# Elemental Reactivity Maps for Materials Discovery

https://mdr.nims.go.jp/datasets/6afe32e7-fd5f-493d-b4aa-a88d27fac7ff

## File

- [Manuscript_final.pdf](https://mdr.nims.go.jp/filesets/690cfb65-11dc-4ee0-81b1-3d35037a2cdb/download) ([Detail](https://mdr.nims.go.jp/filesets/690cfb65-11dc-4ee0-81b1-3d35037a2cdb.md))

## Id

6afe32e7-fd5f-493d-b4aa-a88d27fac7ff

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2025-05-30T08:14:27.212960Z

## Updated at

2026-02-21T07:30:08.727536Z

## Published at

2026-02-21T04:38:36.047522Z

## Doi

https://doi.org/10.48505/nims.5510

## First published url

https://doi.org/10.1021/acs.chemmater.4c02259

## Date published

2025-03-25

## Recorded date published

2025-3-25

## Resource type

journal_article

## Manuscript type

accepted_manuscript

## Collection



## Title

- title: Elemental Reactivity Maps for Materials Discovery
  title_type: original
  lang: en

## Description

- description: 'When searching for novel inorganic materials, limiting the combination
    of constituent elements can greatly improve the search efficiency. In this study,
    we used machine learning to predict elemental combinations with high reactivity
    for materials discovery. The essential issue for such prediction is the uncertainty
    of whether the unreported combinations are nonreactive or not just investigated,
    though the reactive combinations can be easily collected as positive data sets
    from the materials databases. To construct the negative data sets, we developed
    a process to select reliable nonreactive combinations by evaluating the similarity
    between unreported and reactive combinations. The machine learning models were
    trained by both data sets, and the prediction results were visualized by two-dimensional
    heatmaps: elemental reactivity maps to identify elemental combinations with high
    reactivity but no reported stable compounds. The maps predicted high reactivity
    (i.e., synthesizability) for the Co–Al–Ge ternary system, and two novel ternary
    compounds were synthesized: Co4Ge3.19Al0.81 and Co2Al1.26Ge1.74.'
  description_type: abstract
  lang: und

## Creator

- name: Yuki Inada
  role: author
  orcid: https://orcid.org/0000-0002-9104-1320
- name: Masaya Fujioka
  role: author
  orcid: https://orcid.org/0000-0002-5829-6591
- name: Haruhiko Morito
  role: author
- name: Tohru Sugahara
  role: author
  orcid: https://orcid.org/0000-0001-5842-5392
- name: Hisanori Yamane
  role: author
  orcid: https://orcid.org/0000-0002-7931-5210
- name: Yukari Katsura
  role: author
  orcid: https://orcid.org/0000-0002-8905-2995

## Contact agent



## Publisher

organization: American Chemical Society (ACS)

## Managing organization



## Keyword

- subject: Crystal structure
  schema: not_defined
- subject: Elements
  schema: not_defined
- subject: Machine learning
  schema: not_defined
- subject: Materials
  schema: not_defined
- subject: Reactivity
  schema: not_defined

## Rights

- description: This document is the Accepted Manuscript version of a Published Work
    that appeared in final form in Chemistry of Materials, copyright © 2025 The Authors.
    Published by American Chemical Society after peer review and technical editing
    by the publisher. To access the final edited and published work see https://doi.org/10.1021/acs.chemmater.4c02259.
  identifier: http://rightsstatements.org/vocab/InC/1.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo

start_date: 2025-02-21
end_date: 2026-02-21

## Journal

- title: Chemistry of Materials
  issn: '08974756'
  volume: '37'
  issue: '6'
  start_page: 2097
  end_page: 2105

## Conference



## Related item



## Funding

- identifier: JPMJCR19J1
  funder_name: Core Research for Evolutional Science and Technology
- identifier: JPMJFS2108
  funder_name: Japan Science and Technology Agency

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## Fileset

- id: 690cfb65-11dc-4ee0-81b1-3d35037a2cdb
  filename: Manuscript_final.pdf
  content_type: application/pdf
  size: 1541654
  md5: 54b962ebdaacd625fd0d743194bd0fb7

## Thumbnail

fileset_id: 690cfb65-11dc-4ee0-81b1-3d35037a2cdb
filename: Manuscript_final.pdf