ArticleNature communications2025
Active learning accelerates electrolyte solvent screening for anode-free lithium metal batteries.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Architecting Anode Current Collectors for Practical Anode-Free Alkali Metal Batteries.Nano-micro letters · 2026Review
- Data-driven interfacial regulations through molecular additive screening for batteries and electrocatalysis.Chemical science · 2026Review
- A Quantitative Electrostatic Potential Descriptor Enables Deep Learning-Accelerated Discovery of High-Performance Lithium-Ion Battery Electrolytes.Angewandte Chemie (International ed. in English) · 2026Article
- Leveraging Machine Learning for Accelerated Electrode-Electrolyte Interface Design in Rechargeable Li-Based Batteries.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Article
- Unlocking the Chemical Space for Rechargeable Batteries with a Generative Solvent Design System.ACS nano · 2026Article
- Carbonyl-Modulated Lowest Unoccupied Molecular Orbital Energy Directs Machine Learning-Assisted Screening of Electrolyte Additives Toward Ultra-Stable Zinc Metal Anodes.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Article
- Anode-Free Cell Concepts: Critical Analysis and Development of Practical Batteries.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Anode-Free Batteries: Pioneering Energy Storage Revolution.Chemical reviews · 2026Review
- Active learning accelerates electrolyte solvent screening for anode-free lithium metal batteries.Nature communications · 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
4 authors.
Funding
Abstract
Anode-free or 'zero-excess' lithium metal batteries offer high energy density compared to current lithium-ion batteries but require electrolyte innovation to extend cycle life. Due to the lack of universal design principles, electrolyte development for anode-free lithium metal batteries is slow and incremental and mainly driven by trial-and-error. Here, we demonstrate the use of active learning as an alternative approach to accelerate electrolyte discovery for anode-free lithium metal batteries. Unlike conventional data-intensive frequentist machine learning techniques, our active learning framework employs sequential Bayesian experimental design with Bayesian model averaging to efficiently identify optimal candidates in typical data-scarce and noisy label settings. Using capacity retention in real Cu||LiFePO
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.