Neutron scattering and neural-network quantum molecular dynamics investigation of the vibrations of ammonia along the solid-to-liquid transition

Vibrational spectroscopy allows us to understand complex physical and chemical interactions of molecular crystals and liquids such as ammonia, which has recently emerged as a strong hydrogen fuel candidate to support a sustainable society. We report inelastic neutron scattering measurement of vibrational properties of ammonia along the solid-to-liquid phase transition with high enough resolution for direct comparisons to ab-initio simulations. Theoretical analysis reveals the essential role of nuclear quantum effects (NQEs) for correctly describing the intermolecular spectrum as well as high energy intramolecular N-H stretching modes. This is achieved by training neural network models using ab-initio path-integral molecular dynamics (PIMD) simulations, thereby encompassing large spatiotemporal trajectories required to resolve low energy dynamics while retaining NQEs. Our results not only establish the role of NQEs in ammonia but also provide general computational frameworks to study complex molecular systems with NQEs.

Scalable computation of anisotropic vibrations for large macromolecular assemblies

The Normal Mode Analysis (NMA) is a standard approach to elucidate the anisotropic vibrations of macromolecules at their folded states, where low-frequency collective motions can reveal rearrangements of domains and changes in the exposed surface of macromolecules. Recent advances in structural biology have enabled the resolution of megascale macromolecules with millions of atoms. However, the calculation of their vibrational modes remains elusive due to the prohibitive cost associated with constructing and diagonalizing the underlying eigenproblem and the current approaches to NMA are not readily adaptable for efficient parallel computing on graphic processing unit (GPU). Here, we present eigenproblem construction and diagonalization approach that implements level-structure bandwidth-reducing algorithms to transform the sparse computation in NMA to a globally-sparse-yet-locally-dense computation, allowing batched tensor products to be most efficiently executed on GPU. We map, optimize, and compare several low-complexity Krylov-subspace eigensolvers, supplemented by techniques such as Chebyshev filtering, sum decomposition, external explicit deflation and shift-and-inverse, to allow fast GPU-resident calculations. The method allows accurate calculation of the first 1000 vibrational modes of some largest structures in PDB ( > 2.4 million atoms) at least 250 times faster than existing methods.

Alkali hydroxide (LiOH, NaOH, KOH) in water: Structural and vibrational properties, including neutron scattering results

Structural and vibrational properties of aqueous solutions of alkali hydroxides (LiOH, NaOH, and KOH) are computed using quantum molecular dynamics simulations for solute concentrations ranging between 1 and 10M. Element-resolved partial radial distribution functions, neutron and x-ray structure factors, and angular distribution functions are computed for the three hydroxide solutions as a function of concentration. The vibrational spectra and frequency-dependent conductivity are computed from the Fourier transforms of velocity autocorrelation and current autocorrelation functions. Our results for the structure are validated with the available neutron data for 17M concentration of NaOH in water [Semrouni et al., Phys. Chem. Chem. Phys. 21, 6828 (2019)]. We found that the larger ionic radius [rLi+ Collapse

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Affiliation(s)

Ruru Ma Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90007-0242, USA
Nitish Baradwaj Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90007-0242, USA
Ken-Ichi Nomura Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90007-0242, USA
Aravind Krishnamoorthy Department of Mechanical Engineering, Texas A&M University, College Station, Texas 77843-3123, USA
Rajiv K Kalia Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90007-0242, USA
Aiichiro Nakano Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90007-0242, USA
Priya Vashishta Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90007-0242, USA

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Surface Transfer Doping in MoO3-x/Hydrogenated Diamond Heterostructure

Surface transfer doping is proposed to be a potential solution for doping diamond, which is hard to dope for applications in high-power electronics. While MoO3 is found to be an effective surface electron acceptor for hydrogen-terminated diamond with a negative electron affinity, the effects of commonly existing oxygen vacancies remain elusive. We have performed reactive molecular dynamics simulations to study the deposition of MoO3-x on a hydrogenated diamond (111) surface and used first-principles calculations based on density functional theory to investigate the electronic structures and charge transfer mechanisms. We find that MoO3-x is an effective surface electron acceptor and the spatial extent of doped holes in hydrogenated diamond is extended, promoting excellent transport properties. Charge transfer is found to monotonically decrease with the level of oxygen vacancy, providing guidance for engineering of the surface transfer doping process.

Inelastic Neutron Scattering Study of Phonon Density of States of Iodine Oxides and First-Principles Calculations

Iodine oxides I2Oy (y = 4, 5, 6) crystallize into atypical structures that fall between molecular- and framework-base types and exhibit high reactivity in an ambient environment, a property highly desired in the so-called "agent defeat materials". Inelastic neutron scattering experiments were performed to determine the phonon density of states of the newly synthesized I2O5 and I2O6 samples. First-principles calculations were carried out for I2O4, I2O5, and I2O6 to predict their thermodynamic properties and phonon density of states. Comparison of the INS data with the Raman and infrared measurements as well as the first-principles calculations sheds light on their distinctive, anisotropic thermomechanical properties.

