ArticleACS omega2026
Machine Learning-Based Prediction of Polymer Chemical Resistance to Organic Solvents.
Article in ACS omega, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting the chemical resistance of polymers to organic solvents is a longstanding challenge in materials science, with significant implications for sustainable materials design and industrial applications. In this study, we present an interpretable data-driven framework that extends analysis of polymer chemical resistance beyond conventional solubility models. By systematically analyzing a large data set of polymer-solvent combinations, we observe associations between polymer crystallinity and density, as well as solvent polarity, and resistance outcomes, consistent with established theoretical models. Using a curated data set of 2231 polymer-solvent combinations and gradient boosting models trained on MD-derived descriptors, force-field kernel mean descriptors, and COSMO-RS χ parameters, the model achieved ROC-AUC values of 0.85 and 0.91 within the present data set and validation setting under polymer-level and solvent-cluster-level validation, respectively. These findings provide a foundation for exploratory screening and hypothesis generation for polymer chemical resistance, while acknowledging the scope of the underlying data set.
Identifiers
What OpenQuestion holds
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.