Phase behavior and atomic dynamics in RbNa: insights from machine learning interatomic potentials based onmolecular dynamics.

Liquid alkali metal alloys have garnered significant attention because of their potential applications in coolant systems and batteries, driven by the need for environmental conservation and technological development. However, research on these complex systems is limited, necessitating a deeper understanding to ensure their safe and effective utilization. This study presents a comprehensive investigation of the factors that determine the phase diagram of RbNa1-x.

Catalysis in Extreme Field Environments: A Case Study of Strongly Ionized SiO Nanoparticle Surfaces.

High electric fields can significantly alter catalytic environments and the resultant chemical processes. Such fields arise naturally in biological systems but can also be artificially induced through localized nanoscale excitations. Recently, strong field excitation of dielectric nanoparticles has emerged as an avenue for studying catalysis in highly ionized environments, producing extreme electric fields.

Induction and Ferroelectric Switching of Flux Closure Domains in Strained PbTiO with Neural Network Quantum Molecular Dynamics.

We have developed an extension of the Neural Network Quantum Molecular Dynamics (NNQMD) simulation method to incorporate electric-field dynamics based on Born effective charge (BEC), called NNQMD-BEC. We first validate NNQMD-BEC for the switching mechanisms of archetypal ferroelectric PbTiO bulk crystal and 180° domain walls (DWs). NNQMD-BEC simulations correctly describe the nucleation-and-growth mechanism during DW switching.

Squishing Skyrmions: Symmetry-Guided Dynamic Transformation of Polar Topologies Under Compression.

Mechanical controllability of recently discovered topological defects (., skyrmions) in ferroelectric materials is of interest for the development of ultralow-power mechano-electronics that are protected against thermal noise. However, fundamental understanding is hindered by the "multiscale quantum challenge" to describe topological switching encompassing large spatiotemporal scales with quantum mechanical accuracy.

Defect-free and crystallinity-preserving ductile deformation in semiconducting AgS.

Typical ductile materials are metals, which deform by the motion of defects like dislocations in association with non-directional metallic bonds. Unfortunately, this textbook mechanism does not operate in most inorganic semiconductors at ambient temperature, thus severely limiting the development of much-needed flexible electronic devices. We found a shear-deformation mechanism in a recently discovered ductile semiconductor, monoclinic-silver sulfide (AgS), which is defect-free, omni-directional, and preserving perfect crystallinity.

Photoexcitation-Induced Nonthermal Ultrafast Loss of Long-Range Order in GeTe.

Nonadiabatic quantum molecular dynamics is used to investigate the evolution of GeTe photoexcited states. Results reveal a photoexcitation-induced picosecond nonthermal path for the loss of long-range order. A valence electron excitation threshold of 4% is found to trigger local disorder by switching Ge atoms from octahedral to tetrahedral sites and promoting Ge-Ge bonding.

Hydrogen Bonding in Liquid Ammonia.

The nature of hydrogen bonding in condensed ammonia phases, liquid and crystalline ammonia has been a topic of much investigation. Here, we use quantum molecular dynamics simulations to investigate hydrogen bond structure and lifetimes in two ammonia phases: liquid ammonia and crystalline ammonia-I. Unlike liquid water, which has two covalently bonded hydrogen and two hydrogen bonds per oxygen atom, each nitrogen atom in liquid ammonia is found to have only one hydrogen bond at 2.

Exploring far-from-equilibrium ultrafast polarization control in ferroelectric oxides with excited-state neural network quantum molecular dynamics.

Ferroelectric materials exhibit a rich range of complex polar topologies, but their study under far-from-equilibrium optical excitation has been largely unexplored because of the difficulty in modeling the multiple spatiotemporal scales involved quantum-mechanically. To study optical excitation at spatiotemporal scales where these topologies emerge, we have performed multiscale excited-state neural network quantum molecular dynamics simulations that integrate quantum-mechanical description of electronic excitation and billion-atom machine learning molecular dynamics to describe ultrafast polarization control in an archetypal ferroelectric oxide, lead titanate. Far-from-equilibrium quantum simulations reveal a marked photo-induced change in the electronic energy landscape and resulting cross-over from ferroelectric to octahedral tilting topological dynamics within picoseconds.

Deep Well Trapping of Hot Carriers in a Hexagonal Boron Nitride Coating of Polymer Dielectrics.

Polymer dielectrics can be cost-effective alternatives to conventional inorganic dielectric materials, but their practical application is critically hindered by their breakdown under high electric fields driven by excited hot charge carriers. Using a joint experiment-simulation approach, we show that a 2D nanocoating of hexagonal boron nitride (hBN) mitigates the damage done by hot carriers, thereby increasing the breakdown strength. Surface potential decay and dielectric breakdown measurements of hBN-coated Kapton show the carrier-trapping effect in the hBN nanocoating, which leads to an increased breakdown strength.

Neural Network Quantum Molecular Dynamics, Intermediate Range Order in GeSe, and Neutron Scattering Experiments.

