AI finds your best conditions — physics proves they'll scale.
ScaleChem pairs a self-tuning optimizer that learns from your experiments with a first-principles scale-up engine that proves the winning conditions survive the plant — every step backed by real chemistry data.
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Optimization tools guess in the lab. Scale-up calculators sit in a spreadsheet. ScaleChem is the one platform that does both — and connects them.
A self-tuning Gaussian-process optimizer figures out which variables matter for your chemistry — no hyperparameters to set. Paste your existing data and it recommends the most informative next batch, reaching the optimum in under 5% of a typical 1,000-cell design. Sherpa, the built-in AI advisor, draws on 1.4M classified reactions and a literature corpus to explain the why.
Found your conditions? The scale-up engine tells you whether they survive the plant — from first principles, not black-box ML. Reaction enthalpy and Stoessel thermal classification, Reynolds / Power / Damköhler similarity, yield loss by mechanism, equipment sizing, safety thresholds, and cost per kg. Real transport phenomena, with honest uncertainty.
An optimizer that finds great flask conditions but can't tell you they'll fail at 2,000 L isn't finished. ScaleChem carries your winning chemistry through heat transfer, mixing, yield, equipment, and safety — so "optimal" means optimal at scale, not just in a beaker. The physics keeps the AI honest.
On a 1,000-experiment design space (5 catalysts × 5 solvents × 10 temperatures × 4 pressures), the optimizer reaches 99% of the true optimum after exploring just a few percent of candidates.
Synthetic 1,000-candidate benchmark, batch size 5, identical seed and truth function. "First-generation BO" means a fixed-hyperparameter Gaussian-process optimizer with naive batch selection — representative of legacy lab-automation tools. Modern optimizers (e.g. EDBO+) perform comparably to ScaleChem on this kind of space; the 12× gap is against first-generation BO, not state-of-the-art. Real-world performance varies with reaction-surface complexity.
Each tool feeds the next. Once you know it scales, works, and is safe — you find out what it costs.
Sherpa rides along in Reactions and the Optimizer. Ask which variables to screen, what a compound's boiling point or GHS hazards are, or why a step is risky — and it answers from your live reaction context plus a curated corpus of chemistry literature and 1.4M classified reactions. It pulls real compound properties straight from the data, cites what it draws on, and tells you when it isn't sure. No hand-waving.
Reactions & PFD and the Bayesian optimizer in one product — standalone or integrated. Sketch the synthesis, flag the steps that need work, and let the optimizer pick them up with full context.
Heat transfer, dimensionless scaling, yield, equipment, process safety, and techno-economics — the full physics-guided path from a working lab route to a costed, de-risked plant process.
See the demo, then tell us what you're working on. No signup required to explore — and every engagement starts with a scoping conversation so your first campaign is set up right.
Have a scale-up question? Email us — we read every message.