Topic: AI-Driven Materials Laboratories: Toward Autonomous Discovery and Design

The development of advanced materials has long been a cornerstone of technological progress, enabling breakthroughs in energy, healthcare, electronics, transportation, and sustainability. However, traditional approaches to materials discovery and design remain constrained by empirical trial-and-error processes, limited throughput, and lengthy development cycles. In recent years, the convergence of artificial intelligence (AI), robotics, and materials informatics has opened a transformative pathway toward autonomous materials laboratories—systems capable of rapidly and intelligently navigating vast design spaces with minimal human intervention.

This Special Issue, “AI-Driven Materials Laboratories: Toward Autonomous Discovery and Design,” focuses on the emergence of integrated experimental platforms that combine machine learning, high-throughput experimentation, and automated control systems. These platforms represent a paradigm shift: from manual, sequential workflows to closed-loop, data-driven discovery systems, where AI models dynamically guide experimental decisions, adaptively learn from new data, and optimize both material performance and process efficiency.

The issue will encompass a broad spectrum of topics, including but not limited to:

● AI-guided experimental design and optimization;

● Closed-loop systems for autonomous discovery;

● Robotic and automated platforms for materials research;

● Inverse design and generative models;

● Data infrastructure and digital twins for intelligent experimentation.

Autonomous materials laboratories, artificial intelligence (AI), machine learning, high-throughput experimentation, materials informatics, closed-loop discovery, automated synthesis and characterization, data-driven materials design