ArticleNature communications2023
Machine learning-guided discovery of ionic polymer electrolytes for lithium metal batteries.
Article in Nature communications, 2023. 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 15 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
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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Who cites it
15 citing papers in PubMed.
- Anode-Free Sodium Metal Batteries: From Materials Design to System-Level Integration.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Leveraging Machine Learning for Accelerated Electrode-Electrolyte Interface Design in Rechargeable Li-Based Batteries.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Applications and Advances of Machine Learning in the Development of Solid-State Electrolytes for Lithium-Ion Batteries.ACS omega · 2025Review
- Uniform and High LiSmall (Weinheim an der Bergstrasse, Germany) · 2025Review
- Toward AI ecosystems for electrolyte and interface engineering in solid-state batteries.Science advances · 2025Review
- Uncovering battery electrochemical mechanisms by artificial intelligence.National science review · 2025Review
- Progress and Challenges in Aqueous Batteries: Exploring Polymer Electrolytes for Extreme Temperature Resilience.Small methods · 2025Review
- Neural Synchrony and Consumer Behavior: Predicting Friends' Behavior in Real-World Social Networks.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Article
- Nucleation at solid-liquid interfaces is accompanied by the reconfiguration of electrical double layers.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Progress in Modeling and Applications of Solid Electrolyte Interphase Layers for Lithium Metal Anodes.Nanomaterials (Basel, Switzerland) · 2025Review
- Article
- Data-Driven Materials Research and Development for Functional Coatings.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- Polymer design for solid-state batteries and wearable electronics.Chemical science · 2024Review
- HydrogelFinder: A Foundation Model for Efficient Self-Assembling Peptide Discovery Guided by Non-Peptidal Small Molecules.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As essential components of ionic polymer electrolytes (IPEs), ionic liquids (ILs) with high ionic conductivity and wide electrochemical window are promising candidates to enable safe and high-energy-density lithium metal batteries (LMBs). Here, we describe a machine learning workflow embedded with quantum calculation and graph convolutional neural network to discover potential ILs for IPEs. By selecting subsets of the recommended ILs, combining with a rigid-rod polyelectrolyte and a lithium salt, we develop a series of thin (~50 μm) and robust (>200 MPa) IPE membranes. The Li|IPEs|Li cells exhibit ultrahigh critical-current-density (6 mA cm
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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.