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.

Pressure-Controlled Layer-by-Layer to Continuous Oxidation of ZrS2(001) Surface

Understanding oxidation mechanisms of layered semiconducting transition-metal dichalcogenides (TMDC) is important not only for controlling native oxide formation but also for synthesis of oxide and oxysulfide products. Here, reactive molecular dynamics simulations show that oxygen partial pressure controls not only the ZrS₂ oxidation rate but also the oxide morphology and quality. We find a transition from layer-by-layer oxidation to amorphous-oxide-mediated continuous oxidation as the oxidation progresses, where different pressures selectively expose different oxidation stages within a given time window. While the kinetics of the fast continuous oxidation stage is well described by the conventional Deal-Grove model, the layer-by-layer oxidation stage is dictated by reactive bond-switching mechanisms. This work provides atomistic details and a potential foundation for rational pressure-controlled oxidation of TMDC materials.

Tailoring the Angular Mismatch in MoS2 Homobilayers through Deformation Fields

Ultrathin MoS₂ has shown remarkable characteristics at the atomic scale with an immutable disorder to weak external stimuli. Ion beam modification unlocks the potential to selectively tune the size, concentration, and morphology of defects produced at the site of impact in 2D materials. Combining experiments, first-principles calculations, atomistic simulations, and transfer learning, it is shown that irradiation-induced defects can induce a rotation-dependent moiré pattern in vertically stacked homobilayers of MoS₂ by deforming the atomically thin material and exciting surface acoustic waves (SAWs). Additionally, the direct correlation between stress and lattice disorder by probing the intrinsic defects and atomic environments are demonstrated. The method introduced in this paper sheds light on how engineering defects in the lattice can be used to tailor the angular mismatch in van der Waals (vdW) solids.

Wrinkles, Ridges, Miura-Ori, and Moiré Patterns in MoSe2 Using Neural Networks

Effects of lateral compression on out-of-plane deformation of two-dimensional MoSe₂ layers are investigated. A MoSe₂ monolayer develops periodic wrinkles under uniaxial compression and Miura-Ori patterns under biaxial compression. When a flat MoSe₂ monolayer is placed on top of a wrinkled MoSe₂ layer, the van der Waals (vdW) interaction transforms wrinkles into ridges and generates mixed 2H and 1T phases and chain-like defects. Under a biaxial strain, the vdW interaction induces regions of Miura-Ori patterns in bilayers. Strained systems analyzed using a convolutional neural network show that the compressed system consists of semiconducting 2H and metallic 1T phases. The energetics, mechanical response, defect structure, and dynamics are analyzed as bilayers undergo wrinkle-ridge transformations under uniaxial compression and moiré transformations under biaxial compression. Our results indicate that in-plane compression can induce self-assembly of out-of-plane metasurfaces with controllable semiconducting and metallic phases and moiré patterns with unique optoelectronic properties.

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.

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.

Towards computational polar-topotronics: Multiscale neural-network quantum molecular dynamics simulations of polar vortex states in SrTiO3/PbTiO3 nanowires

Recent discoveries of polar topological structures (e.g., skyrmions and merons) in ferroelectric/paraelectric heterostructures have opened a new field of polar topotronics. However, how complex interplay of photoexcitation, electric field and mechanical strain controls these topological structures remains elusive. To address this challenge, we have developed a computational approach at the nexus of machine learning and first-principles simulations. Our multiscale neural-network quantum molecular dynamics molecular mechanics approach achieves orders-of-magnitude faster computation, while maintaining quantum-mechanical accuracy for atoms within the region of interest. This approach has enabled us to investigate the dynamics of vortex states formed in PbTiO3 nanowires embedded in SrTiO3. We find topological switching of these vortex states to topologically trivial, uniformly polarized states using electric field and trivial domain-wall states using shear strain. These results, along with our earlier results on optical control of polar topology, suggest an exciting new avenue toward opto-electro-mechanical control of ultrafast, ultralow-power polar topotronic devices.

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.

