Bayesian Optimization 

Explore and Exploit Reaction Space 

Optimizing a chemical reaction through trial and error isn’t just time-consuming — it’s costly. Every round of experimentation uses valuable materials, and even small adjustments can lead to hours of planning, setup, and analysis without meaningful progress.

ChemAIRS’ Bayesian Optimization tool streamlines this process. It intelligently explores reaction space, helping chemists identify the optimal conditions in fewer steps

What is Bayesian Optimization for Chemical Reactions? 

Traditionally, synthetic chemists develop new reactions through a slow, manual process: searching the literature, guessing promising conditions, and running dozens of experiments just to see what might work.

With thousands of possible combinations of catalysts, ligands, solvents, temperature, and time, this trial-and-error approach often leads to:

  • Weeks or months of optimization

  • High material costs from failed experiments

  • Delayed timelines and slower scale-up decisions

  • A lot of effort with little meaningful progress

Bayesian optimization offers a smarter alternative to trial and error.
It uses machine learning to learn from your past experiments and recommend the most promising next experiment.

This means you reach optimal conditions in fewer rounds, waste less time and material, and make real progress with every experiment you run.

Distinctive Features 

  • Automatic intelligent starting point
    ChemAIRS automatically recommends the initial set of experiments, giving you a strong, data-driven starting point without manual guesswork.

  • Learns from your internal data
    The model can leverage your historical reaction data, learning from past experiments to make smarter recommendations faster.

 

  • Integrated with retrosynthesis
    Bayesian Optimization is fully integrated with the retrosynthesis module, so optimized conditions fit naturally into your synthetic planning workflow.

  • Faster learning, fewer experiments
    By combining smart initialization, internal data, and retrosynthetic context, ChemAIRS reaches optimal conditions in fewer rounds, saving time, materials, and budget.

How Does It Work?

  • 01 - Define Your Reaction Parameters

    ✔ Choose from discrete variables

    Screen any reaction variable, from catalysts and ligands to temperature and time. Include reagent equivalencies to simultaneously optimize catalyst loading, solvent volume, and more.

    ‍ ‍
    ✔Control material costs.

    Include costs of materials to plan for future scale-up budgets.

  • 02 - Define Your Objectives

    ✔ Set optimization goals

    Maximize conversion, yield, or selectivity, or minimize cost and material usage.

    ‍ ‍
    ✔     Combine multiple targets

    Use single or multi-parameter objectives to balance performance, efficiency, and budget.

  • 03 - Run Your Experiments

    ✔ Learn and improve after every round 

    The Bayesian optimization algorithm adapts to the results of each round of experiments, guiding you towards the global optimum. 

    ✔ Enhance with automation 

    Pair your campaign with HTE instrumentation to further accelerate optimization. 

    ✔ Visualize the data 

    Experimental results are plotted after each round.

Integrated Retrosynthesis
with Bayesian Optimization – Only for ChemAIRS®

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