arXiv:2607. 28537v1 Announce Type: cross Abstract: Metallic magnets exhibit complex spin dynamics governed by electronically generated interactions.
By Ali Rayat, Yunhao Fan, Gia-Wei Chern
arXiv:2607. 10285v1 Announce Type: new Abstract: We study how unsupervised autoencoders trained on microscopic spin configurations from the Ising model learn macroscopic, theory-relevant variables underlying the data-generating process.
By Max Weinmann, Miriam Klopotek
arXiv:2609.27306v1 Announce Type: new
Abstract: Conventional score-based diffusion models learn scores without representing normalized densities, whereas tractable normalized models support both samp...
By Kewen Pan, Ying Tang
arXiv:2606. 30773v1 Announce Type: cross Abstract: We introduce a novel technique for scalable sampling of spin-system states with continuous symmetries using diffusion models.
By Sehmimul Hoque, Roger Melko, Pooya Ronagh
The paper presents a neural operator that learns the Kohn–Sham map, directly predicting electron density from the Kohn–Sham potential without orbital diagonalization. Using a domain‑invariant SE(3)‑equivariant Fourier neural operator trained on 8,504 molecules and solids, the model achieves quasi‑linear scaling self‑consistent field (SCF) convergence across diverse systems—including organic molecules, insulators, and metals—while reproducing Kohn–Sham DFT accuracy for densities, spectra, and structural observables. This enables large‑scale simulations, such as magnesium dislocation densities with 82,500 valence electrons, on a single GPU.
By Danish Khan, Maurice D. Hanisch, Nikolai Argatoff, Evan Xie, Sandeep Sharma, Anima Anandkumar
arXiv:2602.09093v2 Announce Type: replace-cross
Abstract: Computational discovery of magnetic materials remains challenging because magnetism arises from the competition between kinetic energy and Co...
By Max Geier, Liang Fu