Anisotropic atomistic shock response mechanisms of aramid crystals

Aramid fibers composed of poly(p-phenylene terephthalamide) (PPTA) polymers are attractive materials due to their high strength, low weight, and high shock resilience. Even though they have widely been utilized as a basic ingredient in Kevlar, Twaron, and other fabrics and applications, their intrinsic behavior under intense shock loading is still to be understood. In this work, we characterize the anisotropic shock response of PPTA crystals by performing reactive molecular dynamics simulations. Results from shock loading along the two perpendicular directions to the polymer backbones, [100] and [010], indicate distinct shock release mechanisms that preserve and destroy the hydrogen bond network. Shocks along the [100] direction for particle velocity U_p < 2.46 km/s indicate the formation of a plastic regime composed of shear bands, where the PPTA structure is planarized. Shocks along the [010] direction for particle velocity U_p < 2.18 km/s indicate a complex response regime, where elastic compression shifts to amorphization as the shock is intensified. While hydrogen bonds are mostly preserved for shocks along the [100] direction, hydrogen bonds are continuously destroyed with the amorphization of the crystal for shocks along the [010] direction. Decomposition of the polymer chains by cross-linking is triggered at the threshold particle velocity U_p = 2.18 km/s for the [010] direction and U_p = 2.46 km/s for the [100] direction. These atomistic insights based on large-scale simulations highlight the intricate and anisotropic mechanisms underpinning the shock response of PPTA polymers and are expected to support the enhancement of their applications.

Neural Network for Principle of Least Action

The principle of least action is the cornerstone of classical mechanics, theory of relativity, quantum mechanics, and thermodynamics. Here, we describe how a neural network (NN) learns to find the trajectory for a Lennard-Jones (LJ) system that maintains balance in minimizing the Onsager–Machlup (OM) action and maintaining the energy conservation. The phase-space trajectory thus calculated is in excellent agreement with the corresponding results from the “ground-truth” molecular dynamics (MD) simulation. Furthermore, we show that the NN can easily find structural transformation pathways for LJ clusters, for example, the basin-hopping transformation of an LJ₃₈ from an incomplete Mackay icosahedron to a truncated face-centered cubic octahedron. Unlike MD, the NN computes atomic trajectories over the entire temporal domain in one fell swoop, and the NN time step is a factor of 20 larger than the MD time step. The NN approach to OM action is quite general and can be adapted to model morphometrics in a variety of applications.

Probing the presence and absence of metal-fullerene electron transfer reactions in helium nanodroplets by deflection measurements

Metal-fullerene compounds are characterized by significant electron transfer to the fullerene cage, giving rise to an electric dipole moment. We use the method of electrostatic beam deflection to verify whether such reactions take place within superfluid helium nanodroplets between an embedded C₆₀ molecule and either alkali (heliophobic) or rare-earth (heliophilic) atoms. The two cases lead to distinctly different outcomes: C₆₀Na_n (n = 1-4) display no discernable dipole moment, while C₆₀Yb is strongly polar. This suggests that the fullerene and small alkali clusters fail to form a charge-transfer bond in the helium matrix despite their strong van der Waals attraction. The C₆₀Yb dipole moment, on the other hand, is in agreement with the value expected for an ionic complex.

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. The coupling and frustration of these dynamics, in turn, create topological defects in the form of polar strings. The demonstrated nexus of multiscale quantum simulation and machine learning will boost not only the emerging field of ferroelectric topotronics but also broader optoelectronic applications.

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. Nonadiabatic quantum molecular dynamics simulations demonstrate that hBN layers at the polymer-electrode interfaces can trap hot carriers, elucidating the observed increase in the breakdown field. The trapping of hot carriers is due to a deep potential well formed in the hBN layers at the polymer-electrode interface. Searching for materials with similar deep well potential profiles could lead to a computationally efficient way to design good polymer coatings that can mitigate breakdown.

Review of strategies toward the development of alloy two-dimensional (2D) transition metal dichalcogenides

Atomically thin two-dimensional (2D) transition metal dichalcogenides (TMDCs) have attracted significant attention owing to their prosperity in material research. The inimitable features of TMDCs triggered the emerging applications in diverse areas. In this review, we focus on the tailored and engineering of the crystal lattice of TMDCs that finally enhance the efficiency of the material properties. We highlight several preparation techniques and recent advancements in compositional engineering of TMDCs structure. We summarize different approaches for TMDCs such as doping and alloying with different materials, alloying with other 2D metals, and scrutinize the technological potential of these methods. Beyond that, we also highlight the recent significant advancement in preparing 2D quasicrystals and alloying the 2D TMDCs with MAX phases. Finally, we highlight the future perspectives for crystal engineering in TMDC materials for structure stability, machine learning concept marge with materials, and their emerging applications.

