5 papers
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-…
Machine learning nonequilibrium phase transitions in charge-density wave insulators
Yunhao Fan, Sheng Zhang, Gia-Wei Chern
Nonequilibrium electronic forces play a central role in voltage-driven phase transitions but are notoriously expensive to evaluate in dynamical simulations. Here we develop a machi…
Equivariant Neural Networks for Force-Field Models of Lattice Systems
Yunhao Fan, Gia-Wei Chern
Machine-learning (ML) force fields enable large-scale simulations with near-first-principles accuracy at substantially reduced computational cost. Recent work has extended ML force…
Machine Learning Force-Field Approach for Itinerant Electron Magnets
Sheng Zhang, Yunhao Fan, Kotaro Shimizu +1
We review the recent development of machine-learning (ML) force-field frameworks for Landau-Lifshitz-Gilbert (LLG) dynamics simulations of itinerant electron magnets, focusing on t…
Enhanced coarsening of charge density waves induced by electron correlation: Machine-learning enabled large-scale dynamical simulations
Yang Yang, Chen Cheng, Yunhao Fan +1
The phase ordering kinetics of emergent orders in correlated electron systems is a fundamental topic in non-equilibrium physics, yet it remains largely unexplored. The intricate in…