Autonomous Materials Synthesis Laboratories: Integrating Artificial Intelligence with Advanced Robotics for Accelerated Discovery

This comprehensive review examines the evolution of autonomous materials synthesis laboratories that integrate artificial intelligence with advanced robotics to accelerate discovery. Traditional materials development pipelines typically require 10-20 years, but self-driving laboratories (SDLs) and Materials Acceleration Platforms (MAPs) aim to reduce this to 1-2 years through closed-loop systems combining physical experimentation with computational intelligence. The review analyzes critical components including robotic hardware architectures, integrated analytical instrumentation, closed-loop optimization strategies, AI-driven decision-making frameworks, and multi-agent systems for laboratory coordination. Case studies demonstrate remarkable success across various domains: nanomaterials synthesis, inorganic materials exploration, electrocatalyst discovery, and reaction mechanism elucidation. Large language models have recently enhanced these systems by improving knowledge extraction, experimental planning, and multi-agent coordination. The integration of standardized data formats, accessible materials databases, and sophisticated knowledge representation frameworks further accelerates discovery while enhancing reproducibility. This transformative paradigm represents a fundamental reimagining of materials science methodology, shifting from human-guided exploration to AI-orchestrated discovery campaigns.