A remarkable property of certain covalent glasses and their melts is intermediate range order, manifested as the first sharp diffraction peak (FSDP) in neutron-scattering experiments, as was exhaustively investigated by Price, Saboungi, and collaborators. Atomistic simulations thus far have relied on either quantum molecular dynamics (QMD), with systems too small to resolve FSDP, or classical molecular dynamics, without quantum-mechanical accuracy. We investigate prototypical FSDP in GeSe glass and melt using neural-network quantum molecular dynamics (NNQMD) based on machine learning, which allows large simulation sizes with validated quantum mechanical accuracy to make quantitative comparisons with neutron data.

Dielectric Constant of Liquid Water Determined with Neural Network Quantum Molecular Dynamics.

The static dielectric constant ϵ_{0} and its temperature dependence for liquid water is investigated using neural network quantum molecular dynamics (NNQMD). We compute the exact dielectric constant in canonical ensemble from NNQMD trajectories using fluctuations in macroscopic polarization computed from maximally localized Wannier functions (MLWF). Two deep neural networks are constructed.

Sulfurization of MoO in the Chemical Vapor Deposition Synthesis of MoS Enhanced by an HS/H Mixture.

The typical layered transition metal dichalcogenide (TMDC) material, MoS, is considered a promising candidate for the next-generation electronic device due to its exceptional physical and chemical properties. In chemical vapor deposition synthesis, the sulfurization of MoO powders is an essential reaction step in which the MoO reactants are converted into MoS products. Recent studies have suggested using an HS/H mixture to reduce MoO powders in an effective way.

Computational and training requirements for interatomic potential based on artificial neural network for estimating low thermal conductivity of silver chalcogenides.

We examined the estimation of thermal conductivity through molecular dynamics simulations for a superionic conductor, α-AgSe, using the interatomic potential based on an artificial neural network (ANN potential). The training data were created using the existing empirical potential of AgSe to help find suitable computational and training requirements for the ANN potential, with the intent to apply them to first-principles calculations. The thermal conductivities calculated using different definitions of heat flux were compared, and the effect of explicit long-range Coulomb interaction on the conductivities was investigated.

Photoexcitation Induced Ultrafast Nonthermal Amorphization in SbTe.

Phase-change materials are of great interest for low-power high-throughput storage devices in next-generation neuromorphic computing technologies. Their operation is based on the contrasting properties of their amorphous and crystalline phases, which can be switched on the nanosecond time scale. Among the archetypal phase change materials based on Ge-Sb-Te alloys, SbTe displays a fast and energy-efficient crystallization-amorphization cycle due to its growth-dominated crystallization and low melting point.

Ferromagnetic metal conversion directly from two-dimensional nickel hydroxide.

We have demonstrated a direct metallic conversion from nickel hydroxide nanosheets to nickel metal nanostructures by thermal annealing in vacuum. The metal transition of the single-layer nanosheets deposited on a Si substrate was revealed by x-ray absorption near edge structure (XANES) measurements. The XANES signal significantly changed at annealing temperatures above 250 °C.

Application of First-Principles-Based Artificial Neural Network Potentials to Multiscale-Shock Dynamics Simulations on Solid Materials.

The use of artificial neural network (ANN) potentials trained with first-principles calculations has emerged as a promising approach for molecular dynamics (MD) simulations encompassing large space and time scales while retaining first-principles accuracy. To date, however, the application of ANN-MD has been limited to processes. Here we combine first-principles-trained ANN-MD with multiscale shock theory (MSST) to successfully describe shock phenomena.

Intermediate range structure of amorphous CuGeTe: ab initio molecular dynamics study.

We have investigated the intermediate range structure of amorphous CuGeTe based on ab initio molecular dynamics simulations. The highest population of ring size is three, which makes the triangle structure. This ring consists of mainly CuTe.

Hydrogen Bond Preserving Stress Release Mechanism Is Key to the Resilience of Aramid Fibers.

molecular dynamics simulations of shock loading on poly(-phenylene terephthalamide) (PPTA) reveal stress release mechanisms based on hydrogen bond preserving structural phase transformation (SPT) and planar amorphization. The SPT is triggered by [100] shock-induced coplanarity of phenylene groups and rearrangement of sheet stacking leading to a novel monoclinic phase. Planar amorphization is generated by [010] shock-induced scission of hydrogen bonds leading to disruption of polymer sheets, and -to- conformational change of polymer chains.

Guidelines for creating artificial neural network empirical interatomic potential from first-principles molecular dynamics data under specific conditions and its application to α-AgSe.

First-principles molecular dynamics (FPMD) simulations are highly accurate, but due to their high calculation cost, the computational scale is often limited to hundreds of atoms and few picoseconds under specific temperature and pressure conditions. We present here the guidelines for creating artificial neural network empirical interatomic potential (ANN potential) trained with such a limited FPMD data, which can perform long time scale MD simulations at least under the same conditions. The FPMD data for training are prepared on the basis of the convergence of radial distribution function [g(r)].