Related papers: Machine Learning Assisted Insight to Spin Ice Dy$_…
Magnetic excitations in a Ni$_4$ magnetic molecule were investigated by inelastic neutron scattering and bulk susceptibility ($\chi_\text{bulk}$) techniques. The magnetic excitation spectrum obtained from the inelastic neutron scattering…
Spin-dynamics techniques can now be used to study the deterministic time-dependent behavior of magnetic systems containing over 10^5 spins with quite good accuracy. This approach will be introduced, including the theoretical foundations of…
We present an algorithm for finding ground states of two dimensional spin glass systems based on ideas from matrix product states in quantum information theory. The algorithm works directly at zero temperature and defines an approximate…
By utilizing a multi-orbital periodic Anderson model with parameters obtained from \textit{ab initio} band structure calculations, combined with degenerate perturbation theory, we derive effective Kondo-Heisenberg and spin Hamiltonians that…
We propose a non-collinear spin-constrained method that generates training data for deep-learning-based magnetic model, which provides a powerful tool for studying complex magnetic phenomena that requires large-scale simulations at the…
The recent identification of the dysprosium titanate compound $Dy_2 Ti_2 O_7$ as a ``Spin-Ice'', i.e. the spin analog of regular entropic ice of Pauling, has created considerable excitement. The ability to manipulate spins using magnetic…
In designing efficient feedback control laws for fluid flow, the modern control theory can serve as a powerful tool if the model can be represented by a linear ordinary differential equation (ODE). However, it is generally difficult to find…
Spin glasses are magnetic materials exhibiting numerous magnetization patterns, that randomly vary both in real space and in time. To date, it is still not well understood what the nature of these spatiotemporal dynamics is, namely if they…
Data sets that are specified by a large number of features are currently outside the area of applicability for quantum machine learning algorithms. An immediate solution to this impasse is the application of dimensionality reduction methods…
We study the thermodynamic phase transition of a spin Hamiltonian comprising two 3D magnetic sublattices. Each sublattice contains XY spins coupled by the usual bilinear exchange, while spins in different sublattices only interact via…
Spin Hamiltonians, like the Heisenberg model, are used to describe magnetic properties of exchange-coupled molecules and solids. For finite clusters, physical quantities such as heat capacities, magnetic susceptibilities or…
Autoencoders offer a general way of learning low-dimensional, non-linear representations from data without labels. This is achieved without making any particular assumptions about the data type or other domain knowledge. The generality and…
The isothermal remanent magnetization is used to investigate dynamical magnetic properties of spatially three dimensional spin glasses with different spin dimensionality (Ising, XY, Heisenberg). The isothermal remanent magnetization is…
Implicit in the study of magnetic materials is the concept of spin Hamiltonians, which emerge as the low-energy theories of correlation-driven insulators. In order to predict and establish such Hamiltonians for real materials, a variety of…
We perform a global fit to data from Dark Matter (DM) direct detection experiments, including the recent CDMS-II results. We discuss possible interpretations of the DAMA annual modulation signal in terms of spin-independent and…
The construction of good effective models is an essential part of understanding and simulating complex systems in many areas of science. It is a particular challenge for correlated many body quantum systems displaying emergent physics. We…
The development by machine learning of models predicting materials' properties usually requires the use of a large number of consistent data for training. However, quality experimental datasets are not always available or self-consistent.…
The expansiveness of compositional phase space is too vast to fully search using current theoretical tools for many emergent problems in condensed matter physics. The reliance on a deep chemical understanding is one method to identify local…
Kilometer-scale Earth system models are essential for capturing local climate change. However, these models are computationally expensive and produce petabyte-scale outputs, which limits their utility for applications such as probabilistic…
Artificial spin ice arrays of micromagnetic islands are a means of engineering additional energy scales and frustration into magnetic materials. Despite much progress in elucidating the properties of such arrays, the `spins' in the systems…