Thematic issue on adaptive experimentation and optimization in organic chemistry
Artur and Prof. Philippe Schwaller (EPFL) served as guest editors of the thematic issue "Adaptive experimentation and optimization in organic chemistry" in the Beilstein Journal of Organic Chemistry. Their editorial was published on 3 November 2025.
The thematic issue collects recent work on adaptive experimentation, laboratory automation, machine learning and human–AI collaboration in organic chemistry. Topics include closed-loop systems that design and run experiments, active learning for compound selection, high-throughput tools for the optimization of organic synthesis, and machine learning for organocatalysis.
In the editorial, the guest editors argue that the future of the field lies less in fully autonomous systems and more in combining human expertise with machine intelligence.
- Editorial: A. M. Schweidtmann, P. Schwaller (2025). Adaptive experimentation and optimization in organic chemistry. Beilstein Journal of Organic Chemistry, 21, 2367–2368. https://doi.org/10.3762/bjoc.21.180
- Thematic issue: beilstein-journals.org/bjoc/series/131