Chapter 6 : Autonomous Laboratories and AI-Driven Theoretical Chem- istry: Toward Self-Designing Scientific Research

Background: Autonomous laboratories represent a major shift in chemical research by combining artificial intelligence, robotics, and theoretical chemistry to automate experimentation and discovery. Traditional laboratory workflows are often limited by human intervention, reproducibility challenges, and slow exploration of chemical space. Autonomous systems address these constraints by enabling continuous, data-driven experimentation. Objective: This chapter aims to examine how autonomous laboratories integrate AI and theoretical chemistry to enable self-designing and adaptive scientific research. Method: A conceptual and analytical framework is presented, synthesizing developments in laboratory automation, machine learning, and theoretical chemical modeling. The chapter reviews closed-loop experimental workflows, AI optimization strategies, and leading autonomous lab platforms. Key findings: The analysis shows that autonomous laboratories significantly accelerate reaction optimization, materials discovery, and drug development through real-time feedback between theory and experiment. AI methodologies such as Bayesian optimization, reinforcement learning, and active learning enable efficient hypothesis generation and experimental refinement. Key challenges related to hardware integration, data quality, model robustness, and ethical oversight are critically identified. Implications: The findings highlight autonomous laboratories as a foundation for next-generation chemical research, with broad implications for reproducibility, scalability, and the future of self-directed scientific discovery. How to cite: Kodiyatar, N. (2025). Autonomous Laboratories and AI-Driven Theoretical Chemistry: Toward Self-Designing Scientific Research. Zenodo. https://doi.org/10.5281/zenodo.15504367