Graph Attention Hamiltonian Neural Networks: A Lattice System Analysis Model Based on Structural Learning

TL;DR

This paper introduces GAHN, a neural network that learns the structure and interactions within lattice Hamiltonian systems from particle trajectories, aiding in understanding, detection, and design of materials.

Contribution

The paper presents a novel neural network model that infers underlying lattice structures and interactions solely from dynamic trajectories, advancing analysis of complex systems.

Findings

Accurately infers molecular bond connectivity.

Detects lattice structural abnormalities.

Predicts system trajectories effectively.

Abstract

A deep understanding of the intricate interactions between particles within a system is a key approach to revealing the essential characteristics of the system, whether it is an in-depth analysis of molecular properties in the field of chemistry or the design of new materials for specific performance requirements in materials science. To this end, we propose Graph Attention Hamiltonian Neural Network (GAHN), a neural network method that can understand the underlying structure of lattice Hamiltonian systems solely through the dynamic trajectories of particles. We can determine which particles in the system interact with each other, the proportion of interactions between different particles, and whether the potential energy of interactions between particles exhibits even symmetry or not. The obtained structure helps the neural network model to continue predicting the trajectory of the system and further understand the dynamic properties of the system. In addition to understanding the underlying structure of the system, it can be used for detecting lattice structural abnormalities, such as link defects, abnormal interactions, etc. These insights benefit system optimization, design, and detection of aging or damage. Moreover, this approach can integrate other components to deduce the link structure needed for specific parts, showcasing its scalability and potential. We tested it on a challenging molecular dynamics dataset, and the results proved its ability to accurately infer molecular bond connectivity, highlighting its scientific research potential.