Exploring high-performance viscosity index improver polymers via high-throughput molecular dynamics and explainable AI Article Swipe
Rui Zhou
,
Luyao Bao
,
Weifeng Bu
,
Feng Zhou
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41524-025-01539-z
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41524-025-01539-z
Data-driven material innovation has the potential to revolutionize the traditional Edisonian process and significantly shorten development cycles. However, the scarcity of data in materials science and the poor interpretability of machine learning pose serious obstacles to the adoption of this new paradigm. Here, we propose a pipeline that integrates data production, virtual screening, and theoretical innovation using high-throughput all-atom molecular dynamics (MD) as a data flywheel. Using this pipeline, we explored high-performance viscosity index improver polymers and constructed a dataset of 1166 entries for viscosity index improvers (VII) started from only five types of polymers. Under multi-objective constraints, 366 potential high-viscosity-temperature performance polymers were identified, and six representative polymers were validated through direct MD simulations. Starting from high-dimensional physical features, we conducted an unbiased systematic analysis of the quantitative structure-property relationships for polymers VII, providing an explicit mathematical model with promising application in VII industry. This work demonstrates the advanced capabilities and reliability of the pipeline proposed here in initiating material innovation cycles in data-scarce fields, and the establishment of the VII dataset and models will serve as a critical starting point for the data-driven design of high viscosity-temperature performance polymers.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41524-025-01539-z
- https://www.nature.com/articles/s41524-025-01539-z.pdf
- OA Status
- gold
- Cited By
- 4
- References
- 65
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408076907
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4408076907Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1038/s41524-025-01539-zDigital Object Identifier
- Title
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Exploring high-performance viscosity index improver polymers via high-throughput molecular dynamics and explainable AIWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-03-01Full publication date if available
- Authors
-
Rui Zhou, Luyao Bao, Weifeng Bu, Feng ZhouList of authors in order
- Landing page
-
https://doi.org/10.1038/s41524-025-01539-zPublisher landing page
- PDF URL
-
https://www.nature.com/articles/s41524-025-01539-z.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.nature.com/articles/s41524-025-01539-z.pdfDirect OA link when available
- Concepts
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Throughput, Viscosity, Index (typography), Polymer, Dynamics (music), Computer science, Nanotechnology, Chemistry, Chemical engineering, Materials science, Engineering, Organic chemistry, Physics, Composite material, Telecommunications, World Wide Web, Wireless, AcousticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4Per-year citation counts (last 5 years)
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65Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.MD | 114 |
| abstract_inverted_index.an | 123, 136 |
| abstract_inverted_index.as | 63, 178 |
| abstract_inverted_index.in | 23, 143, 159, 164 |
| abstract_inverted_index.of | 21, 30, 39, 81, 94, 127, 154, 170, 187 |
| abstract_inverted_index.to | 7, 36 |
| abstract_inverted_index.we | 44, 70, 121 |
| abstract_inverted_index.366 | 99 |
| abstract_inverted_index.VII | 144, 172 |
| abstract_inverted_index.and | 13, 26, 54, 77, 106, 152, 167, 174 |
| abstract_inverted_index.for | 84, 132, 183 |
| abstract_inverted_index.has | 4 |
| abstract_inverted_index.new | 41 |
| abstract_inverted_index.six | 107 |
| abstract_inverted_index.the | 5, 9, 19, 27, 37, 128, 149, 155, 168, 171, 184 |
| abstract_inverted_index.(MD) | 62 |
| abstract_inverted_index.1166 | 82 |
| abstract_inverted_index.This | 146 |
| abstract_inverted_index.VII, | 134 |
| abstract_inverted_index.data | 22, 50, 65 |
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| abstract_inverted_index.from | 90, 117 |
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| abstract_inverted_index.this | 40, 68 |
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| abstract_inverted_index.with | 140 |
| abstract_inverted_index.work | 147 |
| abstract_inverted_index.(VII) | 88 |
| abstract_inverted_index.Here, | 43 |
| abstract_inverted_index.Under | 96 |
| abstract_inverted_index.Using | 67 |
| abstract_inverted_index.index | 74, 86 |
| abstract_inverted_index.model | 139 |
| abstract_inverted_index.point | 182 |
| abstract_inverted_index.serve | 177 |
| abstract_inverted_index.types | 93 |
| abstract_inverted_index.using | 57 |
| abstract_inverted_index.cycles | 163 |
| abstract_inverted_index.design | 186 |
| abstract_inverted_index.direct | 113 |
| abstract_inverted_index.models | 175 |
| abstract_inverted_index.cycles. | 17 |
| abstract_inverted_index.dataset | 80, 173 |
| abstract_inverted_index.entries | 83 |
| abstract_inverted_index.fields, | 166 |
| abstract_inverted_index.machine | 31 |
| abstract_inverted_index.process | 12 |
| abstract_inverted_index.propose | 45 |
| abstract_inverted_index.science | 25 |
| abstract_inverted_index.serious | 34 |
| abstract_inverted_index.shorten | 15 |
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| abstract_inverted_index.virtual | 52 |
| abstract_inverted_index.Abstract | 0 |
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| abstract_inverted_index.adoption | 38 |
| abstract_inverted_index.advanced | 150 |
| abstract_inverted_index.all-atom | 59 |
| abstract_inverted_index.analysis | 126 |
| abstract_inverted_index.critical | 180 |
| abstract_inverted_index.dynamics | 61 |
| abstract_inverted_index.explicit | 137 |
| abstract_inverted_index.explored | 71 |
| abstract_inverted_index.improver | 75 |
| abstract_inverted_index.learning | 32 |
| abstract_inverted_index.material | 2, 161 |
| abstract_inverted_index.physical | 119 |
| abstract_inverted_index.pipeline | 47, 156 |
| abstract_inverted_index.polymers | 76, 103, 109, 133 |
| abstract_inverted_index.proposed | 157 |
| abstract_inverted_index.scarcity | 20 |
| abstract_inverted_index.starting | 181 |
| abstract_inverted_index.unbiased | 124 |
| abstract_inverted_index.Edisonian | 11 |
| abstract_inverted_index.conducted | 122 |
| abstract_inverted_index.features, | 120 |
| abstract_inverted_index.flywheel. | 66 |
| abstract_inverted_index.improvers | 87 |
| abstract_inverted_index.industry. | 145 |
| abstract_inverted_index.materials | 24 |
| abstract_inverted_index.molecular | 60 |
| abstract_inverted_index.obstacles | 35 |
| abstract_inverted_index.paradigm. | 42 |
| abstract_inverted_index.pipeline, | 69 |
| abstract_inverted_index.polymers. | 95, 191 |
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| abstract_inverted_index.relationships | 131 |
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| abstract_inverted_index.structure-property | 130 |
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| abstract_inverted_index.high-viscosity-temperature | 101 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.5799999833106995 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.91319481 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |