Built for the way materials & chemistry R&D actually happens
Recurring workflows across catalysis, formulation, battery, polymer and alloy R&D — each one mapped to the exact modules that solve it. If your work looks like one of these, the platform was built for it.
Formulation R&D
Developing a new formulation
The problem: You have 40–120 experimental runs from a screening campaign and a promising-but-incomplete picture of what composition wins.
Suggested workflow
Data SynthesisAugment the sparse campaign data into a larger, statistically faithful training set.
EvaluationConfirm the synthetic rows match reality before modeling on them.
PredictionTrain the explainable ensemble; read which descriptors drive activity and stability.
OptimizationSearch compositions under a 100%-sum constraint; rank candidates by TOPSIS.
TEAPrice the top candidates — materials, equipment, CapEx/OpEx — before committing lab time.
The outcome: A ranked portfolio of novel formulations with predicted performance, uncertainty bands and estimated cost per batch.
★ Pareto Candidate Discovery100% Sum Constrained
Top Candidate: NMC-811 CathodeTOPSIS 0.941
Sustainable Energy & Storage
Sustainable energy & storage materials
The problem: Electrolytes and electrode materials face harsh operating conditions — narrow voltage stability windows, cycle-life degradation and raw-material cost pressure — and the composition × condition design space is far too large to sweep by hand.
Suggested workflow
Data SynthesisAugment sparse experimental runs into a larger, statistically faithful training set.
EvaluationConfirm the synthetic rows match reality before modeling on them.
Virtual ScreeningRank the candidate library by structural similarity to reference electrolyte solvents and additives.
PredictionModel capacity retention and ionic conductivity with conformal prediction bands and extrapolation alerts.
OptimizationPareto balance between energy density, cycle life, and material cost.
Active learningPropose the optimal next bench batch to maximize surrogate model information gain.
The outcome: Optimized electrolyte and electrode compositions with high predicted capacity retention at minimized cost and material loading.
Energy MaterialΔE = 3.1 eV
Cycle Stability: High165 mAh/g
Process Chemistry
Finding the right reaction conditions
The problem: You know the chemical system; what varies is temperature, pressure, flow rate, concentration — a large condition space you cannot sweep exhaustively.
Suggested workflow
Design of Experiments (DoE)Generate a Latin Hypercube or Box–Behnken plan that maximizes information per experiment.
Data SynthesisAugment the completed runs into a larger, statistically faithful training set.
EvaluationConfirm the synthetic rows match reality before modeling on them.
PredictionBuild the ensemble from the resulting runs, with conformal intervals for every prediction.
OptimizationMaximize yield and selectivity while constraining conditions to safe equipment limits.
Visualizations / Dossier ReportsVisualize the response surface and share the recommendation with the process team.
The outcome: A shortlist of condition windows worth testing, each with a predicted yield and an honest uncertainty range.
◈ DoE Space DesignLatin Hypercube
Optimal Window: 620°C, 3.2 barResponse 92.4%
Scale-Up Economics
Cost-aware scale-up decisions
The problem: A candidate looks great at the bench, but nobody knows what it costs at pilot scale — or whether the economics kill it.
Suggested workflow
Data SynthesisAugment the bench-scale dataset into a larger, statistically faithful training set.
EvaluationConfirm the synthetic rows match reality before modeling on them.
PredictionTrain the ensemble to estimate performance with honest uncertainty bands.
TEAPrice raw materials, size the equipment with six-tenths power-law rules, and fold in CapEx/OpEx and end-of-life credit.
OptimizationRe-run the search with net value as an objective alongside activity and stability.
PipelineChain the whole loop so the cost model re-evaluates every new candidate automatically.
The outcome: Candidates ranked by the number that actually matters — net value — instead of activity alone.
TEA Scale-UpScale-Up Rule
Net Unit Batch Cost:$2,190 / kg product
Data Preparation
Turning the literature into a dataset
The problem: Your best data is buried in published tables, supplementary files and old lab notebooks — and retyping it is error-prone.
Suggested workflow
Data EnrichmentPull tabular compositions and formulations out of literature PDFs and old notebooks, then compute composition descriptors and structure-derived features for every extracted row.
Data SynthesisMerge literature data with your own runs and augment the combined set.
EvaluationConfirm the synthetic rows match reality before modeling on them.
PredictionTrain on the enriched corpus — your model finally has enough support for extrapolation to be rare.
OptimizationSearch the enriched model for high-performing candidates worth prioritizing in the lab.
The outcome: A clean, enriched, citation-backed dataset that turns years of literature into a model you can query.
❐ Literature ExtractionPDF to Structured Rows
PDF Table
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132 Descriptors
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Training Dataset
Extraction Accuracy:98.2% Verified
Lab Integration
Closing the bench-to-model loop
The problem: Your lab notebook and your modeling live in different worlds; results never flow back into the models.
Suggested workflow
Bench LoggerRecord runs and recipes directly in the platform as they happen, tagged to the project.
Data SynthesisStructured recipes feed the Data Synthesis engine so augmentation reflects your real experimental design.
EvaluationConfirm the synthetic rows match reality before modeling on them.
PredictionRebuild the explainable ensemble from the merged bench record.
OptimizationRe-run the search so the next best candidates reflect the latest bench results.
Active learningEach new bench result updates the surrogate and the next-batch proposal.
Dossier ReportsKeep the whole team on one thread — every run, plot and decision in one project space.
The outcome: An unbroken data loop from bench to model and back, with the project history permanently reproducible.
▲ Active Learning LoopBench to Model
Iteration Speed:Next Batch in 2 min
Not sure which workflow fits?
Describe your project to the AI Copilot, or read the docs — and if you are still stuck, our team will help you map your data to the right modules.