# Acquiring and transferring comprehensive catalyst knowledge through integrated high-throughput experimentation and automatic feature engineering

https://mdr.nims.go.jp/datasets/d6fdc1d7-a64b-4571-85db-e1f6ad5762e5

## File

- [Acquiring and transferring comprehensive catalyst knowledge through integrated high-throughput experimentation and automatic feature engineering.pdf](https://mdr.nims.go.jp/filesets/8656aa91-059c-4da3-be64-923895afd1e2/download) ([Detail](https://mdr.nims.go.jp/filesets/8656aa91-059c-4da3-be64-923895afd1e2.md))

## Id

d6fdc1d7-a64b-4571-85db-e1f6ad5762e5

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2025-01-23T01:02:53.021847Z

## Updated at

2025-07-16T07:17:04.329643Z

## Published at

2025-01-23T23:30:14.973461Z

## Doi

https://doi.org/10.48505/nims.5291

## First published url

https://doi.org/10.1080/14686996.2025.2454219

## Date published

2025-12-31

## Recorded date published

2025-12-31

## Resource type

journal_article

## Manuscript type

accepted_manuscript

## Collection



## Title

- title: Acquiring and transferring comprehensive catalyst knowledge through integrated
    high-throughput experimentation and automatic feature engineering
  title_type: original
  lang: en

## Description

- description: Solid catalyst development has traditionally relied on trial-and-error
    approaches, limiting the broader application of valuable insights across different
    catalyst families. To overcome this fragmentation, we introduce a framework that
    integrates high-throughput experimentation (HTE) and automatic feature engineering
    (AFE) with active learning to acquire comprehensive catalyst knowledge. The framework
    is demonstrated for oxidative coupling of methane (OCM), where active learning
    is continued until the machine learning model achieves robustness for each of
    the BaO-, CaO-, La2O3-, TiO2-, and ZrO2-supported catalysts, with 333 catalysts
    newly tested. The resulting models are utilized to extract catalyst design rules,
    revealing key synergistic combinations in high-performing catalysts. Moreover,
    we propose a method for transferring knowledge between supports, showing that
    features refined on one support can improve predictions on others. This framework
    advances the understanding of catalyst design and promotes reliable machine learning.
  description_type: abstract
  lang: en

## Creator

- name: Ayu Fujiwara
  role: author
  organization: Japan Advanced Institute of Science and Technology
  department: Graduate School of Advanced Science and Technology
- name: Sunao Nakanowatari
  role: author
  organization: Japan Advanced Institute of Science and Technology
  department: Graduate School of Advanced Science and Technology
- name: Yohei Cho
  role: author
  organization: Japan Advanced Institute of Science and Technology
  department: Graduate School of Advanced Science and Technology
- name: Toshiaki Taniike
  role: author
  organization: Japan Advanced Institute of Science and Technology
  department: Graduate School of Advanced Science and Technology

## Contact agent



## Publisher

organization: Taylor & Francis

## Managing organization



## Keyword

- subject: Catalyst informatics
  schema: not_defined
- subject: machine learning
  schema: not_defined
- subject: high-throughput experimentation
  schema: not_defined
- subject: descriptor
  schema: not_defined
- subject: oxidative coupling of methane
  schema: not_defined

## Rights

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

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Science and Technology of Advanced Materials
  issn: '14686996'
  volume: '26'
  article_number: '2454219'

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## Fileset

- id: 8656aa91-059c-4da3-be64-923895afd1e2
  filename: Acquiring and transferring comprehensive catalyst knowledge through integrated
    high-throughput experimentation and automatic feature engineering.pdf
  content_type: application/pdf
  size: 5294360
  md5: c8804838e1d239cab4d62798e842be38

## Thumbnail

fileset_id: 8656aa91-059c-4da3-be64-923895afd1e2
filename: Acquiring and transferring comprehensive catalyst knowledge through integrated
  high-throughput experimentation and automatic feature engineering.pdf