Bring your data
Upload a file and press Analyze. The result opens in Plot Studio.
Analyze · Simulate · Optimize
Run smarter experiments.
AcquiLAB puts your measurements, physics-based simulations and Bayesian optimization in one workspace. Every result, measured or simulated, helps choose the next experiment.
AnalyzeSix data families, one uploadSimulateReactors to DFTOptimizeBO with simulation in the loop
Find what to try next
Explore suggested conditions based on your results.
Recommended next experiment
pH7.2
Temperature58 °C
Catalyst (mM)0.10
Explore a promising region of the response surface.
Response surface
Yield · pH · Temperature
Experiment map
ObservedSuggested
How AcquiLAB works
From data to next experiment.
Simulate first
Test conditions in a physical model before you spend a sample.
Get the next experiment
Bayesian optimization learns from measured and simulated results.
Measure and repeat
Each new result updates the model and keeps its full record.
Inside the platform
See how AcquiLAB fits your workflow.
Analyze & Plot Studio
From raw file to a publication figure, with every setting recorded.
Simulation New
Reactors, transport, electrochemistry, adsorption and molecular models.
Optimization
Next-experiment suggestions for one objective or several.
Assistant
Questions about your own results, answered from your project.
01 / Bayesian optimization
Make every experiment
inform the next.
Bayesian optimization builds a model from your results, estimates its uncertainty, and recommends where to experiment next, balancing promising conditions against unexplored ones.
Define your goal
Choose what to improve, set parameter ranges, and specify experimental constraints.
Learn from your results
Use completed experiments to model the response and estimate uncertainty.
Choose the next conditions
Review suggested experiments that balance improvement with learning.
Measure. Update. Repeat.
Add new results so the model can refine its next recommendations.
Optimization progress
Observed resultsNext experimentModel uncertainty
1Results
Your experimental
data
2Model
Learn the response
and uncertainty
3Recommendation
Get suggested
next conditions
4New experiment
Add results and
refine the model
You choose which experiments to run.
Illustrative model · synthetic data
One objective
Improve a selected outcome within your experimental limits.
Several objectives
See the trade-offs and choose from the best compromises.
02 / Simulation · new in V2
Simulate before you spend a sample.
Run physical and chemical models in the browser, fit them to your data, and send the results straight into optimization. Every run records the model version, its inputs and its quality checks.
- ReactorsBatch, CSTR, plug flow
- Transport & heatDiffusion, reaction–diffusion, membranes, heat transfer
- ElectrochemistryCottrell, Butler–Volmer, cyclic voltammetry, impedance
- AdsorptionIsotherms, breakthrough columns
- CombustionFlame temperature, ignition delay (Cantera)
- MolecularxTB, DFT (PySCF), water MD (OpenMM)
- Your own modelCustom algebraic or ODE equations
Run complete · QC passed
Illustrative model · synthetic data
Fit a model to your data
Estimate rate constants or adsorption capacity from measured curves, with confidence intervals.
Optimize in simulation
Let Bayesian optimization drive a model. Approve each run, or let the loop run on its own.
Guide lab experiments
Simulated runs join a lab campaign as cheaper evidence. The best result is always a measured one.
03 / Analyze & Plot Studio
From raw file to publication figure.
Upload a file, press Analyze, and the result opens in Plot Studio with the method and every setting recorded.
Plot StudioOne question.
Every part of the process.
Experimental results and model prediction
Explore the response alongside your measured conditions.
Lower yieldHigher yield
| Run | pH | Temp. (°C) | Yield (%) |
|---|---|---|---|
| 1 | 5.3 | 81 | 6 |
| 2 | 5.8 | 66 | 19 |
| 3 | 5.6 | 42 | 27 |
| 4 | 6.2 | 51 | 34 |
| 5 | 6.6 | 80 | 31 |
| 6 | 7.1 | 42 | 28 |
A more informed next experiment.
A model learns from your results and weighs improvement against uncertainty.
Learning the response6 observations
Model estimateUncertaintyObserved
See the signal in your results.
Explore an example voltammogram and inspect its peak response.
Peak current12.8 µAPeak potential0.42 V
A clear oxidation peak above the blank signal.
Give your results a clear voice.
Adjust an example figure, then download it as an editable vector.
Six analysis families.
General, spectroscopy, electrochemistry, materials, imaging, and separation & MS.
Change the figure, not the science.
Fits, baselines and peak areas run on the server and are recorded with the method that produced them.
Ready for the journal.
Export vector PDF, EPS, TIFF or PNG, checked against journal sizes and colour-vision rules.
04 / Assistant
Ask about your own results.
The Assistant works inside your project. It reads your campaigns, observations and analysis results before it answers.
AcquiLABWhy did BO suggest pH 7.2 at 58 °C next?
Your best yield so far is 34 % at pH 6.2 and 51 °C (run 4). The model predicts about 41 ± 6 % at pH 7.2 and 58 °C, and few runs have been measured near there, so that point is both promising and informative.
Example conversation
Grounded in your project.
Answers draw on your campaigns, observations and analysis results.
Explains recommendations.
Ask why a condition was suggested and get the reasoning in plain language.
You stay in control.
The Assistant reads and explains. You decide what to run.
Explore common use cases
Solve real research challenges.
Process optimization
Yield, purity or selectivity across temperature, pH and loading.
Electrochemistry & energy
Fit voltammetry and impedance data, then tune conditions.
Materials & adsorption
Isotherms, breakthrough columns and formulation screens.
Ready to run better experiments?
Start with one dataset. Add simulation and optimization when you need them.