Message-passing neural network for magnetic phase transition simulation.

Journal: Journal of physics. Condensed matter : an Institute of Physics journal
Published Date: May 19, 2025

Abstract

Predicting magnetic phase transitions traditionally relies on a Hamiltonian model to capture key magnetic interactions. Recent advances in machine learning enables the development of a unified approach that can handle diverse magnetic systems without designing new Hamiltonians for each case. To this end, we employ message-passing neural network (MPNN) potentials to investigate magnetic phase transitions of two-dimensional chromium trihalidesCrX3(X = I, Br, Cl) . We achieve this by introducing a specialized MPNN with the ability to incorporate the magnetic degrees of freedom. This magnetic MPNN incorporates atomic magnetic moments directly into the message-passing process, enabling accurate modeling of potential energy surfaces in magnetic materials. This approach improves on our previous work, which had the same aim but used Behler-Parrinello neural network that relies on hand-crafted descriptors as the underlying universal magnetic Hamiltonian. It also adds the capability to treat magnetic degrees of freedom and atom displacement in a unified way. Using two-dimensionalCrX3as examples and combining the MPNN with the Landau-Lifshitz-Gilbert equation, we simulate ferromagnetic and antiferromagnetic phase transitions as a function of temperature. These results highlight the potential of MPNNs for advancing research in magnetic materials.

Authors

  • Shuhao Hu
    Department of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu, Sichuan, 611130, China.
  • Xinjian Ouyang
    Shaanxi Provincial Key Laboratory of Electronic Devices and Advanced Chips, and School of Microelectronic, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China.
  • Zhilong Wang
    College of Engineering, Cornell University, Ithaca, NY, 14853, USA.
  • Feng Zhang
    Institute of Food Safety, Chinese Academy of Inspection and Quarantine, Beijing 100176, China; Key Laboratory of Food Quality and Safety for State Market Regulation, Beijing 100176, China. Electronic address: fengzhang@126.com.
  • Dawei Wang
    Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals