arXiv:2606. 10349v1 Announce Type: cross Abstract: We present a magnetic extension of the Hierarchically Interacting Particle Neural Network (HIP-NN) that enables large-scale simulations of electron-mediated spin dynamics in disordered itinerant magnets.
arXiv:2607. 28537v1 Announce Type: cross Abstract: Metallic magnets exhibit complex spin dynamics governed by electronically generated interactions.
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.
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...
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.
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.
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...
arXiv:2603. 27996v2 Announce Type: replace Abstract: Diffusion models have emerged as a powerful framework for generative tasks in deep learning.
The paper presents an energy‑based reduced‑order model for micromagnetic magnetization dynamics that couples a convolutional autoencoder with a latent neural ODE. The latent dynamics are driven by a learned scalar potential via an antisymmetric operator and a symmetric dissipative operator, ensuring monotonic energy decrease while allowing motion along level sets. The model is trained solely on short trajectory windows without explicit physical labels, and shows that antisymmetric‑dissipative and deep‑quadratic energy formulations yield superior long‑term trajectory predictions compared to purely dissipative models.
arXiv:2606. 22984v2 Announce Type: replace-cross Abstract: Efficient sampling of the Boltzmann distribution in frustrated spin glasses is central to statistical mechanics and combinatorial optimization.
arXiv:2603.27936v3 Announce Type: replace-cross Abstract: Nonlinear Partial Differential Equations (PDEs) are ubiquitous in mathematical physics and engineering. Although Physics-Informed Neural Netw...
arXiv:2606. 14498v1 Announce Type: cross Abstract: Predicting the Kohn-Sham Hamiltonian with machine learning can accelerate density functional theory while retaining access to molecular orbitals, energy levels, and electronic-structure observables that energy-only surrogates cannot resolve.
arXiv:2609.14906v1 Announce Type: cross Abstract: The Hohenberg-Kohn theorem establishes that, in principle, the ground state (GS) charge density contains all GS information of a many-electron system...