Related papers: Intrinsic structure of relaxor ferroelectrics from…

We propose a first-principles formulism for system with spin fluctuations and apply it to the ordered Fe3Pt to uncover the Invar anomalies, including negative thermal expansion and spontaneous magnetization. The theory has coherently…

Neutron and x-ray scattering studies on relaxor ferroelectric systems Pb(Zn$_{1/3}$Nb$_{2/3}$)O$_3$ (PZN), Pb(Mg$_{1/3}$Nb$_{2/3}$)O$_3$ (PMN), and their solid solutions with PbTiO$_3$ (PT) have shown that inhomogeneities and disorder play…

An argument that relaxor ferroelectricity in the isovalent alloy $\mathrm {Ba(Zr}_{1-x}\mathrm{Ti}_{x})\mathrm{O}_3$ can be understood as an induced moment soft pseudo-spin glass on the B-ions of the $\mathrm{ABO}_{3}$ matrix is extended to…

Crystal structure prediction (CSP) is emerging as a powerful method for the computational design of metal-organic frameworks (MOFs). In this article we employ CSP to perform high-throughput exploration of the crystal energy landscape of…

Machine learning interatomic potentials (MLIPs) have revolutionized computational materials science by bridging the gap between quantum mechanical accuracy and classical simulation efficiency, enabling unprecedented exploration of materials…

Many rotational invariants for crystal structure representations have been used to describe the structure-property relationship by machine learning. The machine learning interatomic potential (MLIP) is one of the applications of rotational…

We propose a data-driven approach for constructing machine-learning interatomic potentials (MLIPs) trained under a regularization with the aim of avoiding nonphysical heat flux. Specifically, we introduce a regularization term for the heat…

The optimization of properties of perovskite oxides has drawn interest on account of their diverse areas of application. In this work, the hierarchical clustering technique is used to reduce the multi-collinearity among selected features…

Lithium-based disordered rocksalts (LDRs), which are an important class of cathodes for advanced Li-ion batteries, represent a complex chemical and configurational space for conventional density functional theory (DFT)-based high-throughput…

Identifying the uniqueness of FeP-based superconductors may shed new lights on the mechanism of superconductivity in iron-pnictides. Here, we report nuclear magnetic resonance(NMR) studies on LiFeP and LiFeAs which have the same crystal…

Machine learning interatomic potentials (MLIPs) enable efficient modeling of molecular interactions with quantum mechanical (QM) accuracy. However, constructing robust and representative training datasets that capture subtle,…

As with many parts of the natural sciences, machine learning interatomic potentials (MLIPs) are revolutionizing the modeling of molecular crystals. However, challenges remain for the accurate and efficient calculation of sublimation…

We present a quantitative analysis of the phonon lineshapes obtained by neutron inelastic scattering methods in the relaxor ferroelectric Pb(Mg1/3Nb2/3)O3 (PMN). Differences in the shapes and apparent positions of the transverse acoustic…

This work demonstrates that fine-tuning transforms foundational machine-learned interatomic potentials (MLIPs) to achieve consistent, near-ab initio accuracy across diverse architectures. Benchmarking five leading MLIP frameworks (MACE,…

A machine-learned spin-lattice interatomic potential (MSLP) for magnetic iron is developed and applied to mesoscopic scale defects. It is achieved by augmenting a spin-lattice Hamiltonian with a neural network term trained to descriptors…

Machine learning interatomic potentials (MLIPs) can now reproduce the energy, forces and stresses of bulk materials with high accuracy compared to first-principles calculations. The description of imperfections, where coordination…

Pb(Fe$_{0.5}$Nb$_{0.5}$)O$_3$ (PFN), one of the few relaxor multiferroic systems, has a $G$-type antiferromagnetic transition at $T_N$ = 143 K and a ferroelectric transition at $T_C$ = 385 K. By using high-resolution neutron-diffraction…

Microstrctural, vibrational, ferroelectric and dielectric properties investigation reveals non-stoichiometric chemical ordered regions (CORs) and relaxor like dielectric characteristics in lead magnesium niobate (PMN) ceramics uniaxially…

Machine learning interatomic potentials (MLIPs) have become widely used tools in atomistic simulations. For much of the history of this field, the most commonly employed architectures were based on short-ranged atomic energy contributions,…

Machine-Learned Interatomic Potentials (MLIPs) require vast amounts of atomic structure data to learn forces and energies, and their performance continues to improve with training set size. Meanwhile, the even greater quantities of…