Synergy of Machine Learning and High‐Throughput Experimentation: A Road Toward Autonomous Synthesis

Delve into the transformative synergy where high-throughput experimentation (HTE) produces rich datasets; machine learning (ML) enhances and speeds up discovery; and automated instrumentation are paving the way for self-sustaining research platforms. This review showcases pioneering advances in synthetic chemistry, pharmaceutical innovation, and eco-friendly processes through compelling case studies, offering a glimpse into a future shaped by intelligent, efficient scientific exploration.

Abstract

The integration of machine learning (ML) and high-throughput experimentation (HTE) is rapidly transforming research practices in synthetic chemistry. Traditional trial-and-error methods, historically slow and labour-intensive, are being replaced by automated, predictive workflows that significantly accelerate the optimization of chemical reactions. This review highlights the foundational principles and recent advancements in ML and HTE, while emphasizing automation, parallelization, and miniaturization across different systems and their adaptation in autonomous laboratories. Case studies illustrate successful application of ML and HTE in synthetic chemistry, underscoring the enhanced efficiency, and precision through this synergy. The review concludes by addressing current challenges and future directions, outlining how ongoing developments in automation, robotics, and AI/ML-driven experimentation will shape the future landscape of chemistry research.

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