ArticleNature communications2024
AI-assisted discovery of high-temperature dielectrics for energy storage.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 10 papers.
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
10 citing papers in PubMed.
- Molecular Design of Polymer Dielectrics With Local State Traps for High-Temperature Energy Storage.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Mortise-and-tenon molecular chains enable polymeric hexagonal crystallinity with superior high-temperature capacitor performance.Science advances · 2026Article
- Simultaneous Ultrahigh Energy Density and High Efficiency Achieved in Dipole-Glass Polyimide Dielectrics.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Benchmarking Large Language Models for Polymer Property Predictions.Macromolecular rapid communications · 2026Article
- Active learning in latent spaces enables rapid inverse design of ferroelectric ceramics for energy storage.Nature communications · 2026Article
- Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules.Chemical reviews · 2026Review
- Multifunctional polyimide performance prediction based on explainable machine learning.Smart molecules : open access · 2025Article
- EcoCurrentNet an integrated DNN-CatBoost model for predicting optoelectronic material performance under varying conditions.Scientific reports · 2025Article
- A Learning Framework for Atomic-Level Polymer Structure Generation.Chemistry of materials : a publication of the American Chemical Society · 2025Article
- Decoupling thermal stability and insulation in dielectric polymers via donor-acceptor rearrangement.Nature communications · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
13 authors.
Funding
Abstract
Electrostatic capacitors play a crucial role as energy storage devices in modern electrical systems. Energy density, the figure of merit for electrostatic capacitors, is primarily determined by the choice of dielectric material. Most industry-grade polymer dielectrics are flexible polyolefins or rigid aromatics, possessing high energy density or high thermal stability, but not both. Here, we employ artificial intelligence (AI), established polymer chemistry, and molecular engineering to discover a suite of dielectrics in the polynorbornene and polyimide families. Many of the discovered dielectrics exhibit high thermal stability and high energy density over a broad temperature range. One such dielectric displays an energy density of 8.3 J cc
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.