Practical guides and research on getting more from your materials R&D data. Download and read at your own pace.
Original Polymerize research among materials R&D professionals across Japan: the key challenges in materials development, the state of AI adoption, the barriers, and what successful adoption takes.
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.
