ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
Integrating Molecular Dynamics and Machine Learning for Solvation-Guided Electrolyte Optimization in Lithium Metal Batteries.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Overview of advances in anode electrode engineering for high-energy-density lithium-sulfur batteries.RSC advances · 2026Review
- Leveraging Machine Learning for Accelerated Electrode-Electrolyte Interface Design in Rechargeable Li-Based Batteries.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Beyond Fluorination: A Golden Criterion Guided by Chemical Coordination-Informed Machine Learning for High-Voltage Electrolyte Design.Angewandte Chemie (International ed. in English) · 2026Article
- Integrating Molecular Dynamics and Machine Learning for Solvation-Guided Electrolyte Optimization in Lithium Metal Batteries.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 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
9 authors.
Funding
Abstract
Optimizing liquid electrolytes is essential for achieving long-term cycling stability and high safety in lithium metal batteries. However, severe side reactions and lithium dendrite formation during repeated cycling lead to low Coulombic efficiency (CE) and limited lifespan. Herein, a rapid and cost-effective strategy that integrates high-throughput molecular dynamics simulations with machine learning predictions is proposed, further validated through experimental evaluation. A mixed electrolyte composed of LiFSI (LiN(SO
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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.