Speaker
Who Should Attend？
✔ Seeking practical ways to accelerate formulation development with data and AI
✔ Exploring alternative raw materials due to environmental regulations, supply disruption, or material discontinuation
✔ Struggling with quality variability in recycled materials and its impact on final product performance
✔ Looking for ways to visualize and understand the factors behind performance variation
✔ Interested in predicting material performance for unfamiliar or previously unused raw materials
Director European Business, Polymerize GmbH
Michael Grysczyk
Michael is an industrial engineer with over 8 years of experience in digitalizing the polymer industry. He has worked at both a polymer converter and a material supplier with compounding capabilities. Through this experience, he has gained first-hand insight into industry pain points, including the challenges of increasing recycled content in formulations and the critical aspects of introducing new software solutions from a customer perspective. Today, he helps companies leverage AI to make their R&D processes as efficient as possible.
marketing@polymerize.io
Accelerating Innovation in Sustainable & Alternative Materials - Unlocking the Power of Hidden Data with AI
Turning accumulated materials data into actionable insights—while accelerating development and responding quickly to evolving market needs—has become a key priority in R&D.
While AI-driven development offers significant potential, limited data and a lack of contextual information often make reliable learning difficult.
In this webinar, we will explore the impact Data- and AI-driven R&D can have on materials development—from faster decision-making and improved prediction accuracy to accelerated material discovery.
Building on this, we will highlight how leveraging raw material metadata—such as composition and physicochemical properties—can improve learning accuracy and support the discovery of sustainable and alternative materials.
Key Takeaway
✔ Understand how AI-driven R&D can accelerate materials development — from faster decision-making to smarter material discovery
✔ Learn why limited and sparse data often hinder model accuracy — and how to overcome this challenge
✔ Discover how raw material metadata can enrich learning — improving prediction accuracy and material understanding
✔ Gain practical insight into handling variability — especially in recycled, sustainable, and alternative materials
✔ Explore a pathway from data analysis to optimization — including factor analysis, inverse design, and accelerated formulation development
✔ Identify actionable next steps for applying data and AI in your own R&D workflow
By registering for this webinar, you agree to receive occasional updates from Polymerize related to materials R&D insights, events, and solutions. You may opt out at any time.