Reactwise’s integration with AI4Green’s electronic lab notebook (ELN) creates a direct connection between experimental work in the lab and data-driven optimization. By linking Reactwise with their ELN, the integration enables a unified workflow, allowing ReactWise users to easier leverage historic data for transfer learning methods such as MemoryBO and warm start optimization campaigns.
Reactwise offers full API integrations with external software and hardware platforms, enabling seamless data exchange across the digital laboratory ecosystem. In our integration with AI4Green, this two-way connection allows historical ELN data to support MemoryBO and improve future recommendations, while data generated in Reactwise, including PAT-connected workflows, is sent back to the ELN. This flexible framework reduces manual data handling, improves data continuity, and links optimization directly with laboratory records.
Together, Reactwise and AI4Green provide a powerful foundation for more intelligent and efficient chemical development. AI4Green acts as a central environment for capturing and storing experimental data, while Reactwise uses that data to support optimization and decision-making. When combined with PAT and self-driving laboratory workflows, this integration helps teams make better-informed decisions, build richer experimental data sets, and accelerate research through a more connected and responsive digital lab environment.
AI4Green is dedicated to advancing sustainable chemistry through cutting-edge, data-driven methods, open-source innovation, and the promotion of FAIR data principles, developed by Professor Jonathan Hirst's research group at the University of Nottingham.
Learn more
