ArticleJournal of chemical information and modeling2024
Design of Recyclable Plastics with Machine Learning and Genetic Algorithm.
Article in Journal of chemical information and modeling, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
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Who cites it
4 citing papers in PubMed.
- Benchmarking Large Language Models for Polymer Property Predictions.Macromolecular rapid communications · 2026Article
- AI-Based Polymer Classification Using Ensemble Deep Learning and Heuristic Optimization: Implications for Recycling Applications.Polymers · 2026Article
- Article
- Polysulfamates as "Macroisosteres" of Polyurethanes with Improved Degradability.Angewandte Chemie (International ed. in English) · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present an artificial intelligence-guided approach to design durable and chemically recyclable ring-opening polymerization (ROP) class polymers. This approach employs a genetic algorithm (GA) that designs new monomers and then utilizes virtual forward synthesis (VFS) to generate almost a million ROP polymers. Machine learning models to predict thermal, thermodynamic, and mechanical properties─crucial for application-specific performance and recyclability─are used to guide the GA toward optimal polymers. We present potential substitute polymers for polystyrene (PS) that achieve all property targets with low estimated synthetic complexity.
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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.