Context Review
Capture experimental context and constraints before optimization. Literature automation is not a shipped Product capability.
- Record prior methods and limits
- Capture useful parameter ranges
- Keep setup grounded in known context
Solutions
Bayesian Optimization at the center of scientific iteration
AcquiLAB supports researchers with constrained scientific analysis and assistant tools so each experiment produces optimization-ready outcomes for the BO engine.
Workflow
Context→Protocol→Experiment→Analysis→Bayesian Optimization→Report
Protocol Drafting
Structure experiments before lab execution.
- Draft protocol skeleton
- Define variables and constraints
- Frame objective for BO
Experiment Setup
Prepare lab-ready runs and measurable outcomes.
- Translate plan to run conditions
- Set measurable output targets
- Prepare repeatable run inputs
Result Analysis
Turn raw outputs into structured optimization values.
- Use analyzers and plot tools
- Extract optimization-ready values
- Structure data for model updates
Bayesian Optimization
Central decision engine for next-step recommendations.
- Model response and uncertainty
- Balance exploration vs exploitation
- Recommend next experiment iteratively
Reporting & Visualization
Communicate optimization progress clearly.
- Summarize progress and outcomes
- Generate figure-ready visuals
- Draft report/discussion support
BO highlight
BO remains the central decision engine
Updates the response model after each structured result.
Recommends the next run via exploration/exploitation tradeoff.
Improves recommendations iteratively as evidence grows.
Stops when target is reached, gains plateau, or limits are met.