ArticleACS central science2023
A Model Ensemble Approach Enables Data-Driven Property Prediction for Chemically Deconstructable Thermosets in the Low-Data Regime.
Article in ACS central science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- On-Demand Deconstructable Thermosets Through Cleavable Comonomer and Thermolatent Base Synergy.Angewandte Chemie (International ed. in English) · 2026Article
- Direct metathesis deconstruction to recycle poly(dicyclopentadiene) composites.Science advances · 2026Article
- Article
- Cleavable Strand-Fusing Cross-Linkers as Additives for Chemically Deconstructable Thermosets with Preserved Thermomechanical Properties.Angewandte Chemie (International ed. in English) · 2025Article
- Contextual Analysis of Scientific Publications for Advancing Writing Skills (CASPArS): Self-Learning for Science Writing Using Top Scientists' Literature.Journal of chemical education · 2025Article
- Data-Driven Design of Novel Polymer Excipients for Pharmaceutical Amorphous Solid Dispersions.Bioconjugate chemistry · 2024Article
- Thiol-triggered deconstruction of bifunctional silyl ether terpolymersChemical science · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Thermosets present sustainability challenges that could potentially be addressed through the design of deconstructable variants with tunable properties; however, the combinatorial space of possible thermoset molecular building blocks (e.g., monomers, cross-linkers, and additives) and manufacturing conditions is vast, and predictive knowledge for how combinations of these molecular components translate to bulk thermoset properties is lacking. Data science could overcome these problems, but computational methods are difficult to apply to multicomponent, amorphous, statistical copolymer materials for which little data exist. Here, leveraging a data set with 101 examples, we introduce a closed-loop experimental, machine learning (ML), and virtual screening strategy to enable predictions of the glass transition temperature (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.