Electric-field-induced crossover of polarization reversal mechanisms in Al1-xScxN ferroelectrics

Scandium-doped aluminum nitride, Al_1-xSc_xN, represents a new class of displacive ferroelectric materials with high polarization and sharp hysteresis along with high-temperature resilience, facile synthesizability and compatibility with standard CMOS fabrication techniques. The fundamental physics behind the transformation of unswitchable piezoelectric AlN into switchable Al-Sc-N ferroelectrics depends upon important atomic properties such as local structure, dopant distributions and the presence of competing mechanism of polarization switching in the presence of an applied electric-field that have not been understood. We computationally synthesize Al_1-xSc_xN to quantify the inhomogeneity of Sc distribution and phase segregation, and characterize its crystal and electronic structure as a function of Sc-doping. Nudged elastic band calculations of the potential energy surface and quantum molecular dynamics simulations of direct electric-field-driven ferroelectric switching reveal a crossover between two polarization reversal mechanisms-inhomogeneous nucleation-and-growth mechanism originating near Sc-rich regions in the limit of low applied fields and nucleation-limited-switching in the high-field regime. Understanding polarization reversal pathways for these two mechanisms as well as the role of local Sc concentration on activation barriers provides design rules to identify other combinations of dopant elements, such as Zr, Mg etc. to synthesize superior AlN-based ferroelectric materials.

Cardiac rehabilitation for patients with heart failure: a national Danish register-based study of predictors of referral and outcomes

Funding Acknowledgements

Type of funding sources: Foundation. Main funding source(s): The Danish Heart Foundation

Background

 Heart failure (HF) places a large burden on patients and society as a major cause of morbidity, mortality and healthcare costs. Participation in exercise-based cardiac rehabilitation (CR) in people with HF is a clinically and cost-effective strategy and recommended in international clinical guidelines.

Purpose

The aims of this study were to: (1) examine the temporal trends and predictors of national CR referral, and (2) compare the risk of hospital readmission and mortality in those referred for CR compared to no referral.

Methods

All patients in Denmark with incident HF were identified by the Danish Heart Failure Register in the period 2010 to 2018 (n = 33,257) and CR referral assessed within 120 days of hospital admission. Multivariable logistic regression models were used to evaluate the association between CR referral and predictors and to compare risk of hospital readmission and mortality until 1 year between referred and not referred patients.

Results

Overall, 45.0% of HF patients were referred to exercise-based CR, increasing from 31.7% in 2010 to 52.2% in 2018. Factors independently associated with higher CR referral were: NYHA functional class II, LVEF &lt;50%, diagnosis of myocardial infarction and use of ACE inhibitor. Male gender, older age, region, unemployment, retirement, living alone, non-Danish ethnic origin, lower educational level, NYHA class IV, treatment for hypertension, existing chronic obstructive lung disease and stroke were associated with lower CR referral. CR referral was associated with lower risk of readmission (adjusted odds ratio: 0.90;95%CI: 0.85-0.95), HF-specific mortality (0.61; 0.39-0.95) and all-cause mortality (0.61; 0.55-0.69) as compared to no referral.

Conclusions

Although CR referral has increased over time, only some 1 in 2 diagnosed HF patients in Denmark are referred to exercise-based CR. CR referral is associated with lower risk in readmissions and mortality. Strategies to promote CR referral including healthcare professional education on the benefits of CR and alternative methods of CR delivery are urgently needed to improve access to CR, especially for high-risk groups.

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.

Dielectric Polymer Property Prediction Using Recurrent Neural Networks with Optimizations

Despite the growing success of machine learning for predicting structure-property relationships in molecules and materials, such as predicting the dielectric properties of polymers, it is still in its infancy. We report on the effectiveness of solving structure-property relationships for a computer-generated database of dielectric polymers using recurrent neural network (RNN) models. The implementation of a series of optimization strategies was crucial to achieving high learning speeds and sufficient accuracy: (1) binary and nonbinary representations of SMILES (Simplified Molecular Input Line System) fingerprints and (2) backpropagation with affine transformation of the input sequence (ATransformedBP) and resilient backpropagation with initial weight update parameter optimizations (iRPROP^- optimized). For the investigated database of polymers, the binary SMILES representation was found to be superior to the decimal representation with respect to the training and prediction performance. All developed and optimized Elman-type RNN algorithms outperformed nonoptimized RNN models in the efficient prediction of nonlinear structure-activity relationships. The average relative standard deviation (RSD) remained well below 5%, and the maximum RSD did not exceed 30%. Moreover, we provide a C++ codebase as a testbed for a new generation of open programming languages that target increasingly diverse computer architectures.

Sulfurization of MoO3 in the Chemical Vapor Deposition Synthesis of MoS2 Enhanced by an H2S/H2 Mixture

The typical layered transition metal dichalcogenide (TMDC) material, MoS₂, 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 MoO₃ powders is an essential reaction step in which the MoO₃ reactants are converted into MoS₂ products. Recent studies have suggested using an H₂S/H₂ mixture to reduce MoO₃ powders in an effective way. However, reaction mechanisms associated with the sulfurization of MoO₃ by the H₂S/H₂ mixture are yet to be fully understood. Here, we perform quantum molecular dynamics (QMD) simulations to investigate the sulfurization of MoO₃ flakes using two different gaseous environments: pure H₂S precursors and a H₂S/H₂ mixture. Our QMD results reveal that the H₂S/H₂ mixture could effectively reduce and sulfurize the MoO₃ reactants through additional reactions of H₂ and MoO₃, thereby providing valuable input for experimental synthesis of higher-quality TMDC materials.

