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2d ago

Tohoku University Debuts AI Materials Map to Speed Thermoelectric Discovery

The tool merges experimental literature with first‑principles computations to position candidates by structural similarity alongside predicted thermoelectric performance.

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Overview

  • The map visualizes materials as points on a graph where proximity reflects structural similarity and an axis captures predicted zT for thermoelectric performance.
  • Researchers combined StarryData2 literature measurements with Materials Project calculations and trained MatDeepLearn using a message‑passing neural network.
  • The team says the map helps experimentalists identify analogs of promising materials and reuse established synthesis protocols to reduce trial‑and‑error.
  • The study, led by Yusuke Hashimoto with Takaaki Tomai, Xue Jia, and Hao Li at Tohoku University’s AIMR/FRIS, was published in APL Machine Learning on July 28, 2025.
  • Planned extensions target magnetic and topological materials with added magnetic, chemical, and topological descriptors toward a broader design platform.