Publication date: 22nd July 2026
The discovery of sustainable materials requires navigating vast chemical and processing spaces while balancing performance, stability, scalability, and resource constraints. Conventional trial-and-error experimentation is often too slow and data-limited to meet the urgency of challenges in clean energy, sustainable manufacturing, and next-generation electronics. In this talk, I will describe how self-driving laboratories that combine robotics, automated synthesis and characterization, and machine-learning-guided decision-making can transform materials discovery into a data-rich, closed-loop process. I will highlight recent work from our group on autonomous platforms for semiconductor nanomaterials, halide perovskite quantum dots, and catalytic materials, where AI-guided experimentation accelerates optimization, reveals hidden synthesis–structure–property relationships, and reduces waste generation. Emphasis will be placed on how different modalities of self-driving laboratories, including flow-based, batch-based, and multi-robot platforms, can support sustainable materials development by increasing experimental throughput, improving reproducibility, and enabling rapid learning from every experiment. These advances point toward a future in which autonomous laboratories serve as broadly accessible engines for discovering and manufacturing high-performance materials with lower environmental and economic cost.
