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Interfacial electrochemical methods focus on the phenomena occurring at the boundary between an electrode and a solution, as opposed to bulk methods that concentrate on the solution's overall properties. These interfacial methods are classified as either static or dynamic based on the presence of a nonzero current in the electrochemical cell and the consistency of analyte concentrations. Static methods, such as potentiometry, measure the cell's potential without any significant current...
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Toward AI ecosystems for electrolyte and interface engineering in solid-state batteries.

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Artificial intelligence (AI) is accelerating innovation in solid-state batteries (SSBs) by optimizing electrolyte and interface design. This review highlights AI

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Area of Science:

  • Materials Science
  • Energy Storage
  • Computational Chemistry

Background:

  • Solid-state batteries (SSBs) offer superior safety and longevity for sustainable energy storage.
  • Developing stable solid electrolytes and interfaces is a critical bottleneck for SSB advancement.

Purpose of the Study:

  • To review the application of artificial intelligence (AI) in engineering solid electrolytes and interfaces for SSBs.
  • To explore AI's potential to overcome key challenges in SSB development, including stability, conductivity, and interface resistance.

Main Methods:

  • Integration of AI-driven materials discovery pipelines.
  • Application of machine learning force fields and generative models for materials design.
  • Analysis of multiscale and multimodal modeling strategies.

Main Results:

  • AI is demonstrating significant potential in screening materials and predicting properties for SSB components.
  • Current AI approaches address stability, conductivity, and interface resistance in solid electrolytes.
  • Generative models show promise for designing novel electrolyte and interface materials.

Conclusions:

  • AI is a transformative tool for accelerating SSB innovation and overcoming design challenges.
  • A roadmap for an intelligent SSB development ecosystem involves multiscale modeling and physics-informed AI.
  • Interdisciplinary collaboration is crucial for advancing sustainable battery technologies through AI.