no code implementations • 12 Apr 2022 • Yi-Hsuan Liu, Sheng Zhang, Puhan Zhang, Ting-Kuo Lee, Gia-Wei Chern
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 systems.
no code implementations • 3 Jan 2022 • Puhan Zhang, Sheng Zhang, Gia-Wei Chern
A general theory of the descriptor for the classical fields is formulated, and two types of models are distinguished depending on the presence or absence of an internal symmetry for the classical field.
no code implementations • 22 Dec 2021 • Puhan Zhang, Gia-Wei Chern
We present a generalized potential theory of nonequilibrium torques for the Landau-Lifshitz equation.
no code implementations • 27 May 2021 • Sheng Zhang, Puhan Zhang, Gia-Wei Chern
With the aid of modern machine learning methods, we demonstrate the first-ever large-scale kinetic Monte Carlo simulations of the phase separation process for the Falicov-Kimball model, which is one of the canonical strongly correlated electron systems.
2 code implementations • 18 May 2021 • 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 small-size exact diagonalization solutions.
no code implementations • 7 Jun 2020 • Puhan Zhang, Preetha Saha, Gia-Wei Chern
We demonstrate machine-learning enabled large-scale dynamical simulations of electronic phase separation in double-exchange system.
no code implementations • 4 Oct 2018 • Jianhua Ma, Puhan Zhang, Yao-Hua Tan, Avik W. Ghosh, Gia-Wei Chern
Learning from data has led to a paradigm shift in computational materials science.