16 citations · 22 across the 10 of their papers we have counts for
10 papers
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…
Designing electronic magnetoelectric matter with organic quantum spin trimers
Yuko Hosokoshi, Christopher P. Aoyama, Zhuowei Zhang +17
Magnetoelectric (ME) phenomena are commonly driven by spin-lattice coupling. Here we demonstrate a different route based on frustrated quantum spin trimers that intrinsically inter…
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…
Kinetics of orbital ordering in cooperative Jahn-Teller models: Machine-learning enabled large-scale simulations
Supriyo Ghosh, Sheng Zhang, Chen Cheng +1
We present a scalable machine learning (ML) force-field model for the adiabatic dynamics of cooperative Jahn-Teller (JT) systems. Large scale dynamical simulations of the JT model…
Coarsening of chiral domains in itinerant electron magnets: A machine learning force field approach
Yunhao Fan, Sheng Zhang, Gia-Wei Chern
Frustrated itinerant magnets often exhibit complex noncollinear or noncoplanar magnetic orders which support topological electronic structures. A canonical example is the anomalous…
Machine learning for phase ordering dynamics of charge density waves
Chen Cheng, Sheng Zhang, Gia-Wei Chern
We present a machine learning (ML) framework for large-scale dynamical simulations of charge density wave (CDW) states. The charge modulation in a CDW state is often accompanied by…