Global challenges, such as climate change and resource scarcity, are driving the demand for sustainable materials with reduced environmental impact. Developing these materials requires advanced data management across the lifecycle—from research and development to production and quality control. However, current data handling methods often struggle with the complexity of modern materials data, limiting their impact on sustainability and innovation (1). This talk introduces the Material Intelligence Platform (MIP), an AI-supported framework designed to uncover complex relationships within material data and support decision-making across the lifecycle.

Our research question is: How can AI and machine learning efficiently uncover critical correlations in complex material datasets to support sustainable material innovation? The MIP employs clustering algorithms, regression models, and data harmonization techniques to identify correlations between material properties — such as mechanical strength and durability—and their performance in specific applications. By integrating data from sources like laboratory measurements and production parameters, the MIP enables precise, data-driven decisions.

We will present examples from the polymer and paint & coatings industries to illustrate how AI-supported insights from the MIP reveal correlations between material properties and processing conditions, enabling researchers to optimize formulation and process parameters. The MIP also includes centralized data access and visualization tools, connecting R&D and quality control teams to improve workflow efficiency and collaboration.

Observed outcomes from initial studies include reduced time-to-market and improved product quality, aligning with studies that highlight the impact of advanced data management and AI on efficiency and quality (2, 3). This work demonstrates AI’s potential to drive sustainable innovation, addressing the data complexity in materials science and enhancing development and quality practices.