Ai-designed durable coatings for skin-like wetting on silicone/pdms surfaces
An AI-native materials informatics platform that models and optimizes surface chemistry, coating structure, and topography to give silicone/PDMS materials skin-like wetting behavior. It ranks candidate graft, polymer-brush, and thin-film approaches, predicts durability metrics, and accelerates iteration through design-make-test loops, delivering reusable predictive models and validated prototype surfaces.
Overview
Polymerize’s materials-informatics platform combines domain-specific AI models, centralized experimental data management, and lab-scale prototyping to design durable surface treatments for silicone and PDMS. The goal is to give these elastomers skin-like fluid behavior: low contact-angle hysteresis, minimal fluid pinning, controlled spreading and transfer under contact and shear, and consistent friction in the 0.4–0.6 range—all while surviving repeated use, cleaning, and storage.
The platform addresses a design space that is too large for manual trial-and-error exploration. It systematically maps chemistry, process conditions, curing parameters, and micro/nano-texture effects to the wetting and durability metrics that matter. Each iteration sharpens the underlying structure-process–property models, allowing rapid convergence on robust, reproducible surfaces and coating processes.
This capability is relevant for product categories where silicone is used, but where users also expect the touch and fluid-handling feel of natural skin—including options such as wearable devices, personal care applicators, and other elastomeric interfaces.
Technical specifications
Key components and workflow:
- Design space covers covalent grafts, polymer-brush architectures, thin-film compositions, and hybrid chemistry-plus-texture routes.
- Models link chemistry, process, and topography that contact-angle hysteresis, pinning, spreading, transfer under pressure and shear, and wear resistance.
- Candidate concept and ranking uses the Polymerize One Cortex modeling layer together with Labs software.
- Top candidates are synthesized on silicone coupons in partner laboratories and characterized for wetting, pinning, and transfer, and early aging.
- Acceleration tests (repeated rub/wash/storage aging) generate retention curves and support final surface selection.
- Each design–make–test loop feeds experimental output back into the model, shrinking uncertainty and producing ranking and more reliable candidates in each round.
Program targets:
- Contact-angle hysteresis profile defined for target test fluids.
- Polling and spread behavior while minimizing unwanted transfer.
- Coefficient of friction in the 0.4–0.6 range.
- Durable surface performance over repeated use and cleaning cycles.
- A documented, scalable process and a reusable predictive model for future silicone/PDMS wetting designs.
Technology readiness level
The proposed silicone/PDMS wetting program is at an early development stage, with validation planned as a structured sequence of phases: target definition, in-silico design and candidate ranking, lab-scale prototype synthesis and characterization, and accelerated durability down-selection.
The underlying AI-native platform has been applied in previous materials programs, including paint-cohesion and encapsulated-phase-change-material projects, where it cut prediction error and reduced development time. These earlier results indicate the general methodology is ready to be used for new surface-wetting models. However, the specific silicone/PDMS skin-like-wetting model is just beginning experimental validation.
The stated approach is therefore in the proof-of-concept-to-prototype validation range, and completion of the phased program would advance the technology toward a commercially reproducible surface-treatment process.
About Polymerize
Polymerize is an AI-native platform specifically engineered to transform materials research and development. It operates as a system of intelligence that integrates a unified data foundation, domain-specific AI models, and explainable insights to accelerate the discovery, development, and scaling of new materials. By digitizing experimental workflows, the platform replaces manual, spreadsheet-based processes with a centralized, data-driven backbone. The solution features over 35 domain-guided models trained on polymers, chemicals, and advanced materials, which allow R&D teams to predict material properties, optimize formulations, and gain scientific explanations for AI-driven recommendations.
The platform serves global R&D teams across industries such as automotive, specialty chemicals, coatings, and packaging, helping them reduce failed experiments and shorten go-to-market timelines. By capturing and leveraging institutional knowledge, Polymerize enables organizations to turn years of trial-and-error into targeted innovation, ensuring that data from past experiments informs future research. Customers use the platform to achieve significant ROI through increased discovery speed and streamlined operational forecasting, with security compliance standards like SOC 2 and ISO 27001 integrated to protect sensitive intellectual property. Founded in 2020 and headquartered in Singapore, the company maintains a global presence with offices and strategic collaborations spanning Asia, Europe, and the United States.
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