Literature Review
Ground setup with constrained scientific context.
- Review prior methods
- Extract useful parameter ranges
- Keep setup research-grounded
Solutions
Bayesian Optimization at the center of scientific iteration
AcquiLAB supports researchers with a constrained scientific LLM and analysis tools so each experiment produces optimization-ready outcomes for the BO engine.
Workflow
Literature→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
Workflow use case: update model after each run, then prioritize the next experiment with highest expected information gain.
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.
Stop when target is reached, gains plateau, or limits are met.
Supported workflows
AcquiLAB supports optimization workflows in electrochemistry, spectroscopy, chromatography, materials experiments, biological assays, and custom tabular experiments. Domain depth varies and continues to evolve.