Most molecular and materials machine-learning models fit correlations across whole molecules or crystals rather than learning the quantum interactions between atomic pairs. Yet bonding, charge redistribution, orbital hybridization, and electronic coupling all emerge from these two-body interactions. We introduce Quantu...
Can Polat, Mustafa Kurban, E. Serpedin et al.· Machine Learning: Science an...· 0 citations
Vision-language and language models increasingly interpret materials data, yet benchmarks report that they hallucinate invalid properties and violate physical law. Evaluation matches final answers to scarce human labels, while discovery agents verify final proposals or density functional theory (DFT) execution. Neither...
Hasan Kurban, Rasul Khanbayov, Mustafa Kurban· 0 citations
The parity gap is derived, a group-theoretic metric quantifying the piezoelectric tensor freedom permitted by a crystal's proper rotation subgroup but eliminated by inversion symmetry in O(3)$, providing a unified framework to distinguish empirical error reduction from exact structural compliance with physical law.
Can Polat, Mustafa Kurban, E. Serpedin et al.· 0 citations
Multimodal evaluations cannot say whether a vision-language model misread an image or misreasoned about it, because every existing method for separating the two places a second model in the loop. We introduce the render ceiling, a model-free reference for benchmarks built by rendering known objects: inverting the froze...
Can Polat, Mustafa Kurban, E. Serpedin et al.· 0 citations
This work shows that deterministic, database-grounded verification catches and repairs errors selectively, and that the binding constraint is detection rather than repair, and that the binding constraint is detection rather than repair.
Can Polat, Mustafa Kurban, E. Serpedin et al.· arXiv.org· 0 citations
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