articleAdvanced MaterialsJan 10, 2026Closed access

AI‐Driven Big Data Frameworks for Electrode–Electrolyte Interphases in Batteries

ABAbdullah Bin FaheemZHZengyu HanDWDongshuang WuHLHaobo Li

Nanyang Technological University

PubMed
Indexed incrossrefpubmed

Abstract

This review presents a comprehensive perspective on how AI and big data strategies can transform the understanding and design of the electrode-electrolyte interphases (EEI) in rechargeable batteries, highlighting their pivotal role in battery performance and longevity. Through uniting high-throughput experimentation and high-throughput computation (HTC), which includes automated cell fabrication, advanced characterization, large-scale HTC screening, and reaction network modeling, diverse datasets can be generated to reveal the mechanistic foundations of interfacial processes. The integration of these datasets with artificial intelligence-orchestrated workflows and machine learning models, such as closed-loop…

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5
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FWCI
31.14
Percentile
100%
References
286
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4

Topics & keywords

Topics
Keywords
  • Workflow
  • Big data
  • Interoperability
  • Battery (electricity)
  • Economic shortage
  • Perspective (graphical)
  • Data integration
  • SPARK (programming language)
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