ArticleACS central science2023
Chemistry-Informed Machine Learning for Polymer Electrolyte Discovery.
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 30 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
30 citing papers in PubMed.
- Decoding Structure-Property Relationships in Anion Exchange Membranes via a Chemically Informed Dual-Channel Graph Attention Network.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Challenges and Opportunities in Machine Learning for Light-Emitting Polymers.Macromolecular rapid communications · 2026Review
- Article
- Digital materials ecosystem: from databases to AI agents for autonomous discovery.Chemical science · 2026Review
- Graph-Based Machine Learning Identifies Oxygenated Block Polymer Replacements for Conventional Plastics and Elastics.Journal of the American Chemical Society · 2026Article
- High-Conductivity Electrolytes Screened Using Fragment- and Composition-Aware Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Cracking the code of multi-layer films to promote circularity in single-use plastic packaging.Nature communications · 2026Review
- Uncovering Structure-Conductivity Relationships in Anion Exchange Membranes (AEMs) Using Interpretable Machine Learning.Membranes · 2025Article
- Uncovering battery electrochemical mechanisms by artificial intelligence.National science review · 2025Review
- Review
- Unlocking Solid Polymer Electrolytes: Advancing Materials through Characterization-Driven Insights.JACS Au · 2025Review
- Article
- Deep learning for property prediction of natural fiber polymer composites.Scientific reports · 2025Article
- PVDF-based solid polymer electrolytes for lithium-ion batteries: strategies in composites, blends, dielectric engineering, and machine learning approaches.RSC advances · 2025Review
- Advancements in thermochemical predictions: a multi-output thermodynamics-informed neural network approach.Journal of cheminformatics · 2025Article
- Lithium-salt-free polyoxovanadates/cellulose acetate-poly(ethylene glycol) nanocomposite solid electrolytes with enhanced electrochemical properties.Scientific reports · 2025Article
- PiNN: Equivariant Neural Network Suite for Modeling Electrochemical Systems.Journal of chemical theory and computation · 2025Article
- CaProtein science : a publication of the Protein Society · 2025Article
- Machine Learning-Aided Inverse Design and Discovery of Novel Polymeric Materials for Membrane Separation.Environmental science & technology · 2025Review
- Liquid Metal Electrocatalyst with Ultralow Pt Loading for Ethanol Oxidation.Small science · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Solid polymer electrolytes (SPEs) have the potential to improve lithium-ion batteries by enhancing safety and enabling higher energy densities. However, SPEs suffer from significantly lower ionic conductivity than liquid and solid ceramic electrolytes, limiting their adoption in functional batteries. To facilitate more rapid discovery of high ionic conductivity SPEs, we developed a chemistry-informed machine learning model that accurately predicts ionic conductivity of SPEs. The model was trained on SPE ionic conductivity data from hundreds of experimental publications. Our chemistry-informed model encodes the Arrhenius equation, which describes temperature activated processes, into the readout layer of a state-of-the-art message passing neural network and has significantly improved accuracy over models that do not encode temperature dependence. Chemically informed readout layers are compatible with deep learning for other property prediction tasks and are especially useful where limited training data are available. Using the trained model, ionic conductivity values were predicted for several thousand candidate SPE formulations, allowing us to identify promising candidate SPEs. We also generated predictions for several different anions in poly(ethylene oxide) and poly(trimethylene carbonate), demonstrating the utility of our model in identifying descriptors for SPE ionic conductivity.
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.