Induction and Ferroelectric Switching of Flux Closure Domains in Strained PbTiO3 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 PbTiO3 bulk crystal and 180° domain walls (DWs). NNQMD-BEC simulations correctly describe the nucleation-and-growth mechanism during DW switching. In triaxially strained PbTiO3 with strain conditions commonly seen in many superlattice configurations, we find that flux-closure texture can be induced with application of an electric field perpendicular to the original polarization direction. Upon field reversal, the flux-closure texture switches via a pair of transient vortices as the intermediate state, indicating an energy-efficient switching pathway. Our NNQMD-BEC method provides a theoretical guidance to study electro-mechano effects with existing machine learning force fields using a simple BEC extension, which will be relevant for engineering applications such as field-controlled switching in mechanically strained ferroelectric devices.

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

Mechanical controllability of recently discovered topological defects (e.g., 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. Here, we overcome this challenge by developing a machine-learning-based multiscale simulation framework─a hybrid neural network quantum molecular dynamics (NNQMD) and molecular mechanics (MM) method. For nanostructures composed of SrTiO3 and PbTiO3, we find how the symmetry of mechanical loading essentially controls polar topological switching. We find under symmetry-breaking uniaxial compression a squishing-to-annihilation pathway versus formation of a topological composite named skyrmionium under symmetry-preserving isotropic compression. The distinct pathways are explained in terms of the underlying materials' elasticity and symmetry, as well as the Landau-Lifshitz-Kittel scaling law. Such rational control of ferroelectric topologies will likely facilitate exploration of the rich ferroelectric "topotronics" design space.

Defect-free and crystallinity-preserving ductile deformation in semiconducting Ag2S.

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 (Ag2S), which is defect-free, omni-directional, and preserving perfect crystallinity. Our first-principles molecular dynamics simulations elucidate the ductile deformation mechanism in monoclinic-Ag2S under six types of shear systems. Planer mass movement of sulfur atoms plays an important role for the remarkable structural recovery of sulfur-sublattice. This in turn arises from a distinctively high symmetry of the anion-sublattice in Ag2S, which is not seen in other brittle silver chalcogenides. Such mechanistic and lattice-symmetric understanding provides a guideline for designing even higher-performance ductile inorganic semiconductors.

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. The resulting loss of long-range order for a higher valence electron excitation fraction is achieved without fulfilling the Lindemann criterion for melting, therefore utilizing a nonthermal path. The photoexcitation-induced structural disorder is accompanied by charge transfer from Te to Ge, Ge-Te bonding-to-antibonding, and Ge-Ge antibonding-to-bonding change, triggering Ge-Te bond breaking and promoting the formation of Ge-Ge wrong bonds. These results provide an electronic-structure basis to understand the photoexcitation-induced ultrafast changes in the structure and properties of GeTe and other phase-change materials.

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 2.24 Å. The computed lifetime of the hydrogen bond is t ≅ 0.1 ps. In contrast to crystalline water-ice, we find that hydrogen bonding is practically nonexistent in crystalline ammonia-I.

Neural Network Quantum Molecular Dynamics, Intermediate Range Order in GeSe2, 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 GeSe2 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. The system-size dependence of the FSDP height is determined by comparing QMD and NNQMD simulations with experimental data. Partial pair distribution functions, bond-angle distributions, partial and neutron structure factors, and ring-size distributions are presented. Calculated FSDP heights agree quantitatively with neutron scattering data for GeSe2 glass at 10 K and melt at 1100 K.

