ReviewChemical reviews2022
Artificial Intelligence Applied to Battery Research: Hype or Reality?
Review in Chemical reviews, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 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
31 citing papers in PubMed.
- Intelligent Computing for Future Layered Cathodes in Sodium-Ion Batteries.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Molten salt electrochemistry for next-generation batteries.Nature reviews. Chemistry · 2026Review
- Unlocking the Chemical Space for Rechargeable Batteries with a Generative Solvent Design System.ACS nano · 2026Article
- Optimize Before You Synthesize-Enhancing the Ionic Conductivity of LiAngewandte Chemie (International ed. in English) · 2026Article
- Lessons From Deep Eutectic Solvents to Design High Entropy Electrolytes for Electrochemical Energy Storage.ChemSusChem · 2026Article
- Uncertainty Quantification forChemical reviews · 2026Review
- Probing Buried Interfaces in Batteries: TowardChemistry of materials : a publication of the American Chemical Society · 2026Review
- How Artificial Intelligence Can Advance Electrochemical Science and Identify Water Molecule Orientation on Platinum Electrodes.The journal of physical chemistry. C, Nanomaterials and interfaces · 2026Review
- Advancing Energy Development with MBene: Chemical Mechanism, AI, and Applications in Energy Storage and Harvesting.Nano-micro letters · 2026Review
- Uncovering battery electrochemical mechanisms by artificial intelligence.National science review · 2025Review
- Low-Temperature Electrolytes for Lithium-Ion Batteries: Current Challenges, Development, and Perspectives.Nano-micro letters · 2025Review
- Semantic Resources for Managing Knowledge in Battery Research.ChemSusChem · 2025Article
- Article
- From Mining to Manufacturing: Scientific Challenges and Opportunities behind Battery Production.Chemical reviews · 2025Review
- Challenges in the Theory and Atomistic Simulation of Metal Electrodeposition.ACS electrochemistry · 2025Review
- Machine Learning Force Fields in Electrochemistry: From Fundamentals to Applications.ACS nano · 2025Review
- Molecular Regulation and Intermolecular Chemistry in Gel Polymer Electrolytes for High-Voltage Lithium Batteries.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Graph-Theory Algorithm for Prediction of Electrolyte Degradation Reactions in Lithium- and Sodium-Ion Batteries.Materials (Basel, Switzerland) · 2025Article
- Transfer learning assessment of small datasets relating manufacturing parameters with electrochemical energy cell component properties.NPJ advanced manufacturing · 2025Article
- Synergizing physics and machine learning for advanced battery management.Communications engineering · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily understandable, review of general interest to the battery community. It addresses the concepts, approaches, tools, outcomes, and challenges of using AI/ML as an accelerator for the design and optimization of the next generation of batteries─a current hot topic. It intends to create both accessibility of these tools to the chemistry and electrochemical energy sciences communities and completeness in terms of the different battery R&D aspects covered.
Indexed as
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.