The paper proposes treating each atom as an agent that predicts the behavior of its neighbors through collective predictive coding (CPC), a framework borrowed from language emergence. By applying CPC to classical spin systems—such as two-dimensional Ising models, a stripe‑ordered spin model, and the Sherrington–Kirkpatrick spin‑glass—macroscopic order parameters, including replica overlap, arise automatically from observed spin configurations. For the Ising cases, the equilibrium distribution of the learned collective predictive order reproduces a Landau‑type potential, demonstrating that macroscopic order can emerge without reference to microscopic Hamiltonians.
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: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:2609.37392v1 Announce Type: new Abstract: Modeling the temporal evolution of macroscopic properties of complex systems is an important scientific task. To predict this evolution without full mi...
The paper introduces a graph-recurrent neural network (GRNN) that learns emergent collective dynamics directly from trajectory data, without requiring explicit microscopic equations of motion. The GRNN combines gated recurrent memory with graph message passing, allowing local hidden states to capture information beyond instantaneous observables and to propagate spatially. The authors validate the approach on the semiclassical Holstein model, successfully reproducing temporal and spatial correlations and dynamical scaling during domain coarsening of charge-density-wave order.
The paper introduces UniTB, a universal, E(3)-equivariant Hamiltonian framework that blends density‑functional tight‑binding priors with deep learning to predict electronic structures efficiently. UniTB corrects a prior Hamiltonian via a mixture‑of‑experts network and a charge‑equilibration head, ensuring physical downstream properties and suppressing spurious states. Trained on over 29,000 crystals, UniTB achieves sub‑meV accuracy, matches DFT predictions for amorphous Ge‑Sb‑Te, and enables rapid screening of more than 16,000 materials for industrial applications.
arXiv:2608. 18592v1 Announce Type: new Abstract: Whether distinct neural architectures develop common collective dynamics remains an open question.
arXiv:2607. 07127v1 Announce Type: cross Abstract: Lattice field theory is the workhorse of non-perturbative physics, used to simulate phenomena from the strong nuclear force to critical phenomena in materials.
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.
arXiv:2608. 01833v1 Announce Type: cross Abstract: Grokking is a striking phenomenon in neural network training, where a model can undergo a prolonged period of pure memorization before abrupt generalization.
arXiv:2606. 07563v1 Announce Type: cross Abstract: Across machine learning, biology, and physics, independently evolving systems often converge toward strikingly similar high-level structures despite radically different microscopic details.
Simulating microstructure evolution requires quantum-mechanical accuracy and mesoscopic reach in length and time scales, a combination that no current method achieves. Classical phase-field models pro...
arXiv:2608. 16578v1 Announce Type: new Abstract: AI agents increasingly operate as part of interacting systems rather than in isolation.