In this issue, we focus on the combination of techniques such as machine learning, artificial intelligence, robotics and automation for the synthesis of chemicals and materials.

Green ammonia will play an important function in decarbonized energy systems but its production places a high burden on limited renewable resources in land-constrained countries. Here we propose the offshore production of green ammonia, which can increase energy security without land competition.

Cross-coupling reactions are among the most widely used synthetic methods in medicinal chemistry; however, they typically form bonds with C(sp²)-hybridized atoms. The resulting molecules often have suboptimal physicochemical and topological properties. Here virtual and experimental libraries of products from benzylic C(sp³)–H cross-coupling are shown to access underpopulated 3D chemical space.

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.

Stereocontrolled radical polymerizations are elusive, owing to the difficulty of controlling facial addition at a propagating planar, sp² radical chain end. Now, cobalt–porphyrin initiators are reported that enable the preparation of well-defined, highly isotactic polyacrylamides at low Lewis acid loading.

Renewable-energy-driven electrosynthesis of chemical feedstocks is gaining attention as a green alternative to traditional processes. Now, the production of industrially relevant C–S-based compounds from CO₂ and SO₃²⁻, with a simple Cu₂O electrocatalyst, has been demonstrated.

Andrew Cooper, a professor at the University of Liverpool and Academic Director of the Materials Innovation Factory, talks to Nature Synthesis about the use of robotics and artificial intelligence for the synthesis and discovery of materials and chemicals.