Start with Bayesian optimization
Understand the model, decision rule, and iterative experiment loop.
Read the introductionResources
Resources for better
experimental decisions.
Understand Bayesian optimization, explore scientific workflows, and find help with AcquiLAB.
Open documentationUnderstand the next experiment.
Practical reading on the decisions behind Bayesian optimization.
The optimization loop
Follow the path from experimental setup to results and the next recommendation.
Explore the workflowModels and uncertainty
Learn how Gaussian process surrogates represent a response and its uncertainty.
Read about surrogate modelsChoosing the next experiment
Review Expected Improvement, Upper Confidence Bound, and Probability of Improvement.
Compare acquisition functionsCompeting objectives
Understand Pareto solutions and the trade-offs between scientific outcomes.
Read about MOBOWhen to stop a campaign
Review response plateaus, objective thresholds, and experiment budgets.
Explore stopping criteriaThese guides open the current AcquiLAB V2 documentation.
Explore the scientific workflow.
Product information, research examples, and published updates.
Scientific analysis and Plot Studio
An overview of the analysis layer, figure preparation, and experiment tracking.
Read the product overviewScientific workflows
Connect experiments, analysis, optimization, and reporting in AcquiLAB V2.
Explore workflowsWhich method for which experiment
From noisy assays to parallel reactors: the optimization engine and acquisition function that fit each situation.
Read the guideProduct updates
Read the published timeline of AcquiLAB features, fixes, and improvements.
Read the changelogSupport
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