# Bayesian diversity control for batch-based phase diagram determination

https://mdr.nims.go.jp/datasets/17450f16-dc14-40a2-8faf-b050bd40c8a9

## File

- [d5dd00486a.pdf](https://mdr.nims.go.jp/filesets/218ef7a3-95a2-4449-a68d-82860ca957d7/download) ([Detail](https://mdr.nims.go.jp/filesets/218ef7a3-95a2-4449-a68d-82860ca957d7.md))

## Id

17450f16-dc14-40a2-8faf-b050bd40c8a9

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2026-03-31T21:35:24.752347Z

## Updated at

2026-04-01T05:33:21.032838Z

## Published at

2026-04-01T07:26:11.200250Z

## Doi



## First published url

https://doi.org/10.1039/d5dd00486a

## Date published

2026-02-16

## Recorded date published

2026-3-18

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Bayesian diversity control for batch-based phase diagram determination
  title_type: original
  lang: en

## Description

- description: Machine learning methods are increasingly used in experimental design
    in phase diagram determination. Some methods perform batch design, where multiple
    points are sampled from the design space. In this case, it is essential to diversify
    samples to avoid performing almost identical experiments, and control the diversity
    level appropriately. Manual diversity control is unintuitive and may require additional
    trial-and-error in prior to the experiments are started. We propose a Bayesian
    model called determinantal point process for phase diagram construction (DPP-PDC)
    that can perform batch design and automatic diversity control simultaneously.
    Central to this model is the uncertainty-weighted determinantal point process
    that samples a set of points with high uncertainty under diversity control. Experiments
    with Cu-Mg-Zn ternary system demonstrate that DPP-PDC can actively control the
    sample diversity to achieve high efficiency.
  description_type: abstract
  lang: und

## Creator

- name: Peiheng Zou
  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: Royal Society of Chemistry (RSC)

## Managing organization



## Keyword

- subject: phase diagram
  schema: not_defined
- subject: machine learning
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by-nc/3.0/

## Other identifier(s)



## Data origin



## Embargo



## Journal

- title: Digital Discovery
  issn: 2635098X
  volume: '5'
  issue: '3'
  start_page: 1252
  end_page: 1256

## Conference



## Related item



## Funding

- identifier: JPMJCR21O2
  funder_name: Core Research for Evolutional Science and Technology
- identifier: JPMJER1903
  funder_name: Exploratory Research for Advanced Technology
- identifier: 25K01492
  funder_name: Japan Society for the Promotion of Science

## Instrument



## Instrument operator



## Instrument managing organization



## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



## Specific property for specimen



## Process for specimen treatment



## Computational method



## Energy level/transition state



## Software



## Custom property



## Fileset

- id: 218ef7a3-95a2-4449-a68d-82860ca957d7
  filename: d5dd00486a.pdf
  content_type: application/pdf
  size: 819130
  md5: 016e5522c9be12b57d89272030a47650

## Thumbnail

fileset_id: 218ef7a3-95a2-4449-a68d-82860ca957d7
filename: d5dd00486a.pdf