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Data-Driven Reaction Optimization

Combining experimental design with machine learning to accelerate discovery.

mysimulator teamUpdated June 2026≈ 3 min read▶ Open the simulation

Methods

Design of Experiments (DoE) and response surfaces

Bayesian optimization and acquisition functions

Active learning with uncertainty

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Automation

Robotic platforms, ELNs/LIMS, and closed-loop workflows with standardized data.

Examples

Example: Photocatalysis Optimization Loop

Define design space and metrics.

Run BO with automated platform.

Analyze Pareto-optimal conditions.

Frequently asked questions

How to select parameters?

Use domain knowledge and sensitivity analysis.

How many experiments?

Adaptive strategies minimize runs.

Exploration vs exploitation?

Tune acquisition functions and constraints.

Handling noise?

Replicates and robust models.

Constraints?

Safety, cost, and resource limits.

Transfer learning?

Leverage priors and related reactions.

Data formats?

Machine-readable with units and metadata.

Multi-objective?

Pareto fronts and scalarization.

Interpretability?

Feature importance and mechanistic insight.

ROI?

Time saved and improved yields/selectivity.

Try it live

Everything above runs in your browser — open Reaction-Diffusion and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Reaction-Diffusion simulation