← Back to all news
arXiv AI By Koji Hashimoto, Tadahiro Taniguchi

Atom as an Agent: Emergent Order by Collective Predictive Coding

Read the original on arXiv AI →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv Machine Learning
Oct 23

Graph-Recurrent Learning of Emergent Collective Dynamics

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.

By Ali Rayat, Acacia Lee, Gia-Wei Chern
More like this →
arXiv AI
Oct 28

Physical structure as an organizing principle for universal Hamiltonian learning

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.

By Mingkang Liu, Zhanghao Zhouyin, Guanghui Wang, Daye Zheng, Lei Shen, Qiangqiang Gu
More like this →