Lattice thermal transport in two-dimensional alloys and fractal heterostructures

Engineering thermal transport in two dimensional materials, alloys and heterostructures is critical for the design of next-generation flexible optoelectronic and energy harvesting devices. Direct experimental characterization of lattice thermal conductivity in these ultra-thin systems is challenging and the impact of dopant atoms and hetero-phase interfaces, introduced unintentionally during synthesis or as part of deliberate material design, on thermal transport properties is not understood. Here, we use non-equilibrium molecular dynamics simulations to calculate lattice thermal conductivity of [Formula: see text] monolayer crystals including [Formula: see text] alloys with substitutional point defects, periodic [Formula: see text] heterostructures with characteristic length scales and scale-free fractal [Formula: see text] heterostructures. Each of these features has a distinct effect on phonon propagation in the crystal, which can be used to design fractal and periodic alloy structures with highly tunable thermal conductivities. This control over lattice thermal conductivity will enable applications ranging from thermal barriers to thermoelectrics.

Carrier-specific dynamics in 2H-MoTe2 observed by femtosecond soft x-ray absorption spectroscopy using an x-ray free-electron laser

Femtosecond carrier dynamics in layered 2H-MoTe2 semiconductor crystals have been investigated using soft x-ray transient absorption spectroscopy at the x-ray free-electron laser (XFEL) of the Pohang Accelerator Laboratory. Following above-bandgap optical excitation of 2H-MoTe2, the photoexcited hole distribution is directly probed via short-lived transitions from the Te 3d 5/2 core level (M5-edge, 572-577 eV) to transiently unoccupied states in the valence band. The optically excited electrons are separately probed via the reduced absorption probability at the Te M5-edge involving partially occupied states of the conduction band. A 400 ± 110 fs delay is observed between this transient electron signal near the conduction band minimum compared to higher-lying states within the conduction band, which we assign to hot electron relaxation. Additionally, the transient absorption signals below and above the Te M5 edge, assigned to photoexcited holes and electrons, respectively, are observed to decay concomitantly on a 1-2 ps timescale, which is interpreted as electron-hole recombination. The present work provides a benchmark for applications of XFELs for soft x-ray absorption studies of carrier-specific dynamics in semiconductors, and future opportunities enabled by this method are discussed.

Growth Kinetics and Atomistic Mechanisms of Native Oxidation of ZrSxSe2-x and MoS2 Crystals

A thorough understanding of native oxides is essential for designing semiconductor devices. Here, we report a study of the rate and mechanisms of spontaneous oxidation of bulk single crystals of ZrS_xSe_2-x alloys and MoS₂. ZrS_xSe_2-x alloys oxidize rapidly, and the oxidation rate increases with Se content. Oxidation of basal surfaces is initiated by favorable O₂ adsorption and proceeds by a mechanism of Zr-O bond switching, that collapses the van der Waals gaps, and is facilitated by progressive redox transitions of the chalcogen. The rate-limiting process is the formation and out-diffusion of SO₂. In contrast, MoS₂ basal surfaces are stable due to unfavorable oxygen adsorption. Our results provide insight and quantitative guidance for designing and processing semiconductor devices based on ZrS_xSe_2-x and MoS₂ and identify the atomistic-scale mechanisms of bonding and phase transformations in layered materials with competing anions.

Photoexcitation Induced Ultrafast Nonthermal Amorphization in Sb2Te3

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, Sb₂Te₃ displays a fast and energy-efficient crystallization-amorphization cycle due to its growth-dominated crystallization and low melting point. This growth-dominated crystallization contrasts with the nucleation-dominated crystallization of Ge₂Sb₂Te₅. Here, we show that the energy required for and the time associated with the amorphization process can be further reduced by using a photoexcitation-based nonthermal path. We employ nonadiabatic quantum molecular dynamics simulations to investigate the time evolution of Sb₂Te₃ with 2.6, 5.2, 7.5, 10.3, and 12.5% photoexcited valence electron-hole carriers. Results reveal that the degree of amorphization increases with excitation, saturating at 10.3% excitation. The rapid amorphization originates from an instantaneous charge transfer from Te-p orbitals to Sb-p orbitals upon photoexcitation. Subsequent evolution of the excited state, within the picosecond time scale, indicates an Sb-Te bonding to antibonding transition. Concurrently, Sb-Sb and Te-Te antibonding decreases, leading to formation of wrong bonds. For photoexcitation of 7.5% valence electrons or larger, the electronic changes destabilize the crystal structure, leading to large atomic diffusion and irreversible loss of long-range order. These results highlight an ultrafast energy-efficient amorphization pathway that could be used to enhance the performance of phase change material-based optoelectronic devices.

