ReviewScience advances2025
Toward AI ecosystems for electrolyte and interface engineering in solid-state batteries.
Review in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Leveraging Machine Learning for Accelerated Electrode-Electrolyte Interface Design in Rechargeable Li-Based Batteries.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Solid-state batteries (SSBs) are pivotal for sustainable energy storage, delivering extended life span, low-temperature resilience, and enhanced safety. However, designing stable solid electrolytes and interfaces in SSBs remains a formidable challenge. As a disruptive catalyst for paradigm shifts spanning materials discovery and energy system redesign, artificial intelligence (AI) is unleashing unprecedented possibilities-could it be the key breakthrough for SSB innovation? Here, we critically review the progress of AI applications in electrolyte and interface engineering, covering key aspects such as stability, conductivity, mechanical properties, and interface resistance. This work emphasizes the integration of cutting-edge modeling strategies, including the materials' screening pipelines, machine learning force fields, and generative models. Furthermore, we conduct an in-depth analysis of persistent challenges and propose a roadmap featuring multiscale modeling and multimodal models with physical constraints to build an intelligent ecosystem for SSB development. This review is expected to inspire interdisciplinary collaborations and drive forward energy materials design, ultimately accelerating the development of sustainable and cutting-edge battery technologies.
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.