29 citations · 114 across the 9 of their papers we have counts for
9 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 predictions for local electronic properties of disordered correlated electron systems
Yi-Hsuan Liu, Sheng Zhang, Puhan Zhang +2
We present a scalable machine learning (ML) model to predict local electronic properties such as on-site electron number and double occupation for disordered correlated electron sy…
Descriptors for Machine Learning Model of Generalized Force Field in Condensed Matter Systems
Puhan Zhang, Sheng Zhang, Gia-Wei Chern
We outline the general framework of machine learning (ML) methods for multi-scale dynamical modeling of condensed matter systems, and in particular of strongly correlated electron…
Machine learning nonequilibrium electron forces for adiabatic spin dynamics
Puhan Zhang, Gia-Wei Chern
We present a generalized potential theory of nonequilibrium torques for the Landau-Lifshitz equation. The general formulation of exchange forces in terms of two potential energies…
Arrested phase separation in double-exchange models: machine-learning enabled large-scale simulation
Puhan Zhang, Gia-Wei Chern
We present large-scale dynamical simulations of electronic phase separation in the single-band double-exchange model based on deep-learning neural-network potentials trained from s…
Anomalous phase separation dynamics in a correlated electron system: machine-learning enabled large-scale kinetic Monte Carlo simulations
Sheng Zhang, Puhan Zhang, Gia-Wei Chern
Phase separation plays a central role in the emergence of novel functionalities of correlated electron materials. The structure of the mixed-phase states depends strongly on the no…
Machine learning dynamics of phase separation in correlated electron magnets
Puhan Zhang, Preetha Saha, Gia-Wei Chern
We demonstrate machine-learning enabled large-scale dynamical simulations of electronic phase separation in double-exchange system. This model, also known as the ferromagnetic Kond…
Machine learning electron correlation in a disordered medium
Jianhua Ma, Puhan Zhang, Yaohua Tan +2
Learning from data has led to a paradigm shift in computational materials science. In particular, it has been shown that neural networks can learn the potential energy surface and…