DescriptionLabby
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Granular chemistry data
In one place and one format

One platform to manage, visualize, combine and learn from all your chemistry data, with real-time insights powered by concrete and agentic AI.

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Catalogue data

log results dynamically across domains, quickly and safely.

feedbackExperiment DesignChemical Resolution Worklist Generation Synthesis Execution Data Write-BackTriage (Level 1a)Full Level 1Level 2Data TransformationML FeedbackInput: ML prediction or human hypothesisOutput: Planned experiment record in ELNArtifacts: Experiment, Reactions, Chemical refsInput: Abstract reagent listOutput: Physical bottles with lot numbersArtifacts: Chemical → Physical mapping, inventory checkInput: ELN experiment recordOutput: Robot-readable worklist fileArtifacts: Worklist file, deck layout configInput: Worklist + physical reagentsOutput: Products in barcoded vialsArtifacts: Process Instances, Container (physical)Input: Robot execution logsOutput: Updated Process Data in ELNArtifacts: Process Data (actual conditions)Input: All synthesis productsOutput: Hit / Ambiguous / NegativeArtifacts: Test Instances, triage Sample DataInput: Hits from triageOutput: Complete identity profileArtifacts: Test Instances, Level 1 Sample DataInput: Characterised materialsOutput: Application performance dataArtifacts: Test Instances, Level 2 Sample DataInput: Raw instrument filesOutput: ML-ready feature vectorsArtifacts: Layer 1 → Layer 2 → Layer 3Input: Accumulated datasetOutput: New experiment proposalsArtifacts: Model predictions, design constraints

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0.230.670.420.890.550.710.340.780, 0.31α: 40%β: 35%γ: 25%1, 0.88α: 50%β: 20%γ: 30%2, 0.45α: 30%β: 45%γ: 25%3, 0.62α: 55%β: 25%γ: 20%4, 0.79α: 35%β: 40%γ: 25%5, 0.53α: 45%β: 30%γ: 25%6, 0.67α: 25%β: 50%γ: 25%7, 0.41α: 60%β: 15%γ: 25%

Learn

Across Domains

Analyze

Modes of Failure

Predict

Rates and Results

Visualize Structures

Interactive 3-D crystal structure of a metal–organic framework

AI powered insights, powered by broad domain data

classify results with automatic analysis

deployConditions Feature VecClassifierPhase LabelHistoryTrain SplitModel Fit MetricsReagent ratios (Si/Al, Na/Si, H₂O/Si)Temperature, timeSolvent, modulatorOSDA identityGel composition ratio vectorMorgan fingerprints from OSDA SMILESNormalised numeric featuresRandom Forest / XGBoostTrained on labelled synthesis routes>70% accuracy on framework typePure-phase ZSM-5Mixed phasesAmorphous / Failed synthesisELN synthesis recordsLabelled phase outcomesHistorical experiment archiveStratified split by phase labelCross-validation foldsHeld-out test setHyperparameter tuning (grid/Bayesian)Cross-validated trainingFeature importance ranking (SHAP)Accuracy, precision, recallConfusion matrix per phase classROC-AUC

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