Structure Property Relationship

Structure-property relationships (SPR) research aims to understand and predict how a material's structure dictates its properties. Current efforts heavily utilize machine learning, employing graph neural networks, variational autoencoders, and other deep learning architectures to analyze diverse data types, including molecular structures, material microstructures (images), and spectral data, to build predictive models. This field is crucial for accelerating materials discovery and design across various sectors, from pharmaceuticals and cosmetics to advanced materials engineering, by enabling faster and more accurate property prediction than traditional methods. The development of robust and generalizable models, particularly those addressing data scarcity and quality issues, remains a key focus.