Journal of Research in Applied Chemistry

Authors: Aniket R. Kulkarni¹, Pooja S. Rawat², Rahul N. Verma³

Abstract: The increasing complexity of chemical systems and the growing demand for rapid discovery of materials, catalysts, and pharmaceuticals have driven the adoption of high-throughput experimentation (HTE) and automation in chemistry. Traditional experimental workflows are often slow, labor-intensive, and limited in their ability to explore large chemical spaces. In contrast, HTE integrates parallel experimentation, robotics, miniaturization, and data-driven analysis to accelerate discovery while improving reproducibility. Automation further enhances experimental efficiency by reducing human intervention, minimizing errors, and enabling continuous operation. This review discusses the fundamental principles of high-throughput experimentation and laboratory automation, key technologies involved, and their applications across catalysis, materials science, medicinal chemistry, and process optimization. The role of data management, machine learning, and closed-loop autonomous laboratories is also highlighted. Challenges related to cost, data quality, and integration remain significant, but ongoing advancements in hardware and software are steadily addressing these issues. Overall, HTE and automation are reshaping modern chemistry, transforming how experiments are designed, executed, and interpreted.

Keywords:High-throughput experimentation; laboratory automation; robotics in chemistry; data-driven chemistry; autonomous laboratories