29 citations · 34 across the 3 of their papers we have counts for
4 papers
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…
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…
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…