Polymers & Organic Materials

Description
Predict polymer and organic material performance from composition. MatterSeek screens formulations by property before you mix a batch.

Polymers & Organic Materials — Material System / Predictable Performance / Validation Pathway
AI for Polymers and Organic Materials — Predict Performance Before You Mix a Batch
It predicts material properties from chemical composition, so formulations for polymers, adhesives, coatings, sealants, resins and organic electronic materials can be ranked before a compounding or coating trial is scheduled.
Mechanical strength competes with thermal resistance, processability competes with weatherability, and cost constrains all of it. Because these trade-offs have to be evaluated together, development normally proceeds by iterating a small number of candidate formulations through mixing, sample preparation and mechanical and thermal testing — a loop that is thorough but narrow.
You describe a base composition set and the property window you need, and MatterSeek returns ranked candidates with predicted properties and uncertainty. The platform supports one to four base elements with doping-level precision of 0.02, which matches the granularity at which additive and comonomer studies are actually specified. A prediction round completes in about one minute for two base elements, two minutes for three, and seven minutes for four. Output is delivered as multi-property radar charts for side-by-side formulation comparison, 2D and 3D heat maps for exploring how properties shift across a composition range, and exportable tables.
The prediction engine covers thermal, thermodynamic, mechanical, optical and structural property families, including melting point — the quantity that most directly brackets a polymer's processing window.
Shortlisted formulations enter MatterSeek's 1,200 m² automated laboratory, where sample preparation for FTIR and NMR analysis is automated, and thermal analysis is carried out with simultaneous thermal analysis instrumentation. Electrospinning and thin-film routes are also automated, supporting the fabrication of fibrous and thin organic layers. Prediction accuracy is 95.7% against physical measurement. Material families addressed include high-performance polymers, adhesives and sealants, coatings, thermoplastic elastomers, flame-retardant systems, biodegradable polymers and organic electronic materials.
FAQ
Q: Can MatterSeek predict polymer properties without synthesizing samples?
A: Yes. Properties are predicted from composition, with an uncertainty range reported per value, so candidates can be ranked before any material is made.
Q: Which characterization is available to validate a formulation?
A: Automated sample preparation for FTIR and NMR analysis, plus simultaneous thermal analysis — all inside the autonomous laboratory.
Q: Does the platform handle multi-objective formulation targets?
A: Yes. Multiple property constraints can be defined at once, and results are returned as radar charts and heat maps so trade-offs are visible in one view.
Yuyao (Beijing) Technology Co., Ltd. is a professional company specializing in the intersection of AI and advanced materials. The company has independently developed MatterSeek—a foundational large model tailored to the materials sector—and pioneered the HNN (Hierarchical Neural Network) algorithm, while building a proprietary, controllable materials experiment database.

