Hyperdimensional Semantic Graph Compression for Predictive Material Informatics

**Abstract:** Current material informatics approaches struggle with the complexity of high-dimensional feature spaces derived from multi-modal material data (chemical compositions, crystal structures, processing parameters). This paper proposes a novel framework, Hyperdimensional Semantic Graph Compression (HSGC), which leverages hyperdimensional computing (HDC) and graph neural networks (GNNs) for efficient encoding and compression of material property relationships within a semantic knowledge graph. HSGC dynamically compresses the graph by learning hierarchical relationships between material features and properties, leading to a significantly reduced computational footprint and improved predictive accuracy compared to traditional GNN-based methods. The core innovation lies in the fusion of HDC's robust pattern recognition with GNNs' ability to represent complex relationships in graph data, resulting in a highly scalable and accurate predictive model for material discovery and design.