19 citations · 66 across the 6 of their papers we have counts for
6 papers
Machine learning force-field models for metallic spin glass
Menglin Shi, Sheng Zhang, Gia-Wei Chern
Metallic spin glass systems, such as dilute magnetic alloys, are characterized by randomly distributed local moments coupled to each other through a long-range electron-mediated ef…
Convolutional neural networks for large-scale dynamical modeling of itinerant magnets
Xinlun Cheng, Sheng Zhang, Phong C. H. Nguyen +3
Complex spin textures in itinerant electron magnets hold promises for next-generation memory and information technology. The long-ranged and often frustrated electron-mediated spin…
Machine learning for structure-property relationships: Scalability and limitations
Zhongzheng Tian, Sheng Zhang, Gia-Wei Chern
We present a scalable machine learning (ML) framework for predicting intensive properties and particularly classifying phases of many-body systems. Scalability and transferability…
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…
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…