Interpretable Machine Learning for Solid-State Batteries.

Area of Science:

• Materials Science
• Electrochemistry
• Computational Science

Background:

• Solid-state batteries (SSBs) offer high energy density and safety for next-generation energy storage.
• Machine learning (ML) accelerates battery material discovery and performance prediction.
• The 'black-box' nature of current ML models limits their interpretability and credibility in battery research.

Purpose of the Study:

• To propose a structured, interpretable ML framework for advancing solid-state battery research.
• To address the limitations of current ML models in understanding battery mechanisms.
• To facilitate the transition from 'black-box' predictions to mechanism-driven design in SSBs.

Main Methods:

• Developing interpretable ML approaches for five key SSB components: solid electrolyte design, material characterization, electrode/electrolyte interface optimization, battery lifetime prediction, and dendrite inhibition.
• Identifying specific requirements and recommending suitable ML methodologies for each component.
• Summarizing current challenges and proposing solutions, including open-source toolchains.

Main Results:

• A comprehensive framework integrating interpretable ML into the entire SSB research pipeline.
• Specific ML strategies tailored for critical aspects of SSB development.
• A roadmap for enhancing the scientific credibility and practical application of ML in battery science.

Conclusions:

• Interpretable ML is crucial for accelerating the development of high-performance solid-state batteries.
• A structured, mechanism-driven approach using interpretable ML will overcome current research bottlenecks.
• The proposed framework and toolchains will foster innovation in energy storage technologies.