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
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
Machine-learned exchange-correlation functionals correct band gaps at near-semilocal cost, while density-functional tight binding reaches the $10^3$-$10^6$-atom regime; combining them assumes that a better parent yields a better parameterization, but we show it does not. Current-generation functionals are orbital-depen...
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
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
