# Collecting diverse near-optimal samples via nested Thompson sampling

https://mdr.nims.go.jp/datasets/b762cd44-32f2-472e-b62a-a47d025be615

## File

- [s41524-026-02067-0.pdf](https://mdr.nims.go.jp/filesets/b3be6072-0bcc-4d3c-a5ca-4685436c884b/download) ([Detail](https://mdr.nims.go.jp/filesets/b3be6072-0bcc-4d3c-a5ca-4685436c884b.md))

## Id

b762cd44-32f2-472e-b62a-a47d025be615

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2026-09-15T20:58:07.066144Z

## Updated at

2026-09-16T01:04:42.508041Z

## Published at

2026-09-16T03:27:18.366664Z

## Doi



## First published url

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

## Date published

2026-04-07

## Recorded date published



## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Collecting diverse near-optimal samples via nested Thompson sampling
  title_type: original
  lang: en

## Description

- description: "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\r\ndatasets 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."
  description_type: abstract
  lang: und

## Creator

- name: Ryosuke Shibukawa
  role: author
- name: Shoichi Matsuda
  role: author
  orcid: https://orcid.org/0000-0002-0640-3404
- name: Kazuha Nakamura
  role: author
- name: Ryo Tamura
  role: author
  orcid: https://orcid.org/0000-0002-0349-358X
- name: Koji Tsuda
  role: author
  orcid: https://orcid.org/0000-0002-4288-1606

## Contact agent



## Publisher

organization: Springer Science and Business Media LLC

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## Keyword

- subject: Self-driving laboratory
  schema: not_defined
- subject: Thompson sampling
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by-nc-nd/4.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: npj Computational Materials
  issn: '20573960'
  volume: '12'
  issue: '1'
  article_number: '197'

## Conference



## Related item



## Funding

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

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## Fileset

- id: b3be6072-0bcc-4d3c-a5ca-4685436c884b
  filename: s41524-026-02067-0.pdf
  content_type: application/pdf
  size: 1699313
  md5: acaed7da8d40404b409e63e54b02d24e

## Thumbnail

fileset_id: b3be6072-0bcc-4d3c-a5ca-4685436c884b
filename: s41524-026-02067-0.pdf