Related papers: Spin Neural Network Potential for Magnetic Phase T…
The discovery of new multicomponent inorganic compounds can provide direct solutions to many scientific and engineering challenges, yet the vast size of the uncharted material space dwarfs current synthesis throughput. While the…
Iron-bearing oxides undergo a series of pressure-induced electronic, spin and structural transitions that can cause seismic anomalies and dynamic instabilities in Earth's mantle and outer core. We employ x-ray diffraction and x-ray emission…
Gallium oxide (Ga2O3) is a wide-bandgap semiconductor with promising applications in high-power and high-frequency electronics. However, its complex polymorphic nature poses substantial challenges for fundamental studies, particularly in…
The accurate description of the structural and thermodynamic properties of ferroelectrics has been one of the most remarkable achievements of Density Functional Theory (DFT). However, running large simulation cells with DFT is…
We present a study of the structural phase transition, mechanical and thermodynamic properties of UO$_{2}$ by means of the local density approximation (LDA)$+U$ approach. A phase transition pressure of 40 GPa, which agrees well with the…
We demonstrate that a high-dimensional neural network potential (HDNNP) can predict the lattice thermal conductivity of semiconducting materials with an accuracy comparable to that of density functional theory (DFT) calculation. After a…
The \textit{Spirit} framework is designed for atomic scale spin simulations of magnetic systems of arbitrary geometry and magnetic structure, providing a graphical user interface with powerful visualizations and an easy to use scripting…
Niobium (Nb) and its alloys are extensively used in various technological applications owing to their favorable mechanical, thermal and irradiation properties. Accurately modeling Nb under irradiation is essential for predicting…
Metallic strontium titanate (SrTiO$_3$) is known to have both normal-state and superconducting properties that vary strongly over a wide range of charge carrier densities. This indicates the importance of nonlinear dynamics, and has…
Serpentines are layered hydrous magnesium silicates (MgO$\cdot$SiO$_2\cdot$H$_2$O) formed through serpentinization, a geochemical process that significantly alters the physical property of the mantle. They are hard to investigate…
The free energy of a system is central to many material models. Although free energy data is not generally found directly, its derivatives can be observed or calculated. In this work, we present an Integrable Deep Neural Network (IDNN) that…
Dynamic nuclear polarisation (DNP) refers to a class of techniques used to increase the signal in nuclear magnetic resonance measurements by transferring spin polarisation from ensembles of highly polarised electrons to target nuclear…
Machine Learning (ML)-based force fields are attracting ever-increasing interest due to their capacity to span spatiotemporal scales of classical interatomic potentials at quantum-level accuracy. They can be trained based on high-fidelity…
The Bredig transition to the superionic phase indicated with the lambda-peak in Cp was highly expected for PuO2 as other actinide dioxides. However, least-square fit and local smoothing techniques applied to the experimental enthalpy data…
The potential for complex systems to exhibit tipping points in which an equilibrium state undergoes a sudden and often irreversible shift is well established, but prediction of these events using standard forecast modeling techniques is…
The advent of machine learning in materials science opens the way for exciting and ambitious simulations of large systems and long time scales with the accuracy of ab-initio calculations. Recently, several pre-trained universal machine…
Universal machine-learning interatomic potentials (uMLIPs) have become powerful tools for accelerating computational materials discovery by replacing expensive first-principles calculations in crystal structure prediction (CSP). However,…
Antiferromagnets are magnetically ordered materials which exhibit no net moment and thus are insensitive to magnetic fields. Antiferromagnetic spintronics aims to take advantage of this insensitivity for enhanced stability, while at the…
In high spin molecules metal ions are coupled by ferro or antiferromagnetic short range interactions so that their magnetic moments are parallel or antiparallel to each other at temperatures (T) much smaller than the coupling constant J.…
From a general model of the Mn oxides R_{1-x}A_{x}MnO_3, we derive an effective Hamiltonian in the low-energy subspace using the projection operator method, in which a novel coupling between the spin and orbital degrees of freedom is…