相关论文: Determination of the Dzyaloshinskii-Moriya interac…

Nuclear materials are often demanded to function for extended time in extreme environments, including high radiation fluxes and transmutation, high temperature and temperature gradients, stresses, and corrosive coolants. They also have a…

To leverage advancements in machine learning for metallic materials design and property prediction, it is crucial to develop a data-reduced representation of metal microstructures that surpasses the limitations of current physics-based…

Machine learning has proven to be a valuable tool to approximate functions in high-dimensional spaces. Unfortunately, analysis of these models to extract the relevant physics is never as easy as applying machine learning to a large dataset…

Despite a decade of research, the precise mechanisms occurring at interfaces underlying the Dzyaloshinskii-Moriya interaction (DMI), and thus the possibility of fine-tuning it, are not yet fully identified. In this study, we investigate the…

Isolating the features associated with different materials growth conditions is important to facilitate the tuning of these conditions for effective materials growth and characterization. This study presents machine learning models for…

We present a complete set of chemo-structural descriptors to significantly extend the applicability of machine-learning (ML) in material screening and mapping energy landscape for multicomponent systems. These new descriptors allow…

Hamiltonian parameter estimation is crucial in condensed matter physics, but time and cost consuming in terms of resources used. With advances in observation techniques, high-resolution images with more detailed information are obtained,…

We have derived an expression of the Dzyaloshinskii-Moriya (DM) interaction, where all the three components of the DM vector can be calculated independently, for a general, non-collinear magnetic configuration. The formalism is implemented…

The recent observation of ferromagnetic order in two-dimensional (2D) materials has initiated a booming interest in the subject of 2D magnetism. In contrast to bulk materials, 2D materials can only exhibit magnetic order in the presence of…

Advances in robotics, artificial intelligence, and machine learning are ushering in a new age of automation, as machines match or outperform human performance. Machine intelligence can enable businesses to improve performance by reducing…

We have developed a fully microscopic theory of magnetic properties of the prototype molecular magnet Mn12. First, the intra-molecular magnetic properties have been studied by means of first-principles density functional-based methods, with…

We show that the scattering rate for any dark matter (DM) interaction with electrons in any target is proportional to several measurable material properties, encapsulated by a single master formula. This generalizes the dielectric function…

Multilayers of heavy metals and ferromagnets are the basis for the formation of various structures with inhomogeneous chiral magnetic distributions. The most informative method for studying magnetic states in such structures with a high…

We evaluate the micromagnetic energy of a domain wall contained in a thin, horn-shaped nanostructure and propose a method to extract the strength of the interfacial Dzyaloshinskii-Moriya interaction based on the equilibrium position of the…

Identifying the magnetic state of materials is of great interest in a wide range of applications, but direct identification is not always straightforward due to limitations in neutron scattering experiments. In this work, we present a…

To investigate the novel quantum dynamic behaviors of magnetic materials that arise from complex spin-spin interactions, it is necessary to probe the magnetic response at a speed greater than the spin-relaxation and dephasing processes.…

The use of machine learning algorithms is an attractive way to produce very fast detector simulations for scattering reactions that can otherwise be computationally expensive. Here we develop a factorised approach where we deal with each…

Periodically patterned metamaterials are known for exhibiting wave properties similar to the ones observed in electronic band structures in crystal lattices. In particular, periodic ferromagnetic materials are characterized by the presence…

We study the modification of low temperature properties of quantum magnets such as magnetization, heat capacity, energy spectrum, and densities of condensed and noncondensed quasiparticles (triplons) due to anisotropies in the framework of…

The recent surge in the adoption of machine learning techniques for materials design, discovery, and characterization has resulted in an increased interest and application of Image Driven Machine Learning (IDML) approaches. In this work, we…