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cs.LG2025
Beyond Atomic Geometry Representations in Materials Science: A Human-in-the-Loop Multimodal Framework
Can Polat, Erchin Serpedin, Mustafa Kurban +1
Most materials science datasets are limited to atomic geometries (e.g., XYZ files), restricting their utility for multimodal learning and comprehensive data-centric analysis. These…
cs.LG2025★ 1 cited
Understanding the Capabilities of Molecular Graph Neural Networks in Materials Science Through Multimodal Learning and Physical Context Encoding
Can Polat, Hasan Kurban, Erchin Serpedin +1
Molecular graph neural networks (GNNs) often focus exclusively on XYZ-based geometric representations and thus overlook valuable chemical context available in public databases like…