Comment in 2023

Ammonia synthesis is one of the most important chemical processes as it sustains global food production, but it is a highly polluting and energy-intensive process. Here, the challenges of decarbonizing the process to synthesize green ammonia are discussed.

Automated experiments with integrated characterization techniques greatly accelerate materials synthesis and provide data to be used by machine learning algorithms. We reflect on the current use of data-driven automated experimentation in materials synthesis and consider the future of this approach.

Automation and real-time reaction monitoring have enabled data-rich experimentation, which is critically important in navigating the complexities of chemical synthesis. Linking real-time analysis with machine learning and artificial intelligence tools provides the opportunity to accelerate the identification of optimal reaction conditions and facilitate error-free autonomous synthesis. This Comment provides a viewpoint underscoring the growing significance of data-rich experiments and interdisciplinary approaches in driving future progress in synthetic chemistry.

The use of step count as a metric of synthetic efficiency carries opportunities and challenges. Here, proposals are made to standardize what constitutes a synthetic step and how steps are counted. These proposals may be beneficial in the holistic evaluation of published synthetic routes.