Related papers: Spin Neural Network Potential for Magnetic Phase T…
Ag2MnO2 is studied as a possible candidate compound for an antiferromagnetic XY spin model on a triangular lattice. In spite of the large Curie-Weiss temperature of -430 K found in magnetic susceptibi-lity, Mn3+ spins with S = 2 do not…
The work presents the electronic structure, spin state and optical properties of TM-dinuclear molecules (TM = Cr, Mn, Fe, Co, and Ni) which was modelled according to the recently reported Pt$^{II}$-dinuclear complex…
Quantum magnetism is one of the most active fields for exploring exotic phases and phase transitions. The recently synthesized Na2BaCo(PO4)2 (NBCP) is an ideal material incarnation of the spin-1/2 easy axis triangular lattice…
With the rapid development of energy storage technology, high-performance solid-state electrolytes (SSEs) have become critical for next-generation lithium-ion batteries. These materials require high ionic conductivity, excellent…
Penta-NiN2, a novel pentagonal 2D sheet with potential nanoelectronic applications, is investigated in terms of its lattice thermal conductivity, stability, and mechanical behavior. A deep learning interatomic potential (DLP) is firstly…
The development of modern ab initio methods has rapidly increased our understanding of physics, chemistry and materials science. Unfortunately, intensive ab initio calculations are intractable for large and complex systems. On the other…
The iron-based high temperature superconductors exhibit a rich phase diagram reflecting a complex interplay between spin, lattice, and orbital degrees of freedom [1-4]. The nematic state observed in many of these compounds epitomizes this…
The diffusive phase transformations occurring in feldspar, a common mineral in the crust of the Earth, are essential for reconstructing the thermal histories of magmatic and metamorphic rocks. Due to the long timescales over which these…
Ordered intermetallics are long believed to be the final products of the aging of U-Nb solid solutions at low temperatures, a crucial property for the practical applications of this alloy in engineering and industry. However, such…
Metal organic magnets have enormous potential to host a variety of electronic and magnetic phases that originate from a strong interplay between the spin, orbital and lattice degrees of freedom. We control this interplay in the quantum…
It is assumed that U atoms in $UGe_2$ have a number of $f$ electrons appropriate to give them each a spin $s=1$ as well as one extra itinerant electron which may equally well be on one or other U atom. The dynamical degrees of freedom are…
Artificial neural network potentials (NNPs) have emerged as effective tools for understanding atomic interactions at the atomic scale in various phenomena. Recently, we developed highly transferable NNPs for {\alpha}-iron and…
Magnetic materials with the spinel structure (A$^{2+}$B$^{3+}_2$O$^4$) form the core of numerous magnetic devices, but ZnFe$_2$O$_4$ constitutes a peculiar example where the nature of the magnetism is still unresolved. Susceptibility…
Experimental realization of magnetic field induced quantum phase transition (QPT) is reported for NH4CuPO4.H2O, a two spin cluster material with isotropic Heisenberg interaction. Experimental magnetization and specific heat data have been…
Uranium dihydride UH2 is a metastable phase unknown in bulk form but accessible through thin-film synthesis. We prepared UH2 films by reactive dc sputtering on CaF2(001) or Si(001) substrates, the latter equipped with a Mo buffer layer to…
After a successful supernova, a proto-neutron star (PNS) cools by emitting neutrinos on $\sim 1-100$ s timescales. Provided that there are neutrino emission `hot-spots' or `cold-spots' on the surface of the rotating PNS, we can expect a…
Universal machine learning interatomic potentials (uMLIPs) deliver near ab initio accuracy in energy and force calculations at low computational cost, making them invaluable for materials modeling. Although uMLIPs are pre-trained on vast ab…
Uranium compounds can manifest a wide range of fascinating many-body phenomena, and are often thought to be poised at a crossover between localized and itinerant regimes for 5f electrons. The antiferromagnetic dipnictide USb2 has been of…
Machine learning interatomic potentials (MLIPs) are inherently limited by the accuracy of the training data, usually consisting of energies and forces obtained from quantum mechanical calculations, such as density functional theory (DFT).…
We present a study on the transport and materials properties of aluminum spanning from ambient to warm dense matter conditions using a machine-learned interatomic potential (ML-IAP). Prior research has utilized ML-IAPs to simulate phenomena…