Simultaneous Observation of Carrier-Specific Redistribution and Coherent Lattice Dynamics in 2H-MoTe2 with Femtosecond Core-Level Spectroscopy

We employ few-femtosecond extreme ultraviolet (XUV) transient absorption spectroscopy to reveal simultaneously the intra- and interband carrier relaxation and the light-induced structural dynamics in nanoscale thin films of layered 2H-MoTe₂ semiconductor. By interrogating the valence electronic structure via localized Te 4d (39-46 eV) and Mo 4p (35-38 eV) core levels, the relaxation of the photoexcited hole distribution is directly observed in real time. We obtain hole thermalization and cooling times of 15 ± 5 fs and 380 ± 90 fs, respectively, and an electron-hole recombination time of 1.5 ± 0.1 ps. Furthermore, excitations of coherent out-of-plane A_1g (5.1 THz) and in-plane E_1g (3.7 THz) lattice vibrations are visualized through oscillations in the XUV absorption spectra. By comparison to Bethe-Salpeter equation simulations, the spectral changes are mapped to real-space excited-state displacements of the lattice along the dominant A_1g coordinate. By directly and simultaneously probing the excited carrier distribution dynamics and accompanying femtosecond lattice displacement in 2H-MoTe₂ within a single experiment, our work provides a benchmark for understanding the interplay between electronic and structural dynamics in photoexcited nanomaterials.

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.

Atomistic Simulations of Biofouling and Molecular Transfer of a Cross-linked Aromatic Polyamide Membrane for Desalination

Reverse osmosis through a polyamide (PA) membrane is an important technique for water desalination and purification. In this study, molecular dynamics simulations were performed to study the biofouling mechanism (i.e., protein adsorption) and nonequilibrium steady-state water transfer of a cross-linked PA membrane. Our results demonstrated that the PA membrane surface's roughness is a key factor of surface's biofouling, as the lysozyme protein adsorbed on the surface's cavity site displays extremely low surface diffusivity, blocking water passage, and decreasing water flux. The adsorbed protein undergoes secondary structural changes, particularly in the pressure-driven flowing conditions, leading to strong protein-surface interactions. Our simulations were able to present water permeation close to the experimental conditions with a pressure difference as low as 5 MPa, while all the electrolytes, which are tightly surrounded by hydration water, were effectively rejected at the membrane surfaces. The analysis of the self-intermediate scattering function demonstrates that the dynamics of water molecules coordinated with hydrogen bonds is faster inside the pores than during the translation across the pores. The pressure difference applied shows a negligible effect on the water structure and content inside the membrane but facilitates the transportation of hydrogen-bonded water molecules through the membrane's sub-nanopores with a reduced coordination number. The linear relationship between the water flux and the pressure difference demonstrates the applicability of continuum hydrodynamic principles and thus the stability of the membrane structure.

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.

Synergistically Chemical and Thermal Coupling between Graphene Oxide and Graphene Fluoride for Enhancing Aluminum Combustion

Metal combustion reaction is highly exothermic and is used in energetic applications, such as propulsion, pyrotechnics, powering micro- and nano-devices, and nanomaterials synthesis. Aluminum (Al) is attracting great interest in those applications because of its high energy density, earth abundance, and low toxicity. Nevertheless, Al combustion is hard to initiate and progresses slowly and incompletely. On the other hand, ultrathin carbon nanomaterials, such as graphene, graphene oxide (GO), and graphene fluoride (GF), can also undergo exothermic reactions. Herein, we demonstrate that the mixture of GO and GF significantly improves the performance of Al combustion as interactions between GO and GF provide heat and radicals to accelerate Al oxidation. Our experiments and reactive molecular dynamics simulation reveal that GO and GF have strong chemical and thermal couplings through radical reactions and heat released from their oxidation reactions. GO facilitates the dissociation of GF, and GF accelerates the disproportionation and oxidation of GO. When the mixture of GO and GF is added to micron-sized Al particles, their synergistic couplings generate reactive oxidative species, such as CF_x and CF_xO_y, and heat, which greatly accelerates Al combustion. This work demonstrates a new area of using synergistic couplings between ultrathin carbon nanomaterials to accelerate metal combustion and potentially oxidation reactions of other materials.

