Data-Driven Approaches in Materials Research: Design, Discovery, Testing, and Applications

Dear Colleagues,

The convergence of machine learning, artificial intelligence, and computational modeling is reshaping the landscape of materials science. From accelerating the discovery of next-generation battery materials to enabling autonomous experimentation in electron microscopy, these technologies are unlocking new levels of precision, scalability, and insight.

This Special Issue invites contributions that explore how data-driven approaches are transforming materials design, characterization, and optimization. Topics include AI-guided multiscale simulations, intelligent manufacturing workflows, and the automation of complex instrumentation such as scanning and transmission electron microscopy. We are particularly interested in research that bridge theory and experiment, leveraging computational models to guide synthesis, and using AI to interpret high-dimensional data from advanced characterization tools.

By bringing together experts in materials science, data science, and instrumentation, this issue aims to highlight the most promising strategies for accelerating innovation in energy storage, quantum materials, and sustainable manufacturing. We welcome original research articles, reviews, and case studies that demonstrate the practical impact of AI and computational methods across the materials lifecycle.

Dr. Anuj Pokle
Guest Editor

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