The discovery of new battery materials is essential for advancing renewable energy solutions, electric mobility, grid-scale energy storage, and portable electronic devices. Traditional methods of material discovery and characterization are often slow and inadequate to meet the rising demand for high-performance, sustainable batteries. Recent breakthroughs in advanced characterization techniques, combined with artificial intelligence (AI), machine learning, and high-throughput data analysis, offer innovative pathways to revolutionize this process. These interdisciplinary approaches provide deep insights into complex battery materials, enable rapid interpretation of intricate datasets, and accelerate the rational design of next-generation battery technologies. By harnessing these innovations, researchers can address global challenges in sustainable energy and climate change, paving the way for a cleaner, more efficient future.

This Research Topic aims to showcase pioneering efforts at the intersection of advanced characterization and AI-driven data analysis in battery material discovery. It seeks submissions that demonstrate cross-disciplinary advances integrating materials science, electrochemistry, computational intelligence, and data informatics.

Key objectives include:

• Highlighting novel characterization tools and methodologies for probing battery materials across multiple scales.
• Demonstrating AI and machine learning methods that uncover hidden patterns and predict material properties in complex datasets.
• Facilitating the discovery and optimization of advanced materials for battery components, such as cathodes, anodes, and electrolytes.
• Leveraging informatics and data analytics to elucidate structure-property relationships.
• Accelerating materials discovery by integrating high-throughput experimentation with AI-driven analysis.
• Exploring applications to emerging battery technologies, including solid-state, lithium-sulfur, and sodium-ion batteries.
• Promoting open science practices, such as data sharing, reproducibility, and open-source standards.

The Research Topic welcomes original research articles, reviews, case studies, and perspectives on topics including (but not limited to):

• Advanced in-situ and operando characterization techniques for real-time analysis.
• High-throughput experimental platforms and computational screening tools.
• Applications of AI, machine learning, and data mining in battery materials research.
• Development of data analytics and visualization solutions for large-scale datasets.
• Creation and dissemination of open-source databases, tools, or standards.
• Integration of experimental characterization with predictive computational models.
• Development of novel characterization techniques or adaptation for battery materials research.
• Evaluation and benchmarking of innovative methods in real-world battery applications.

Researchers in academia, industry, and government/national laboratories are invited to contribute to advance the frontier of battery material discovery through cutting-edge characterization and data analysis strategies.

Are you interested in contributing a manuscript to this Research Topic?

Revolutionizing Battery Materials Discovery: Advanced Characterization and AI-Driven Data Analysis