← Back to all news
arXiv Machine Learning By Ali Rayat, Acacia Lee, Gia-Wei Chern

Graph-Recurrent Learning of Emergent Collective Dynamics

Read the original on arXiv Machine Learning →

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.

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 Machine Learning.

Related stories

arXiv Machine Learning
Oct 21

Physics Inspired Neural Networks Enable Efficient Graph Coloring

The paper introduces a physics-inspired neural framework that combines graph neural networks with statistical‑mechanics principles to solve large‑scale graph coloring problems. By using a planting‑based supervised signal, symmetry‑breaking regularization, and iterative noise‑annealed neural dynamics, the model navigates complex solution landscapes and scales quadratically with graph size. It generalizes from small training graphs to instances up to 20 times larger, demonstrating that neural architectures can learn scalable algorithmic strategies effective in hard connectivity regions.

By Lorenzo Colantonio (Department of Physics, Sapienza University of Rome), Andrea Cacioppo (Department of Physics, Sapienza University of Rome), Federico Scarpati (Department of Physics, Sapienza University of Rome), Maria Chiara Angelini (Department of Physics, Sapienza University of Rome), Federico Ricci-Tersenghi (Department of Physics, Sapienza University of Rome), Stefano Giagu (Department of Physics, Sapienza University of Rome)
More like this →
arXiv Machine Learning
Sep 21

Transformers Discover Molecular Structure Without Graph Priors

The paper investigates whether machine learning models can uncover physical patterns in atomistic data without relying on traditional physics-based inductive biases such as geometric locality or graph structures. By training a general-purpose architecture on molecular simulation data, the authors demonstrate that the model autonomously learns interatomic interaction strengths resembling classical electrostatics and identifies interaction cutoffs aligned with established physical models. The study also reports predictable neural scaling behavior and competitive accuracy on certain metrics compared to physics-informed architectures, suggesting that explicit priors may only be necessary when empirically justified.

By Tobias Kreiman, Yutong Bai, Fadi Atieh, Elizabeth Weaver, Eric Qu, Aditi S. Krishnapriyan
llmssafety
More like this →