared to finite-element solvers.
  • Achieved experimental conversion efficiencies of 9.3% for MgAgSb/Bi0.4Sb1.6Te3 segmented TE generators.
  • Achieved experimental conversion efficiencies of 8.7% for Mg3Bi1.4Sb0.6-MgAgSb n-p paired TE generators.

Conclusions:

  • TEGNet enables significantly faster and more accurate TE generator design and optimization.
  • AI-driven approaches hold immense potential for advancing thermoelectric materials and device development.
  • The developed AI tool facilitates rapid exploration of diverse TE device architectures and material systems.