ArticleJACS Au2025
A Universal Machine Learning Framework Driven by Artificial Intelligence for Ion Battery Cathode Material Design.
Article in JACS Au, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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Who cites it
2 citing papers in PubMed.
- Intelligent Computing for Future Layered Cathodes in Sodium-Ion Batteries.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Reinforcement Learning for Cathode Material Design Through Sequential Decision-Making Frameworks.Nanomaterials (Basel, Switzerland) · 2026Review
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Authors and funding
2 authors.
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Abstract
Graph neural networks for crystal property prediction typically require precise atomic positions and types, limiting their applicability for novel materials with unknown structures. To address this limitation, we introduce BatteryFormer, a versatile machine learning model that employs average interatomic radius distance instead of precise bond lengths as edge embedding, enabling rapid, high-throughput material screening based solely on composition and structural prototypes. BatteryFormer demonstrates robust predictive performance across a wide range of intervals. It accurately predicted high redox potentials for four distinct cathode materials: layered oxides, fluorophosphate salts, vanadium fluorophosphate salts, and ferric pyrophosphate salts. Notably, it also correctly predicted the low redox potential (1.56 V) of the recently reported cathode material Na
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