High-throughput screening and literature data-driven machine learning-assisted investigation of multi-component La2O3-based catalysts for the oxidative coupling of methane. Issue 9 (22nd February 2022)
- Record Type:
- Journal Article
- Title:
- High-throughput screening and literature data-driven machine learning-assisted investigation of multi-component La2O3-based catalysts for the oxidative coupling of methane. Issue 9 (22nd February 2022)
- Main Title:
- High-throughput screening and literature data-driven machine learning-assisted investigation of multi-component La2O3-based catalysts for the oxidative coupling of methane
- Authors:
- Nishimura, Shun
Le, Son Dinh
Miyazato, Itsuki
Fujima, Jun
Taniike, Toshiaki
Ohyama, Junya
Takahashi, Keisuke - Abstract:
- Abstract : Unique 30 types of multi-component La2 O3 -based catalysts for oxidative coupling of methane were discovered in 75 types of selected catalysts based on high-throughput screening and literature datasets with multi-output machine learning approaches. Abstract : Herein, multi-component La2 O3 -based catalysts for the oxidative coupling of methane (OCM) were designed based on high-throughput screening (HTS) and literature datasets with multi-output machine learning (ML) approaches including random forest regression (RFR), support vector regression (SVR), Gaussian process regression (Bayesian), and itemset mining (LCM). The combined use of HTS data and SVR successively assisted the search for 11 types of multi-component La2 O3 -based OCM catalysts in 20 validations with C2 yields appearing at 450 °C based on indirect ML assistance. The appropriate multi-component predicted from ML contributed to the determination of a characteristic feature of the lower onset temperature for an La2 O3 -based OCM catalyst. The LCM application on the SVR extended HTS data area supported the observation of the effective elements in the HTS area. However, a challenging subject remains, i.e., 2 types of multi-component La2 O3 -based catalysts afforded an effective C2 yield (>5.0%) at 450 °C, as inferred from the 20 selected types of catalyst validation. Thus, to predict unique multi-component La2 O3 -based OCM catalysts further, a combination of HTS and literature data was applied for fourAbstract : Unique 30 types of multi-component La2 O3 -based catalysts for oxidative coupling of methane were discovered in 75 types of selected catalysts based on high-throughput screening and literature datasets with multi-output machine learning approaches. Abstract : Herein, multi-component La2 O3 -based catalysts for the oxidative coupling of methane (OCM) were designed based on high-throughput screening (HTS) and literature datasets with multi-output machine learning (ML) approaches including random forest regression (RFR), support vector regression (SVR), Gaussian process regression (Bayesian), and itemset mining (LCM). The combined use of HTS data and SVR successively assisted the search for 11 types of multi-component La2 O3 -based OCM catalysts in 20 validations with C2 yields appearing at 450 °C based on indirect ML assistance. The appropriate multi-component predicted from ML contributed to the determination of a characteristic feature of the lower onset temperature for an La2 O3 -based OCM catalyst. The LCM application on the SVR extended HTS data area supported the observation of the effective elements in the HTS area. However, a challenging subject remains, i.e., 2 types of multi-component La2 O3 -based catalysts afforded an effective C2 yield (>5.0%) at 450 °C, as inferred from the 20 selected types of catalyst validation. Thus, to predict unique multi-component La2 O3 -based OCM catalysts further, a combination of HTS and literature data was applied for four ML approaches. This was helpful to discover 17 additional combinations of multi-component La2 O3 -based catalysts affording effective C2 yields (>5.0%) at 450 °C in the 38 selected types of predictions. In total, 30 new types of multi-component La2 O3 -based catalysts with a C2 yield greater than 5.0% at 450 °C in CH4 /O2 = 2.0 condition were found based on the indirect ML assistance driven by HTS and literature data. … (more)
- Is Part Of:
- Catalysis science & technology. Volume 12:Issue 9(2022)
- Journal:
- Catalysis science & technology
- Issue:
- Volume 12:Issue 9(2022)
- Issue Display:
- Volume 12, Issue 9 (2022)
- Year:
- 2022
- Volume:
- 12
- Issue:
- 9
- Issue Sort Value:
- 2022-0012-0009-0000
- Page Start:
- 2766
- Page End:
- 2774
- Publication Date:
- 2022-02-22
- Subjects:
- Catalysis -- Periodicals
541.395 - Journal URLs:
- http://pubs.rsc.org/en/Journals/JournalIssues/CY ↗
http://www.rsc.org/ ↗ - DOI:
- 10.1039/d1cy02206g ↗
- Languages:
- English
- ISSNs:
- 2044-4753
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3090.943100
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 21598.xml