Complex spin textures in itinerant electron magnets hold promises for next-generation
memory and information technology. The long-ranged and often frustrated electron …

We present a scalable machine learning (ML) framework for predicting intensive properties
and particularly classifying phases of Ising models. Scalability and transferability are central …

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 …

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 …

Frustrated itinerant magnets often exhibit complex noncollinear or noncoplanar magnetic
orders which support topological electronic structures. A canonical example is the …

We present a scalable machine learning (ML) framework for predicting intensive properties
and particularly classifying phases of many-body systems. Scalability and transferability are …

We present a generalized potential theory for conservative as well as nonconservative
forces for the Landau-Lifshitz magnetization dynamics. Importantly, this formulation makes …

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 the …

We review the recent development of machine-learning (ML) force-field frameworks for
Landau-Lifshitz-Gilbert (LLG) dynamics simulations of itinerant electron magnets, focusing …

We apply various unsupervised machine learning methods for phase classification to
investigate the finite-temperature phase diagram of the spinless Falicov-Kimball model in …