Atoms predict each other to create macroscopic order in physics
Atom as an Agent: Emergent Order by Collective Predictive Coding
Artificial Intelligence
Summary
This paper explains how large-scale patterns in physical systems emerge from how atoms interact with each other. The authors treat each atom like an individual agent that predicts what nearby atoms will do, similar to how people communicate and learn language. By applying this idea to different spin models in physics, the authors show that big patterns—called order parameters—can appear naturally from just observing the atoms’ interactions. This approach does not need prior knowledge of the system's microscopic rules, making it a data-driven way to understand complex physical behavior.
What this means in practice
- •For materials scientists: Identify emergent large-scale properties in complex materials by analyzing atomic-level data without assuming microscopic physical laws.
- •For machine learning engineers: Develop algorithms that infer collective behaviors in multi-agent systems by modeling agents as predictors of each other’s actions.
Tested on simulated data.
Authors
Koji Hashimoto, Tadahiro Taniguchi
Abstract
What is the intrinsic reason of emergence of macroscopic order parameter out of microscopic systems? In this article, we apply to physics the collective predictive coding (CPC), the theory of language/symbol emergence. As our language emerges out of our human communications, physical macroscopic order parameter emerges out of communications among atoms --- each atom is regarded as an agent who predicts each other's behavior through inference. We present three examples of classical spin systems: two-dimensional ferromagnetic and antiferromagnetic Ising models, a two-dimensional spin model with competing stripe orders, and the fully connected Sherrington--Kirkpatrick spin-glass model. In all of these examples, proper macroscopic order parameters including the replica overlap emerge automatically through communication informatics of CPC, only from observed spin configurations. We call the learned macroscopic coordinate the collective predictive order. Furthermore, for the ferromagnetic and antiferromagnetic Ising models, the equilibrium distribution of the collective predictive order yields an emergent potential with the symmetry and thermal evolution characteristic of a Landau potential. Notably, the emergence of the collective predictive order does not refer to any structure of microscopic Hamiltonians/Lagrangians. Thus our ``Atom-as-an-Agent" (AaaA) principle offers a completely data-driven method for the emergence of macroscopic order, that is, machine learning physics.