ArticleACS omega2025
Machine Learning-Enabled Parallel Prediction and Co-Design of Polyimides for Tailored Glass Transition, Dielectric, and Bandgap Properties.
Article in ACS omega, 2025. 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
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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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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Polyimide (PI) is widely used in aerospace, nuclear industries, microelectronics, and flexible devices due to its exceptional thermal stability, electrical insulation, mechanical strength, and low dielectric loss. However, developing high-performance PIs traditionally requires decades of experimental effort. To accelerate discovery, we integrated machine learning (ML) with molecular dynamics (MD) and quantum mechanics (QM) simulations, demonstrating the efficacy of data-driven approaches for novel PI development. We collected 1499 PI structures from relevant literature, with experimentally measured glass transition temperature (
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Registered trials
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