Related papers: Universal Magnetic Structure Prediction from Atomi…

Machine-learning of atomic-scale properties amounts to extracting correlations between structure, composition and the quantity that one wants to predict. Representing the input structure in a way that best reflects such correlations makes…

Two of the most widely used electronic structure theory methods, namely Hartree-Fock and Kohn-Sham density functional theory, both requires the iterative solution of a set of Schr\"odinger-like equations. The speed of convergence of such…

Amorphous materials are coming within reach of realistic computer simulations, but new approaches are needed to fully understand their intricate atomic structures. Here, we show how machine-learning (ML)-based techniques can give new,…

We report a comprehensive experimental and theoretical study of the quasi-one-dimensional quantum magnet CuNCN. Based on magnetization measurements above room temperature as well as muon spin rotation and electron spin resonance…

Graph-based neural networks and, specifically, message-passing neural networks (MPNNs) have shown great potential in predicting physical properties of solids. In this work, we train an MPNN to first classify materials through density…

The origin of astrophysical magnetic fields observed in galaxies and clusters of galaxies is still unclear. One possibility is that primordial magnetic fields generated in the early Universe provide seeds that grow through compression and…

We introduce a novel equivariant graph neural network (GNN) architecture designed to predict the tensorial response properties of molecules. Unlike traditional frameworks that focus on regressing scalar quantities and derive tensorial…

Various machine learning models have been used to predict the properties of polycrystalline materials, but none of them directly consider the physical interactions among neighboring grains despite such microscopic interactions critically…

Magnetic gels and elastomers are promising candidates to construct reversibly excitable soft actuators, triggered from outside by magnetic fields. These magnetic fields induce or alter the magnetic interactions between discrete rigid…

Results of magnetization and neutron diffraction measurements of the manganese vanadate system Mn$_5$(VO$_4$)$_2$(OH)$_4$ are reported. The crystal structure of this compound contains triangular [Mn$_3$O$_{13}$] building blocks that produce…

We investigate the structural conditions for metamagnetism in MnP and related materials using Density Functional Theory. A magnetic stability plot is constructed taking into account the two shortest Mn-Mn distances. We find that a…

In this paper PREMONN (PREdictive MOdular Neural Networks) model/architecture is generalized to functions of two variables and to non-Euclidean spaces. It is presented in the context of 3D invariant shape recognition and texture…

Computational discovery of magnetic materials remains challenging because magnetism arises from the competition between kinetic energy and Coulomb interaction that is often beyond the reach of standard electronic-structure methods. Here we…

We use density functional theory to study the structural, magnetic and electronic structure of the organo-metallic quantum magnet $\mathrm{NiCl_2-4SC(NH_2)_2}$ (DTN). Recent work has demonstrated the quasi-1D nature of the molecular crystal…

We present a graph-based deep learning framework for predicting the magnetic properties of quasi-one-dimensional Ising spin systems. The lattice geometry is encoded as a graph and processed by a graph neural network (GNN) followed by fully…

We use spherical neutron polarimetry to determine the ground state magnetic structure of Mn3Sn. We find that Mn3Sn adopts an inverse triangular structure with spins parallel to <100> (Type III) rather than spins parallel to <110> (Type IV).…

Predicting the properties of a molecule from its structure is a challenging task. Recently, deep learning methods have improved the state of the art for this task because of their ability to learn useful features from the given data. By…

Electronic structure calculations, using the charge and spin self-consistent Korringa- Kohn-Rostoker (KKR) method, have been performed for several $R$Mn$X$ compounds ($R$ = Mg, Ca, Sr, Ba, Y; $X$ = Si, Ge) of the CeFeSi-type structure. The…

We preprocess the raw NMR spectrum and extract key characteristic features by using two different methodologies, called equidistant sampling and peak sampling for subsequent substructure pattern recognition; meanwhile may provide the…

Conventional wisdom of materials modelling stipulates that both chemical composition and crystal structure are integral in the prediction of physical properties. However, recent developments challenge this by reporting accurate…