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. The first, NNQMD, is trained on QMD configurations for liquid water under a variety of temperature and density conditions to learn potential energy surface and forces and then perform molecular dynamics simulations. The second network, NNMLWF, is trained to predict locations of MLWF of individual molecules using the atomic configurations from NNQMD. Training data for both the neural networks is produced using a highly accurate quantum-mechanical method, DFT-SCAN that yields an excellent description of liquid water. We produce 280×10^{6} configurations of water at 7 temperatures using NNQMD and predict MLWF centers using NNMLWF to compute the polarization fluctuations. The length of trajectories needed for a converged value of the dielectric constant at 0°C is found to be 20 ns (40×10^{6} configurations with 0.5 fs time step). The computed dielectric constants for 0, 15, 30, 45, 60, 75, and 90°C are in good agreement with experiments. Our scalable scheme to compute dielectric constants with quantum accuracy is also applicable to other polar molecular liquids.

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-Ag2Se, using the interatomic potential based on an artificial neural network (ANN potential). The training data were created using the existing empirical potential of Ag2Se 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. We clarified that using a rigorous heat flux formula for the ANN potential, even for highly ionic α-Ag2Se, the resulting thermal conductivity was reasonably consistent with the reference value without explicitly considering Coulomb interaction. It was found that ANN training including the virial term played an important role in reducing the dependency of thermal conductivity on the initial values of the weight parameters of the ANN.

Optically Induced Three-Stage Picosecond Amorphization in Low-Temperature SrTiO3.

Photoexcitation can drastically modify potential energy surfaces of materials, allowing access to hidden phases. SrTiO3 (STO) is an ideal material for photoexcitation studies due to its prevalent use in nanostructured devices and its rich range of functionality-changing lattice motions. Recently, a hidden ferroelectric phase in STO was accessed through weak terahertz excitation of polarization-inducing phonon modes. In contrast, whereas strong laser excitation was shown to induce nanostructures on STO surfaces and control nanopolarization patterns in STO-based heterostructures, the dynamic pathways underlying these optically induced structural changes remain unknown. Here nonadiabatic quantum molecular dynamics reveals picosecond amorphization in photoexcited STO at temperatures as low as 10 K. The three-stage pathway involves photoinduced charge transfer and optical phonon activation followed by nonlinear charge and lattice dynamics that ultimately lead to amorphization. This atomistic understanding could guide not only rational laser nanostructuring of STO but also broader "quantum materials on demand" technologies.

Suppression of floating image degradation using a mechanical vibration of a dihedral corner reflector array.

Dihedral corner reflector arrays (DCRAs) are imaging devices that form real images and are used in a variety of applications, including floating virtual touchscreens and image presentation around physical objects. However, they induce several types of degradations to floating images. It is desirable to suppress these degradations to provide better viewing experiences. This paper proposes a method of suppressing degradations which appear as high-frequency noise by using mechanical vibration. The effects of vibrating the DCRA were confirmed through an analysis of the floating image quality in the frequency domain.

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 near-equilibrium processes. Here we combine first-principles-trained ANN-MD with multiscale shock theory (MSST) to successfully describe far-from-equilibrium shock phenomena. Our ANN-MSST-MD approach describes shock-wave propagation in solids with first-principles accuracy but a 5000 times shorter computing time. Accordingly, ANN-MD-MSST was able to resolve fine, long-time elastic deformation at low shock speed, which was impossible with first-principles MD because of the high computational cost. This work thus lays a foundation of ANN-MD simulation to study a wide range of far-from-equilibrium processes.

Field-Induced Carrier Localization Transition in Dielectric Polymers.

Organic polymers offer many advantages as dielectric materials over their inorganic counterparts because of high flexibility and cost-effective processing, but their application is severely limited by breakdown in the presence of high electric fields. Dielectric breakdown is commonly understood as the result of avalanche processes such as carrier multiplication and defect generation that are triggered by field-accelerated hot carriers (electrons or holes). In stark contrast to inorganic dielectric materials, however, there remains no mechanistic understanding to enable quantitative prediction of the breakdown field in polymers. Here, we perform systematic study of different electric fields on hot carrier dynamics and resulting chemical damage in a slab of archetypal polymer, polyethylene, using nonadiabatic quantum molecular dynamics simulations. We found that high electric fields induce localized electronic states at the slab surface, with a critical transition occurring near the experimentally reported intrinsic breakdown field. This transition in turn facilitates strong polaronic coupling between charge carriers and atoms, which is manifested by severe damping of the time evolution of localized states and the presence of C-H vibrational resonance in the hot-carrier motion leading to rapid carbon-carbon bond breaking on the surface. Such polaronic localization transition may provide a critically missing prediction method for computationally screening dielectric polymers with high breakdown fields.

