A Nonlinear Multi‐Objective Prediction Strategy for Small‐Sample Datasets in Homogeneous Catalysis.
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| Title: | A Nonlinear Multi‐Objective Prediction Strategy for Small‐Sample Datasets in Homogeneous Catalysis. |
|---|---|
| Authors: | Liu, Yining1 (AUTHOR), Wang, Shen1,2 (AUTHOR), Li, Yang1 (AUTHOR) chyangli@dlut.edu.cn, Bao, Ming1 (AUTHOR) mingbao@dlut.edu.cn |
| Source: | Journal of Computational Chemistry. 5/30/2026, Vol. 47 Issue 14, p1-19. 19p. |
| Subjects: | Homogeneous catalysis, Multi-objective optimization, Chemical models, Catalysts, Machine learning, Nonlinear estimation |
| Abstract: | The development of homogeneous catalytic reactions is hindered by the resource‐intensive nature of traditional experimental and computational methods, particularly when dealing with small, sparse datasets and complex multi‐objective optimization. While machine learning (ML) offers a promising alternative, prevailing methods rely on large‐scale datasets and often fail to address the challenges of small‐sample datasets, high‐dimensional descriptor spaces, and strong nonlinear relationships inherent in catalytic processes. To address this issue, we present a nonlinear multi‐objective ML workflow, PSO_CRP, for predicting reaction categories (e.g., conversion or yield) and quantitative outcomes (e.g., enantioselectivity or site selectivity), as well as the interpretability analysis. Experimental results on 4 small‐sample datasets demonstrate that our models, relying only on simple RDKit‐derived molecular parameters rather than costly DFT calculations, achieve consistently higher predictive accuracy than at least five common ML models. Model‐based permutation feature importance (PFI) and partial dependence plot (PDP) analyses identified the key molecular descriptors governing reaction outcomes. They quantified their contributions, producing results aligned with previous studies while providing added mechanistic insight to enhance model interpretability and guide rational catalyst design. As a high‐precision, low‐cost, and interpretable framework, this PSO‐based workflow offers valuable insights for forward prediction in homogeneous catalytic systems. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Computational Chemistry is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194163594 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Nonlinear Multi‐Objective Prediction Strategy for Small‐Sample Datasets in Homogeneous Catalysis. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liu%2C+Yining%22">Liu, Yining</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Shen%22">Wang, Shen</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Yang%22">Li, Yang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> chyangli@dlut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Bao%2C+Ming%22">Bao, Ming</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mingbao@dlut.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Computational+Chemistry%22">Journal of Computational Chemistry</searchLink>. 5/30/2026, Vol. 47 Issue 14, p1-19. 19p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Homogeneous+catalysis%22">Homogeneous catalysis</searchLink><br /><searchLink fieldCode="DE" term="%22Multi-objective+optimization%22">Multi-objective optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Chemical+models%22">Chemical models</searchLink><br /><searchLink fieldCode="DE" term="%22Catalysts%22">Catalysts</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Nonlinear+estimation%22">Nonlinear estimation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The development of homogeneous catalytic reactions is hindered by the resource‐intensive nature of traditional experimental and computational methods, particularly when dealing with small, sparse datasets and complex multi‐objective optimization. While machine learning (ML) offers a promising alternative, prevailing methods rely on large‐scale datasets and often fail to address the challenges of small‐sample datasets, high‐dimensional descriptor spaces, and strong nonlinear relationships inherent in catalytic processes. To address this issue, we present a nonlinear multi‐objective ML workflow, PSO_CRP, for predicting reaction categories (e.g., conversion or yield) and quantitative outcomes (e.g., enantioselectivity or site selectivity), as well as the interpretability analysis. Experimental results on 4 small‐sample datasets demonstrate that our models, relying only on simple RDKit‐derived molecular parameters rather than costly DFT calculations, achieve consistently higher predictive accuracy than at least five common ML models. Model‐based permutation feature importance (PFI) and partial dependence plot (PDP) analyses identified the key molecular descriptors governing reaction outcomes. They quantified their contributions, producing results aligned with previous studies while providing added mechanistic insight to enhance model interpretability and guide rational catalyst design. As a high‐precision, low‐cost, and interpretable framework, this PSO‐based workflow offers valuable insights for forward prediction in homogeneous catalytic systems. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Computational Chemistry is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/jcc.70407 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 1 Subjects: – SubjectFull: Homogeneous catalysis Type: general – SubjectFull: Multi-objective optimization Type: general – SubjectFull: Chemical models Type: general – SubjectFull: Catalysts Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Nonlinear estimation Type: general Titles: – TitleFull: A Nonlinear Multi‐Objective Prediction Strategy for Small‐Sample Datasets in Homogeneous Catalysis. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liu, Yining – PersonEntity: Name: NameFull: Wang, Shen – PersonEntity: Name: NameFull: Li, Yang – PersonEntity: Name: NameFull: Bao, Ming IsPartOfRelationships: – BibEntity: Dates: – D: 30 M: 05 Text: 5/30/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 01928651 Numbering: – Type: volume Value: 47 – Type: issue Value: 14 Titles: – TitleFull: Journal of Computational Chemistry Type: main |
| ResultId | 1 |
