# Computational Single-Atom Catalyst Database Empowers the Machine Learning Assisted Design of High-Performance Catalysts

https://mdr.nims.go.jp/datasets/a9be37d3-514f-4615-a6dc-33eeb899b8f3

## File

- [computational-single-atom-catalyst-database-empowers-the-machine-learning-assisted-design-of-high-performance-catalysts (2).pdf](https://mdr.nims.go.jp/filesets/db36b3b5-985d-42c9-b3e9-94d8b4c32b39/download) ([Detail](https://mdr.nims.go.jp/filesets/db36b3b5-985d-42c9-b3e9-94d8b4c32b39.md))

## Id

a9be37d3-514f-4615-a6dc-33eeb899b8f3

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2025-12-16T01:51:14.925079Z

## Updated at

2025-12-19T07:30:28.447529Z

## Published at

2025-12-19T05:11:49.005742Z

## Doi



## First published url

https://doi.org/10.1021/acs.jpcc.5c00491

## Date published

2025-03-13

## Recorded date published

2025-3-13

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Computational Single-Atom Catalyst Database Empowers the Machine Learning
    Assisted Design of High-Performance Catalysts
  title_type: original
  lang: en

## Description

- description: The data-driven strategy has emerged as an important approach for the
    rapid screening of high-performance single-atom catalysts (SACs). However, the
    lack of a comprehensive SACs database seriously hinders the widespread application
    of this strategy. Herein, we construct a public SACs database comprising 1197
    samples via doping nonmetallic atoms (B, N. O, P, and S) in the coordination environment
    and regulating 3d metal centers (Ti, V, Cr, Mn, Fe, Co, Ni, Cu, and Zn). Based
    on density functional theory calculations, the electronic structural properties
    (i.e., Bader charge and d-band center) and binding energies are obtained. According
    to the binding energy calculations, 657 stable catalyst configurations are identified.
    Subsequently, the corresponding adsorption energies for O2, O, and NO are calculated.
    Moreover, machine learning (ML) models, specifically extreme gradient boosting
    regression
  description_type: abstract
  lang: und

## Creator

- name: Mingye Huang
  role: author
- name: Ruiyang Shi
  role: author
- name: Heng Liu
  role: author
- name: Wenjun Ding
  role: author
- name: Jiahang Fan
  role: author
- name: Binghui Zhou
  role: author
- name: Bo Da
  role: author
  orcid: https://orcid.org/0000-0002-0785-8662
  organization: National Institute for Materials Science
- name: Zhengyang Gao
  role: author
- name: Hao Li
  role: author
- name: Weijie Yang
  role: author

## Contact agent



## Publisher

organization: American Chemical Society (ACS)

## Managing organization



## Keyword

- subject: High-Performance Catalysts
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by-nc-nd/4.0/
  date_licensed: 2025-03-03

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Journal of Physical Chemistry C
  issn: '19327447'
  volume: '129'
  issue: '10'
  start_page: 5043
  end_page: 5053

## Conference



## Related item



## Funding

- identifier: '52006073'
  funder_name: National Natural Science Foundation of China
- identifier: '52176104'
  funder_name: National Natural Science Foundation of China

## 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: db36b3b5-985d-42c9-b3e9-94d8b4c32b39
  filename: computational-single-atom-catalyst-database-empowers-the-machine-learning-assisted-design-of-high-performance-catalysts
    (2).pdf
  content_type: application/pdf
  size: 8325748
  md5: 81e48df7ced3663e951e78db943b1e35

## Thumbnail

fileset_id: db36b3b5-985d-42c9-b3e9-94d8b4c32b39
filename: computational-single-atom-catalyst-database-empowers-the-machine-learning-assisted-design-of-high-performance-catalysts
  (2).pdf