§ 01 · Matsi
A datasheet gives you numbers. Matsi uses those numbers to create materials, flipping the script and allowing users to choose from cleaner options that lead to the same properties. A single formula took 18 months to develop and test. Matsi needs 18 minutes.
§ 02 · The reversal
When a CPG brand or a packaging supplier swaps out a legacy petrochemical grade, they start with a performance target, not a recipe. Matsi turns that target into a search with a hard gate on every one of these:
§ 03 · Matched Compatibility Why blending is not a recipe search Incompatible polymers won\u2019t stay mixed. Inside the extruder they separate into micro-phases and the part delaminates. Compatibility belongs to the pair, not to either polymer on its own, which is why you can\u2019t read it off either datasheet.
Blending two polymers doesn't give you the average of their properties. Treat formulation as a search over ratios and you'll get confident suggestions for blends that can't physically exist. That's the trap with machine learning in chemistry, so it's the first thing we check. Candidate ingredient streams go through a thermodynamic compatibility check before any property is predicted. The engine evaluates phase morphology and screens for the compatibilisers that would be required (PP-g-MAH, PLA-g-MAH) to get interface adhesion. A ratio that violates mixing physics is discarded mathematically, before anyone weighs out a gram of resin.
§ 04 · The loop Closed loop Most tools predict once and stop. Matsi runs a cycle: propose candidates, predict their properties, score them against your target, feed the scores back, and propose better ones. It keeps going until the target is met or it runs out of iterations, which is why it can search a space far too large to enumerate.
Key innovation. Hundreds of candidates are generated and evaluated computationally, with properties predicted before any physical testing. That is where the development time and material cost come out.
§ 05 · The search space
A brief never names a recipe, so we have to search every lever that could move the spec. Sustainability and compliance rules cut the pool down before anything gets proposed, so you never see a candidate that could never have qualified anyway.
§ 06 · Our Models
Predictions are only as good as the data used to make them. Our models are calibrated against 36,917 published lab measurements where we ensure property, polymer, and processing are tracked. Top formulation candidates can be moved on to physical validation via compounding and conversion into a final product. Measurements from these specimens join the same dataset, and anchor the next predictions. Our models are improving daily and we track which properties we predict well and the ones we still struggle with, and we’re transparent about it.
§ 07 · What a run returns
Every run gives you a ranked set, best first, and each score comes with the reasoning behind it. There's no single number to take on faith.
Run stf-run-20260528T033824 · figures measured 2026-08-21
Figures from a real run. Calibration is uneven across properties and we say so before a search starts, rather than after.
§ 08 · Capabilities
§ 09 · The result
Match a petrochemical performance spec, on the production assets you already have, without re-tooling the factory. Plenty of tools will hand you a clever answer. What we care about is handing you one you can actually run on Monday.