Our objective is to develop a novel methodology for establishing direct relationships between process parameters, structural characteristics, and resulting material properties. This involves creating hybrid data spaces that integrate experimental data, simulation results, and theoretical models.

What are the key data-driven approaches for identifying and quantifying the relationships between process, structure, and properties? How can we effectively integrate multi-modal data sources to improve the accuracy and reliability of our models?

The project seeks to create a comprehensive framework that enables the design of materials with tailored properties. By linking process parameters to final material characteristics, we aim to optimize manufacturing processes for specific applications.

Our goal is to develop predictive models that can accurately forecast material properties based on process conditions and structural attributes. These models will be validated through rigorous experimental testing and used to guide material development efforts.