Topic: AI for Materials: Innovations in Chemical Synthesis

The integration of artificial intelligence (AI) with chemical synthesis is revolutionizing the discovery and development of advanced materials, offering unprecedented opportunities to accelerate reaction design, optimize synthesis pathways, and unlock novel functional compounds. Over the past three years, breakthroughs in machine learning, generative models, and autonomous experimentation have reshaped traditional approaches to chemical synthesis, particularly in catalyst design, reaction condition prediction, and sustainable material fabrication. This Special Issue, AI for Materials: Innovations in Chemical Synthesis, highlights cutting-edge research at the intersection of AI and synthetic chemistry. Topics include AI-guided synthesis route planning, predictive modeling of reaction kinetics, robotic platforms for autonomous material synthesis, and generative adversarial networks (GANs) for molecular/materials discovery. We emphasize applications in energy storage materials, catalytic systems, and green chemistry, addressing challenges such as limited experimental datasets, multi-objective optimization, and interpretability of AI-driven protocols. Submissions showcasing AI's role in reducing trial-and-error experimentation, enhancing scalability, and enabling closed-loop synthesis workflows are particularly encouraged. This issue aims to bridge computational intelligence with practical synthetic innovation, fostering cross-disciplinary collaboration for next-generation smart chemistry.

AI-guided chemical synthesis, autonomous robotic synthesis systems, machine learning for reaction optimization, generative models for molecular design, sustainable synthesis prediction, explainable AI in catalysis, high-throughput synthetic screening