Machine Learning and Autonomous Systems for Accelerated Synthesis

Autonomous systems integrating machine learning (ML) and laboratory automation are transforming synthetic chemistry by enabling closed-loop experimentation and discovery. In this review, we examine the state-of-the-art in autonomous systems for organic synthesis, with a focus on the components, configurations, and ML algorithms that enable automated reaction planning, execution, and optimization. We survey representative systems that span applications from reaction discovery to molecular optimization, comparing flow and batch configurations and identifying trends in system design. Emphasis is placed on the critical bottlenecks of purification and analytical measurement, particularly structural elucidation of unexpected products-areas that currently constrain autonomous platforms. We describe recent advances in chromatographic method development, structural elucidation from mass spectrometry and nuclear magnetic resonance, and novel ML-based approaches to quantify complex mixtures without calibration. By focusing on enabling technologies in chemical analysis, we identify opportunities for ML and automation to expand beyond domain-specific platforms and accelerate the pace of synthetic discovery.