Tellurene Photodetector with High Gain and Wide Bandwidth

Two-dimensional (2D) semiconductors have been extensively explored as a new class of materials with great potential. In particular, black phosphorus (BP) has been considered to be a strong candidate for applications such as high-performance infrared photodetectors. However, the scalability of BP thin film is still a challenge, and its poor stability in the air has hampered the progress of the commercialization of BP devices. Herein, we report the use of hydrothermal-synthesized and air-stable 2D tellurene nanoflakes for broadband and ultrasensitive photodetection. The tellurene nanoflakes show high hole mobilities up to 458 cm²/V·s at ambient conditions, and the tellurene photodetector presents peak extrinsic responsivity of 383 A/W, 19.2 mA/W, and 18.9 mA/W at 520 nm, 1.55 μm, and 3.39 μm light wavelength, respectively. Because of the photogating effect, high gains up to 1.9 × 10³ and 3.15 × 10⁴ are obtained at 520 nm and 3.39 μm wavelength, respectively. At the communication wavelength of 1.55 μm, the tellurene photodetector exhibits an exceptionally high anisotropic behavior, and a large bandwidth of 37 MHz is obtained. The photodetection performance at different wavelength is further supported by the corresponding quantum molecular dynamics (QMD) simulations. Our approach has demonstrated the air-stable tellurene photodetectors that fully cover the short-wave infrared band with ultrafast photoresponse.

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.

Electrostrictive Cavitation in Water Induced by a SnO2 Nanoparticle

Cavitation phenomenon in dielectric fluids has been a recent topic of interest in theory and experiment. We study a dielectric fluid-nanoparticle system subjected to an external electric field using molecular dynamics simulations. Electric fields ranging from 0.042 to 0.25 V/Å are applied to a water and tin dioxide system. Cavitation is observed in simulations with both SPC/E water and the hydrogen bonding polarizable model. The cavitation onset time displays a stretched exponential relaxation response with respect to the applied electric field with an exponent β = 0.423 ± 0.08. This is in accordance with the exact theoretical value for systems with long-ranged forces. Cavity growth rates are divided into two phases, a spherical growth phase and a cylindrical one. Both are reported as a function of the applied electric field. The structure of the electric field is analyzed both spatially and temporally.

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

Ab initio molecular dynamics simulations of shock loading on poly(p-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 trans-to-cis conformational change of polymer chains. In contrast to the latter, the former mechanism preserves the hydrogen bonding and cohesiveness of polymer chains in the identified novel crystalline phase preserving the strength of PPTA. The interplay between hydrogen bond preserving (SPT) and nonpreserving (planar amorphization) shock release mechanisms is critical to understanding the shock performance of aramid fibers.

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.

Phonon-Suppressed Auger Scattering of Charge Carriers in Defective Two-Dimensional Transition Metal Dichalcogenides

Two-dimensional transition metal dichalcogenides (TMDs) draw strong interest in materials science, with applications in optoelectronics and many other fields. Good performance requires high carrier concentrations and long lifetimes. However, high concentrations accelerate energy exchange between charged particles by Auger-type processes, especially in TMDs where many-body interactions are strong, thus facilitating carrier trapping. We report time-resolved optical pump-THz probe measurements of carrier lifetimes as a function of carrier density. Surprisingly, the lifetime reduction with increased density is very weak. It decreases only by 20% when we increase the pump fluence 100 times. This unexpected feature of the Auger process is rationalized by our time-domain ab initio simulations. The simulations show that phonon-driven trapping competes successfully with the Auger process. On the one hand, trap states are relatively close to band edges, and phonons accommodate efficiently the electronic energy during the trapping. On the other hand, trap states localize around defects, and the overlap of trapped and free carriers is small, decreasing carrier-carrier interactions. At low carrier densities, phonons provide the main charge trapping mechanism, decreasing carrier lifetimes compared to defect-free samples. At high carrier densities, phonons suppress Auger processes and lower the dependence of the trapping rate on carrier density. Our results provide theoretical insights into the diverse roles played by phonons and Auger processes in TMDs and generate guidelines for defect engineering to improve device performance at high carrier densities.

Two-Dimensional Lateral Epitaxy of 2H (MoSe2)-1T' (ReSe2) Phases

Two-dimensional (2D) transition metal dichalcogenide (TMDC) heterostructures have been proposed as potential candidates for a variety of applications like quantum computing, neuromorphic computing, solar cells, and flexible field effective transistors. The 2D TMDC heterostructures at the present stage face difficulties being implemented in these applications because of lack of large and sharp heterostructure interfaces. Herein, we address this problem via a CVD technique to grow thermodynamically stable heterostructure of 2H/1T' MoSe₂-ReSe₂ using conventional transition metal phase diagrams as a reference. We demonstrate how the thermodynamics of mixing in the MoReSe₂ system during CVD growth dictates the formation of atomically sharp interfaces between MoSe₂ and ReSe₂, which can be confirmed by high-resolution scanning transmission electron microscopy imaging, revealing zigzag selenium-terminated interface between the epitaxial 2H and 1T' lattices. Our work provides useful insights for understanding the stability of 2D heterostructures and interfaces between chemically, structurally, and electronically different phases.

