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In this work, we use a melt process in a vacuum-sealed quartz tube followed by spark plasma sintering to synthesize GeTe compounds. Through the application of active learning with Bayesian optimization, we systematically explore the process parameters to enhance the thermoelectric figure of merit (zT) to achieve a value of zT = 1.14 at 700 K. The results demonstrate an improvement of 25% over the equivalent 2-step process, the same efficiency (within 5%) as the 3-step process while drastically reducing processing time by over 90%, and this within 24 experiments out of more than 18 billion possible combinations. This illustrates the potential of the utilization of GeTe in mid-temperature range applications, and the effectiveness of artificial intelligence assistance to improve materials' properties in the tremendously vast process parameters space. This study is intended to be a textbook case displaying the effectiveness of active learning and Bayesian optimization at performing a full process optimization for enhancing the performance of a material with minimum experimental trials, time and cost.

権利情報:

• Creative Commons BY Attribution 4.0 International
  

キーワード: Material process, bayesian optimisation, active learning, chalcogenides, thermoelectrics

刊行年月日: 2025-12-31

出版者: Informa UK Limited

掲載誌:

• Science and Technology of Advanced Materials: Methods (ISSN: 27660400) vol. 5 issue. 1 2545174

研究助成金:

• JSPS PE20752
• CNRS
• NIMS

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1080/27660400.2025.2545174

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更新時刻: 2025-12-10 08:30:23 +0900

MDRでの公開時刻: 2025-12-10 08:23:46 +0900