Related papers: Intrinsic structure of relaxor ferroelectrics from…
PbZr_{1-x}Ti_xO_3 (PZT) and Pb(Mg_{1/3}Nb_{2/3})_{1-x}Ti_xO_3 (PMN-$x$PT) are complex lead-oxide perovskites that display exceptional piezoelectric properties for pseudorhombohedral compositions near a tetragonal phase boundary. In PZT…
Antiferromagnetic antiperovskites, of the form Mn$_3B$N ($B$ = Ni, Cu, Zn, Sn, Ir, and Pt), have shown an outstanding behavior in which, giant negative thermal expansion and chiral magnetic structures are intertwined. As such, aiming to…
We propose a scheme for {\it ab initio} configurational sampling in multicomponent crystalline solids using Behler-Parinello type neural network potentials (NNPs) in an unconventional way: the NNPs are trained to predict the energies of…
Interatomic potentials (IPs) with wide elemental coverage and high accuracy are powerful tools for high-throughput materials discovery. While the past few years witnessed the development of multiple new universal IPs that cover wide ranges…
Motivated by a recent proposal by O. P. Sushkov and co-workers to search for a P,T-violating Schiff moment of the $^{207}$Pb nucleus in a ferroelectric solid, we have carried out a high-field nuclear magnetic resonance study of the…
Experimental and theoretical studies of unusual polar, dielectric and magnetic properties of room temperature multiferroics, such as perovskites Pb(Fe$_{1/2}$Nb$_{1/2}$)O$_3$ (PFN) and Pb(Fe$_{1/2}$Ta$_{1/2}$)O$_3$ (PFT), are very…
Machine learning interatomic potentials (MLIPs) trained on large, chemically diverse datasets are revolutionizing computational chemistry, enabling molecular dynamics simulations of battery electrolytes with near-DFT accuracy over 10,000…
Machine learning interatomic potentials (MLIPs) have massively changed the field of atomistic modeling. They enable the accuracy of density functional theory in large-scale simulations while being nearly as fast as classical interatomic…
We review results obtained from recent neutron scattering studies of the lead-oxide class of perovskite relaxors PMN and PZN. A ferroelectric soft mode has been identified in PMN at 1100 K that becomes overdamped near 620 K. This is the…
Machine-learned interatomic potentials (MLIPs) have become the gold standard for atomistic simulations, yet their extension to magnetic materials remains challenging because spin fluctuations must be captured either explicitly or…
Machine learning interatomic potentials (MLIPs) are routinely used to model diverse atomistic phenomena, yet parameterizing them to accurately capture solid-state phase transformations remains difficult. We present error metrics and…
We present a detailed study of the local atomic and magnetic structure of the type-I multiferroic perovskite system (Sr,Ba)(Mn,Ti)O$_3$ using x-ray and neutron pair distribution function (PDF) analysis, polarized neutron scattering, and…
Machine learning interatomic potentials (MLIPs) enable the accurate simulation of materials at larger sizes and time scales, and play increasingly important roles in the computational understanding and design of materials. However, MLIPs…
Machine-learned interatomic potentials (MLIPs) have rapidly progressed in accuracy, speed, and data efficiency in recent years. However, training robust MLIPs in multicomponent systems still remains a challenge. In this work, we train a…
Machine-learning interatomic potential (MLIP) has been of growing interest as a useful method to describe the energetics of systems of interest. In the present study, we examine the accuracy of linearized pairwise MLIPs and…
We present the magnetic structure of $\mathrm{TmMn_3O_6}$, solved via neutron powder diffraction - the first such study of any $R\mathrm{Mn_3O_6}$ A-site columnar-ordered quadruple perovskite to be reported. We demonstrate that long range…
Using the full-potential linearized-augmented-plane-wave (FLAPW) method, we have analyzed systematically the trends in the structural and electronic properties of the 3d and 4d transition-metal oxides SrMO_3 (M = Ti, V, Zr and Nb). The…
Molten salts are crucial for clean energy applications, yet exploring their thermophysical properties across diverse chemical space remains challenging. We present the development of a machine learning interatomic potential (MLIP) called…
Machine learning interatomic potentials (MLIPs) evaluate potential energy surfaces orders of magnitude faster while maintaining accuracy comparable to first-principles calculations, and universal MLIPs that cover most of the periodic table…
A review is given of recent neutron and x-ray scattering studies of the lead-oxide perovskite relaxor systems Pb(Zn1/3Nb2/3)O3-xPbTiO3 and Pb(Mg1/3Nb2/3)O3-xPbTiO3. X-ray measurements by Noheda et al. have established that these two systems…