Guidelines for creating artificial neural network empirical interatomic potential from first-principles molecular dynamics data under specific conditions and its application to α-Ag2Se.

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)]. While training the ANN using total energy and atomic forces of the FPMD data, the error of pressure is also monitored and minimized. To create further robust potential, we add a small amount of FPMD data to reproduce the interaction between two atoms that are close to each other. ANN potentials for α-Ag2Se were created as an application example, and it has been confirmed that not only g(r) and mean square displacements but also the specific heat requiring a long time scale simulation matched the FPMD and the experimental values. In addition, the MD simulation using the ANN potential achieved over 104 acceleration over the FPMD one. The guidelines proposed here mitigate the creation difficulty of the ANN potential, and a lot of FPMD data sleeping on the hard disk after the research may be put on the front stage again.

Hot-Carrier Dynamics and Chemistry in Dielectric Polymers.

Dielectric polymers are widely used in electronics and energy technologies, but their performance is severely limited by the electrical breakdown under a high electric field. Dielectric breakdown is commonly understood as an avalanche of processes such as carrier multiplication and defect generation that are triggered by field-accelerated hot electrons and holes. However, how these processes are initiated remains elusive. Here, nonadiabatic quantum molecular dynamics simulations reveal microscopic processes induced by hot electrons and holes in a slab of an archetypal dielectric polymer, polyethylene, under an electric field of 600 MV/m. We found that electronic-excitation energy is rapidly dissipated within tens of femtoseconds because of strong electron-phonon scattering, which is consistent with quantum-mechanical perturbation calculations. This in turn excites other electron-hole pairs to cause carrier multiplication. We also found that the key to chemical damage is localization of holes that travel to a slab surface and weaken carbon-carbon bonds on the surface. Such quantitative information can be incorporated into first-principles-informed, predictive modeling of dielectric breakdown.

Positive natural selection of TRIB2, a novel gene that influences visceral fat accumulation, in East Asia.

Accumulation of visceral fat increases cardiovascular mortality in industrialized societies. However, during the evolution of the modern human, visceral fat may have acted as energy storage facility to survive in times of famine. Therefore, past natural selection might contribute to shaping the variation of visceral fat accumulation in present populations. Here, we report that the gene encoding tribbles homolog 2 (TRIB2) influenced visceral fat accumulation and was operated by recent positive natural selection in East Asians. Our candidate gene association analysis on 11 metabolic traits of 5,810 East Asians revealed that rs1057001, a T/A transversion polymorphism in 3'untranslated region (UTR) of TRIB2, was strongly associated with visceral fat area (VFA) and waist circumference adjusted for body mass index (P = 2.7 × 10(-6) and P = 9.0 × 10(-6), respectively). rs1057001 was in absolute linkage disequilibrium with a conserved insertion-deletion polymorphism in the 3'UTR and was associated with allelic imbalance of TRIB2 transcript levels in adipose tissues. rs1057001 showed high degree of interpopulation variation of the allele frequency; the low-VFA-associated A allele was found with high frequencies in East Asians. Haplotypes containing the rs1057001 A allele exhibited a signature of a selective sweep, which may have occurred 16,546-27,827 years ago in East Asians. Given the predominance of the thrifty gene hypothesis, it is surprising that the apparently non-thrifty allele was selectively favored in the evolution of modern humans. Environmental/physiological factors other than famine would be needed to explain the non-neutral evolution of TRIB2 in East Asians.