ArticleNature communications2025
Decoding single-crystal lithium growth through solid electrolyte interphase omics.
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 7 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
7 citing papers in PubMed.
- 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
- Axially coordinated single-atom interface mitigates isolated K toward highly reversible anode-free K metal batteries.Science advances · 2026Article
- Revisiting the Modification Strategies of Alloy-Base Anode for Solid-State Lithium-Ion Batteries Through Deconstructing Anode-Interface-Solid Electrolyte.Nano-micro letters · 2026Review
- Uncovering battery electrochemical mechanisms by artificial intelligence.National science review · 2025Review
- Decoding single-crystal lithium growth through solid electrolyte interphase omics.Nature communications · 2025Article
Corrections and comments
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Authors and funding
18 authors.
Funding
Abstract
Accurately understanding the impact of solid electrolyte interphase (SEI) on lithium deposition is critical for high-energy lithium metal batteries. Yet traditional strategies, focusing solely on isolated components, fail to capture multi-constituent synergies and underlying mechanisms. To address this challenge, we introduce the concept of SEI omics and establish a dataset of cryogenic transmission electron microscopy images combined with co-localized component information. By integrating interpretable machine learning and physics-based feature selection, we decoupled the roles of SEI constituents, revealing that higher N/S/P/F content and reduced O in the SEI improve lithium deposition. Combined density functional theory and electrochemical phase-field modeling uncovered multi-scale effects of SEI components on Li growth. Results confirm that designing an inner SEI layer with high surface energy and migration ability significantly refines deposition morphology. Guided by machine learning-optimized composition, a highly disordered SEI was engineered, achieving high average Coulombic efficiency of 99.35% over 800 cycles for Li||Cu cell at 1 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.