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説明:

Self-driving laboratories (SDLs) that combine automated experiments with machine learning have accelerated data-driven discovery. Although Bayesian optimization (BO) is widely used in SDLs to autonomously propose experimental conditions, many real systems require sampling diverse near-optimal candidates rather than identifying a single optimum. We propose nested Thompson sampling (NTS), a batch BO method that enhances diversity by incorporating the concept of nested sampling. In NTS, regions where the posterior exceeds a likelihood threshold are uniformly sampled, enabling exploration of multiple promising regions while requiring only one hyperparameter, that is, the threshold schedule. Benchmark studies using materials
datasets demonstrated that NTS achieves higher sample diversity than a conventional batch BO method. Furthermore, application of NTS to automated electrolyte exploration in an SDL successfully produced diverse experimental samples. The NTS algorithm is implemented in the NIMO package, providing a practical framework for autonomous and diverse materials exploration.

権利情報:

• Creative Commons BY-NC-ND Attribution-NonCommercial-NoDerivs 4.0 International
  

キーワード: Self-driving laboratory, Thompson sampling

刊行年月日: 2026-04-07

出版者: Springer Science and Business Media LLC

掲載誌:

• npj Computational Materials (ISSN: 20573960) vol. 12 issue. 1 197

研究助成金:

• Ministry of Education, Culture, Sports, Science, and Technology (MEXT) Program: Data Creation and Utilization Type Materials Research and Development Projec JPMXP1121467561
• JST PRESTOJPMJPR24T8 JPMJPR24T8
• JST CREST JPMJCR21O2

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1038/s41524-026-02067-0

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更新時刻: 2026-09-16 10:04:42 +0900

MDRでの公開時刻: 2026-09-16 12:27:18 +0900