Insights, customer stories, and practical guides for teams bringing AI into materials R&D.
A Japanese TPU manufacturer hit precise targets for 10 material properties across two process flows, with over 95% prediction accuracy.
An additive manufacturing company digitised its FDM process data and used tailored ML models to optimise properties 60% faster than conventional methods.
An elastomer-sealing pioneer cut trial-and-error experiments by 50% and improved the mechanical performance of its end products with AI-guided formulation.
Prof. Jin-Hong Mok of Dongguk University uses Polymerize to cut production waste, design customised food textures, accelerate scale-up, and train the next generation of engineers.
Maxell expanded from Materials Informatics to Process Informatics in adhesive tape development, using AI and image analysis to lift product quality and production efficiency.
Prof. Sang-Yong Nam shares how Polymerize's AI-driven inverse prediction helped his team validate data rapidly and reach formulations beyond conventional limits.
Why Samyang Group's R&D center chose Polymerize and how it changed researchers' daily workflows, from data centralization to AI-driven experiment optimization.
Maxell transitioned from internal Python-based MI tools to Polymerize Labs to scale data-driven materials innovation across the organization.
How data- and AI-driven R&D turns accumulated materials data into faster decisions, better predictions, and the discovery of sustainable and alternative materials.
Most materials R&D teams sit on years of experimental data they can't actually use. This guide shows you how to structure, standardize, and activate it, so AI can do what it promises.
High-performance AI models have struggled to explain their predictions. This white paper shares a practical, solution-centric application of Explainable AI for material synthesis using SHAP.
