From reaction idea to optimised conditions,
in four clear steps.
AutoRxn sits between your chemistry and your automation. Define the space, upload your results, and let Bayesian optimisation decide which experiments are worth running next.
- 1Define factors, ranges, and objectives.
- 2Run reactions and upload results.
- 3AutoRxn learns the response surface.
- 4New batch of experiments proposed.
A loop built around real experiments
Each step maps directly onto how chemists already plan and run campaigns— AutoRxn just takes care of the modelling and experiment selection.
Set up your reaction space
Choose your substrates, catalysts, solvents, and ranges for key parameters like temperature, time, and equivalents. AutoRxn builds the design space for you.
- Continuous and categorical factors
- Hard limits, safe ranges, and exclusions
- Multi-objective or single-objective targets
Upload or connect your data
Import reaction tables from ELNs, spreadsheets, or automation platforms. Keep yields, selectivity, and analytical data aligned to each experiment.
- CSV / XLSX imports (structured columns)
- Outlier flagging and data validation
- Support for multi-response outputs
Model the response surface
AutoRxn uses Bayesian optimisation to learn how your factors influence performance, with explicit uncertainty estimates for each region.
- Gaussian process models under the hood
- Uncertainty-aware predictions
- Live diagnostics and model fit
Generate smarter experiments
Select acquisition strategies (exploration vs exploitation) and AutoRxn proposes the next batch of experiments for your reactor or platform.
- Batch proposals matched to plate or reactor formats
- Safety constraints and practical limits respected
- Track improvement over baseline conditions
Under the hood: Bayesian optimisation, exposed just enough.
AutoRxn uses probabilistic models to balance exploration and exploitation. You get sensible defaults out of the box, with the option to tune strategies when you want more control.
Gaussian processes with kernels tailored to chemical design spaces, including categorical factors.
Expected improvement and exploration-heavy strategies for early campaigns, with batch selection support.
Apply lab and safety limits directly in the UI so suggested experiments stay physically realistic.
Ready to run your next optimisation loop in AutoRxn?
Start with a pilot project, or connect your existing reaction tables to see how the modeller behaves on real data.