Matsi / Formulation Intelligence Formulation Intelligence A formulation is a recipe for a material: which polymers, which fillers, in what proportions. Getting one right normally means months of trial-and-error in a lab. Matsi searches that space computationally: it proposes candidate recipes and predicts how each will behave before anyone makes it.
Intelligent discovery and design of material formulations that meet performance, cost, compliance and supply chain resilience constraints simultaneously.
Every number below comes from a file with a date on it. We don't estimate. These numbers 36,917 measurements are structured values pulled from the literature we have processed, not estimates. Of 444 material–property models, 178 are tuned against those measurements; the rest run on published values until a measurement tests them. A single search evaluates candidate recipes and predicts a full property profile for each, plus cost and carbon impact.
Run stf-run-20260528T033824 · figures measured 2026-08-21
§ 02 · Is this you?
You need recycled content without losing performance.
Every trip through recycling shortens the polymer chains, and past a point the material just won't hold the spec any more. What you really want to know is how much recyclate you can carry, and what you'd have to change to carry it.
How the search works →You have a compliance deadline.
EU PPWR, California SB 54, a compostability certification, a food-contact rule. The deadline won't move, so the material has to, and it has to do it on the equipment you already own.
What the constraints look like →You need cost parity with a bio-based alternative.
Matching the petrochemical incumbent on performance is one problem. Beating PLA, PBAT and PHA on cost is a completely different one. Both have to land, or nobody switches.
Bring us the numbers →§ 03 · What a candidate has to clear
The constraint column
The four rows are what we optimise for. The right-hand column turns each one into
something you could actually test. “Performance” is a word;
perf ≥ perf_legacy is a check: the new material must be at least as
strong as the one you use today. Note the two benchmarks differ on purpose: performance
is measured against the oil-based incumbent, cost against the bio-based alternatives.
Four requirements. A formulation has to clear all four, or we don't call it a result.
| № | Pillar | Claim | Constraint |
|---|---|---|---|
| 01 | Performance | Ensure finished product performance delivers on customers needs better than that of legacy product material formulations. | perf ≥ perf_legacy |
| 02 | Profitability | Deliver stronger value proposition on materials costs with 10X the productivity of R&D resources’ conventional approach. | Δcost < 0 vs PLA · PBAT · PHA |
| 03 | Resilience | Navigate market volatility, trade policy implications and supply chain risks and trade-offs with transparency and agility. | risk → bounded |
| 04 | Sustainability | Leverage existing resources, more intelligently designed, to ensure compliance and move towards greater circularity without compromise on functional and business needs. | bio ↑ · compliance = pass |
§ 04 · Specimens · Plate 01–07 Specimen plates Seven everyday plastic objects. For each we show what it is really made of and the rule it has to satisfy: a food-contact regulation, a flammability rating, a compostability standard. These are the constraints a formulation has to clear before it can replace anything. Where we do not yet have a sourced constraint, the plate says so rather than guessing.
Each object is drawn with two inks. Where they cross, a third colour appears that neither ink contained. Formulation works the same way — combine inputs, and you get properties none of them had on their own.