Directed message passing neural network (D-MPNN) with graph edge attention (GEA) for property prediction of biofuel-relevant species Article Swipe
Han Xu
,
Ming Jia
,
Yachao Chang
,
Yaopeng Li
,
Shaohua Wu
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.egyai.2022.100201
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.egyai.2022.100201
Predictive models based on graph neural network (GNN) have attracted increasing interest in quantitative structure-property relation (QSPR) modeling of organic species including biofuel components in recent years. For the task of property prediction of biofuel-relevant species, the present work applies the Directed Message Passing Neural Network (D-MPNN) framework, an emerging type of GNN, and incorporates graph attention on the D-MPNN architecture to improve its capability. modeling using other common machine learning methods is also conducted, confirming the advantage of D-MPNN in comparison. Graph Edge Attention (GEA) is proposed for the D-MPNN layers and shows success in increasing model accuracy after implementation. A relatively sizable subset from the QM9 data and 4 other datasets forming a wide scope of target properties (e.g., thermodynamic properties, ignition properties, surface tension, etc.) are selected for the models. A breakdown analysis of the species distribution of these datasets is conducted for more informed modeling. As the data availability of biofuel species is often a main obstacle for related modeling tasks, this study shows that the performance of D-MPNN with the proposed GEA attention mechanism is most enhanced when using a medium data size of 2000∼5000. Some discussions are made regarding data issues and the use of machine learning methods and graph attention for the predictive modeling of biofuel properties, pointing out the need for more data with better species distribution that is representative of biofuels.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.egyai.2022.100201
- OA Status
- gold
- Cited By
- 30
- References
- 48
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4294969138Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.egyai.2022.100201Digital Object Identifier
- Title
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Directed message passing neural network (D-MPNN) with graph edge attention (GEA) for property prediction of biofuel-relevant speciesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-09-05Full publication date if available
- Authors
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Han Xu, Ming Jia, Yachao Chang, Yaopeng Li, Shaohua WuList of authors in order
- Landing page
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https://doi.org/10.1016/j.egyai.2022.100201Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.egyai.2022.100201Direct OA link when available
- Concepts
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Computer science, Machine learning, Graph, Property (philosophy), Artificial intelligence, Artificial neural network, Biofuel, Obstacle, Enhanced Data Rates for GSM Evolution, Data mining, Theoretical computer science, Ecology, Philosophy, Political science, Biology, Epistemology, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
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30Total citation count in OpenAlex
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2025: 11, 2024: 12, 2023: 7Per-year citation counts (last 5 years)
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48Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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