Fanghua Tian | Searcharxiv

arXiv2024

Room-temperature intrinsic ferromagnetism of two-dimensional Na2Cl crystals originated by s- and p-orbitals

Ferromagnetism, as one of the most valuable properties of materials, has attracted sustained and widespread interest in basic and applied research from ancient compasses to modern electronic devices. Traditionally, intrinsic ferromagnetism has been attributed to the permanent magnetic moment induced by partially filled d- or f-orbitals. However, the development of ferromagnetic materials has been limited by this electronic structure convention. Thus, the identification of additional materials that are not constrained by this conventional rule but also exhibit intrinsic ferromagnetism is highly expected and may impact all the fields based on ferromagnetism. Here, we report the direct observation of room-temperature intrinsic ferromagnetism in two-dimensional (2D) Na2Cl crystals, in which there are only partially filled s- and p-orbitals rather than d- or f-orbitals, using the superconducting quantum interference device (SQUID) and magnetic force microscope (MFM). These Na2Cl crystals formed in reduced graphene oxide (rGO) membranes have an unconventional stoichiometric structure leading to unique electron and spin distributions. And the structure of these 2D Na2Cl crystals, including the Na and Cl sites, is characterized in situ for the first time and directly observed by cryo-electron microscopy (cryo-EM) based on the observed difference in contrast between Na stacked with Cl and single Na. These findings break the conventional rule of intrinsic ferromagnetism and provide new insights into the design of novel magnetic and electronic devices and transistors with a size down to the atomic scale.

cond-mat.mtrl-sci↗

arXiv2019

Machine learning magnetic parameters from spin configurations

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, which can serve as an input to machine learning (ML) algorithms to extract Hamiltonian parameters. However, the number of labeled images is rather limited. Here, we provide a protocol for Hamiltonian parameter estimation based on a machine learning architecture, which is trained on a small amount of simulated images and applied to experimental spin configuration images. Sliding windows on the input images enlarges the number of training images; therefore we can train well a neural network on a small dataset of simulated images which are generated adaptively using the same external conditions such as temperature and magnetic field as the experiment. The neural network is applied to the experimental image and estimates magnetic parameters efficiently. We demonstrate the success of the estimation by reproducing the same configuration from simulation and predict a hysteresis loop accurately. Our approach paves a way to a stable and general parameter estimation.

cond-mat.dis-nn↗