5 papers
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-…
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 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…
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…
Machine learning approach for vibronically renormalized electronic band structures
Niraj Aryal, Sheng Zhang, Weiguo Yin +1
We present a machine learning (ML) method for efficient computation of vibrational thermal expectation values of physical properties from first principles. Our approach is based on…