Optical Control of Non-Equilibrium Phonon Dynamics

The light-induced selective population of short-lived far-from-equilibrium vibration modes is a promising approach for controlling ultrafast and irreversible structural changes in functional nanomaterials. However, this requires a detailed understanding of the dynamics and evolution of these phonon modes and their coupling to the excited-state electronic structure. Here, we combine femtosecond mega-electronvolt electron diffraction experiments on a prototypical layered material, MoTe₂, with non-adiabatic quantum molecular dynamics simulations and ab initio electronic structure calculations to show how non-radiative energy relaxation pathways for excited electrons can be tuned by controlling the optical excitation energy. We show how the dominant intravalley and intervalley scattering mechanisms for hot and band-edge electrons leads to markedly different transient phonon populations evident in electron diffraction patterns. This understanding of how tuning optical excitations affect phonon populations and atomic motion is critical for efficiently controlling light-induced structural transitions of optoelectronic devices.

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.

Nanoindentation on Monolayer MoS2 Kirigami

Mechanical properties of materials can be altered significantly by the ancient art of kirigami. We study the mechanical properties of atomically thin kirigami membranes of MoS₂ using molecular dynamics simulations. Nanoindentation simulations are performed to study the mechanical response of rectangular and hexagonal kirigami structures. Dramatic changes are observed in the ductility of monolayer kirigami MoS₂ compared with those of a pristine MoS₂ monolayer. Load-displacement curves of kirigami structures exhibit negligible hysteresis, and kirigami structures display remarkable elastic recovery upon unloading. Defects formed at the edges and corners of kirigami structures play an important role in the mechanical response of the membranes.

Defect Healing in Layered Materials: A Machine Learning-Assisted Characterization of MoS2 Crystal Phases

Monolayer MoS₂ is an outstanding candidate for a next-generation semiconducting material because of its exceptional physical, chemical, and mechanical properties. To make this promising layered material applicable to nanostructured electronic applications, synthesis of a highly crystalline MoS₂ monolayer is vitally important. Among different types of synthesis methods, chemical vapor deposition (CVD) is the most practical way to synthesize few- or mono-layer MoS₂ on the target substrate owing to its simplicity and scalability. However, synthesis of a highly crystalline MoS₂ layer remains elusive. This is because of the number of grains and defects unavoidably generated during CVD synthesis. Here, we perform multimillion-atom reactive molecular dynamics (RMD) simulations to identify an origin of the grain growth, migration, and defect healing process on a CVD-grown MoS₂ monolayer. RMD results reveal that grain boundaries could be successfully repaired by multiple heat treatments. Our work proposes a new way of controlling the grain growth and migration on a CVD-grown MoS₂ monolayer.

Rapid and reversible lithiation of doped biogenous iron oxide nanoparticles

Certain bacteria produce iron oxide material assembled with nanoparticles (NPs) that are doped with silicon (Fe:Si ~ 3:1) in ambient environment. Such biogenous iron oxides (BIOX) proved to be an excellent electrode material for lithium-ion batteries, but underlying atomistic mechanisms remain elusive. Here, quantum molecular dynamics simulations, combined with biomimetic synthesis and characterization, show rapid charging and discharging of NP within 100 fs, with associated surface lithiation and delithiation, respectively. The rapid electric response of NP is due to the large fraction of surface atoms. Furthermore, this study reveals an essential role of Si-doping, which reduces the strength of Li-O bonds, thereby achieving more gentle and reversible lithiation culminating in enhanced cyclability of batteries. Combined with recent developments in bio-doping technologies, such fundamental understanding may lead to energy-efficient and environment-friendly synthesis of a wide variety of doped BIOX materials with customized properties.

Ab initio molecular dynamics study of prebiotic production processes of organic compounds at meteorite impacts on ocean

Recent experiments concerning prebiotic materials syntheses suggest that the iron-bearing meteorite impacts on ocean during Late Heavy Bombardment provided abundant organic compounds associated with biomolecules such as amino acids and nucleobases. However, the molecular mechanism of a series of chemical reactions to produce such compounds is not well understood. In this study, we simulate the shock compression state of a meteorite impact for a model system composed of CO₂ , H₂ O, and metallic iron slab by ab initio molecular dynamics combined with multiscale shock technique, and clarify possible elementary reaction processes up to production of organic compounds. The reactions included not only pathways similar to the Fischer-Tropsch process known as an important hydrocarbon synthesis in many planetary processes but also those resulting in production of a carboxylic acid. It is also found that bicarbonate ions formed from CO₂ and H₂ O participated in some forms in most of these observed elementary reaction processes. These findings would deepen the understanding of the full range of chemical reactions that could occur in the meteorite impact events. © 2018 Wiley Periodicals, Inc.