most citedUnderstanding the Capabilities of Molecular Graph Neural Networks in Materials Science Through Multimodal Learning and Physical Context Encoding

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cond-mat.mtrl-sci2026

The parity gap in crystal tensor prediction

Can Polat, Mustafa Kurban, Erchin Serpedin +1

Crystal symmetry dictates whether a physical response tensor must vanish, establishing a direct test for machine learning predictions independent of property calculations. We deriv…

cond-mat.mtrl-sci2026

How Far Can You Grow? Characterizing the Extrapolation Frontier of Graph Generative Models for Materials Science

Can Polat, Erchin Serpedin, Mustafa Kurban +1

Every generative model for crystalline materials harbors a critical structure size beyond which its outputs become unreliable; we call this the extrapolation frontier. Despite its…

cond-mat.mtrl-sci2026

C2NP: A Benchmark for Learning Scale-Dependent Geometric Invariances in 3D Materials Generation

Can Polat, Erchin Serpedin, Mustafa Kurban +1

Generative models for materials have achieved strong performance on periodic bulk crystals, yet their ability to generalize across scale transitions to finite nanostructures remain…