High-Throughput AI-Driven Materials Discovery and Design for High-Rate Batteries: from Computational Screening to Device Performance

Developing materials for high-rate batteries with fast charging/discharging properties while maintaining longevity and safety is a formidable scientific challenge. Conventional approaches struggle to efficiently explore the vast chemical and compositional landscape required. This Research Topic focuses on how emerging, data-driven experimental methodologies—including machine learning for predictive design, high-throughput computational screening, and data-guided experimentation—are revolutionizing this pursuit. We invite contributions that demonstrate how these integrated tools can accelerate the discovery and optimization of critical components, from novel electrodes and solid electrolytes to stable interfaces, for next-generation high-rate energy storage.

The development of high-rate battery materials—essential for fast-charging electric vehicles and grid stabilization—faces a critical bottleneck. The traditional, sequential process of material design, synthesis, and testing is prohibitively slow for exploring the immense design space of chemical compositions, atomic structures, and microstructural morphologies needed to achieve superior ionic conductivity, interfacial stability, and mechanical integrity simultaneously.

Recent advances provide a transformative toolkit. High-throughput computational screening, powered by machine learning interatomic potentials, can now rapidly predict properties across thousands of candidates. Concurrently, the rise of automated synthesis and characterization platforms generates the large, consistent datasets required to train robust AI models that guide material optimization. This convergence of computational and experimental high-throughput methods creates a powerful feedback loop.

This Research Topic aims to harness these integrated, data-driven approaches to fundamentally accelerate the discovery-to-validation pipeline. We seek to showcase how this paradigm shift can target key bottlenecks—from designing fast-ion conductors and robust interphases to engineering porous electrodes—thereby intelligently designing the high-performance materials required for the next generation of energy storage.

This Research Topic focuses on the comprehensive development of next-generation materials for high-rate batteries, spanning from computational acceleration to experimental breakthroughs. We encourage submissions that integrate data-driven methods with innovative synthesis, characterization, and engineering to overcome fundamental material and interfacial challenges. Key themes include:

- Material Discovery & Design: AI-aided prediction, high-throughput screening, and computational modeling of novel electrodes, solid electrolytes, and interface-stabilizing coatings.

- Experimental Innovation: Advanced synthesis routes (e.g., mechanochemical, vapor-phase, ultrafast sintering), novel characterization techniques for dynamic interfaces, and structural engineering of electrodes and electrolytes.

- Multiscale Integration: From atomistic design to microstructure control and device-level integration, with an emphasis on performance validation under high-rate conditions.

- Data & Automation: Autonomous experimentation, robotics-assisted workflows, and shared datasets that close the loop between simulation and synthesis.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Editorial
• FAIR² Data
• Mini Review
• Original Research
• Perspective
• Review

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Editorial
• FAIR² Data
• Mini Review
• Original Research
• Perspective
• Review

Keywords: High-rate batteries, Materials design, High-throughput screening, Electrolyte Optimization, Machine learning

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.