From the 1 of 12 linked papers with an AI index.
12 papers
Graph Neural Network Force Fields for Spin Dynamics in Metallic Magnets
Ali Rayat, Yunhao Fan, Gia-Wei Chern
The paper presents a graph neural network framework that learns magnetic force fields from electronic calculations to efficiently simulate spin dynamics in metallic magnets, reprod…
Magnetic HIP-NN for spin dynamics in disordered itinerant magnets
Supriyo Ghosh, Yunhao Fan, Sheng Zhang +2
We present a magnetic extension of the Hierarchically Interacting Particle Neural Network (HIP-NN) that enables large-scale simulations of electron-mediated spin dynamics in disord…
Machine-learning modeling of magnetization dynamics in quasi-equilibrium and driven metallic spin systems
Gia-Wei Chern, Yunhao Fan, Sheng Zhang +1
We review recent advances in machine-learning (ML) force-field methods for large-scale Landau-Lifshitz-Gilbert (LLG) simulations of metallic spin systems. We generalize the Behler-…
Graph neural network force fields for adiabatic dynamics of lattice Hamiltonians
Yunhao Fan, Gia-Wei Chern
Scalable and symmetry-consistent force-field models are essential for extending quantum-accurate simulations to large spatiotemporal scales. While descriptor-based neural networks…
Machine-learning force-field models for dynamical simulations of metallic magnets
Gia-Wei Chern, Yunhao Fan, Sheng Zhang +1
We review recent advances in machine learning (ML) force-field methods for Landau-Lifshitz-Gilbert (LLG) simulations of itinerant electron magnets, focusing on scalability and tran…
Machine Learning Modeling of Charge-Density-Wave Recovery After Laser Melting
Sankha Subhra Bakshi, Yunhao Fan, Gia-Wei Chern
We investigate the nonequilibrium dynamics of a laser-pumped two-dimensional spinless Holstein model within a semiclassical framework, focusing on the melting and recovery of long-…