Lee, Heeseung Yoo, Hyuk Jun Jang, Hye Su Park, Byeongho Park, Yang Jeong Han, Sang Soo 2026-03-27T01:00:25Z 2026-03-27T01:00:25Z 2026-03-24 2026-03 2051-6347 https://pubs.kist.re.kr/handle/201004/154472 The convergence of laboratory automation, artificial intelligence (AI), and data-driven science has catalyzed the emergence of self-driving laboratories (SDLs), autonomous platforms capable of designing, executing, and analyzing experiments with minimal human input. While early SDLs (SDL 1.0) demonstrated the feasibility of closed-loop discovery, their impact has been constrained by limited scope, poor interoperability, and reliance on human-curated heuristics. This review outlines the vision of SDL 2.0: a new generation of flexible, scalable, and collaborative discovery engines for chemistry and materials science. We discuss recent advances in modular hardware design, AI-driven decision-making including Bayesian optimization, computer vision, and large language models, and orchestration software that integrate scheduling, data management, and safety protocols. Building on these foundations, we propose six defining characteristics for SDL 2.0: interoperable, collaborative, generalizable, orchestrated, safe, and creative. Together, these features establish SDLs as globally networked platforms, enabling reproducible experimentation, accelerated innovation, and democratized access to advanced research infrastructure. By embedding modularity, AI reasoning, and community-driven standards into their core, SDLs 2.0 promise to transform not only how experiments are conducted, but also who can participate in and benefit from the accelerating pace of scientific discovery. English Royal Society of Chemistry Toward self-driving laboratory 2.0 for chemistry and materials discovery Article 10.1039/d5mh01984b 1 Materials Horizons Materials Horizons N scie scopus 2-s2.0-105032220297 Chemistry, Multidisciplinary Materials Science, Multidisciplinary Chemistry Materials Science Review; Early Access BAYESIAN OPTIMIZATION ROBOT CHALLENGES PLATFORM