Catalysis in Extreme Field Environments: A Case Study of Strongly Ionized SiO2 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. While the dynamics of laser-driven surface ion emission has been extensively explored, understanding the molecular dynamics leading to fragmentation has remained elusive. Here, we employ a multiscale approach performing nonadiabatic quantum molecular dynamics (NAQMD) simulations on hydrogenated silica surfaces in both bare and wetted environments under field conditions mimicking those of an ionized nanoparticle. Our findings indicate that hole localization drives fragmentation dynamics, leading to surface silanol dissociation within 50 fs and charge transfer-induced water splitting in wetted environments within 150 fs. Further insight into such ultrafast mechanisms is critical for the advancement of catalysis on the surface of charged nanosystems.

Exploring the Global Reaction Coordinate for Retinal Photoisomerization: A Graph Theory-Based Machine Learning Approach.

Unraveling the reaction pathway of photoinduced reactions poses a great challenge owing to its complexity. Recently, graph theory-based machine learning combined with nonadiabatic molecular dynamics (NAMD) has been applied to obtain the global reaction coordinate of the photoisomerization of azobenzene. However, NAMD simulations are computationally expensive as they require calculating the nonadiabatic coupling vectors at each time step. Here, we showed that ab initio molecular dynamics (AIMD) can be used as an alternative to NAMD by choosing an appropriate initial condition for the simulation. We applied our methodology to determine a plausible global reaction coordinate of retinal photoisomerization, which is essential for human vision. On rank-ordering the internal coordinates, based on the mutual information (MI) between the internal coordinates and the HOMO energy, NAMD and AIMD give a similar trend. Our results demonstrate that our AIMD-based machine learning protocol for retinal is 1.5 times faster than that of NAMD to study reaction coordinates.

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+

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.

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.

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: C60Nan (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.

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 Up < 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 Up < 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 Up = 2.18 km/s for the [010] direction and Up = 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.

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.

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

Scandium-doped aluminum nitride, Al1-xScxN, 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 Al1-xScxN 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.

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 ZrSxSe2-x alloys and MoS2. ZrSxSe2-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 ZrSxSe2-x and MoS2 and identify the atomistic-scale mechanisms of bonding and phase transformations in layered materials with competing anions.

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.

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.

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.

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.

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.

Electronic Origin of Optically-Induced Sub-Picosecond Lattice Dynamics in MoSe2 Monolayer.

Atomically thin layers of transition metal dichalcogenide (TMDC) semiconductors exhibit outstanding electronic and optical properties, with numerous applications such as valleytronics. While unusually rapid and efficient transfer of photoexcitation energy to atomic vibrations was found in recent experiments, its electronic origin remains unknown. Here, we study the lattice dynamics induced by electronic excitation in a model TMDC monolayer, MoSe2, using nonadiabatic quantum molecular dynamics simulations. Simulation results show sub-picosecond disordering of the lattice upon photoexcitation, as measured by the Debye-Waller factor, as well as increasing disorder for higher densities of photogenerated electron-hole pairs. Detailed analysis shows that the rapid, photoinduced lattice dynamics are due to phonon-mode softening, which in turn arises from electronic Fermi surface nesting. Such mechanistic understanding can help guide optical control of material properties for functionalizing TMDC layers, enabling emerging applications such as phase change memories and neuromorphic computing.

Computational Synthesis of MoS2 Layers by Reactive Molecular Dynamics Simulations: Initial Sulfidation of MoO3 Surfaces.

Transition metal dichalcogenides (TMDC) like MoS2 are promising candidates for next-generation electric and optoelectronic devices. These TMDC monolayers are typically synthesized by chemical vapor deposition (CVD). However, despite significant amount of empirical work on this CVD growth of monolayered crystals, neither experiment nor theory has been able to decipher mechanisms of selection rules for different growth scenarios, or make predictions of optimized environmental parameters and growth factors. Here, we present an atomic-scale mechanistic analysis of the initial sulfidation process on MoO3 surfaces using first-principles-informed ReaxFF reactive molecular dynamics (RMD) simulations. We identify a three-step reaction process associated with synthesis of the MoS2 samples from MoO3 and S2 precursors: O2 evolution and self-reduction of the MoO3 surface; SO/SO2 formation and S2-assisted reduction; and sulfidation of the reduced surface and Mo-S bond formation. These atomic processes occurring during early stage MoS2 synthesis, which are consistent with experimental observations and existing theoretical literature, provide valuable input for guided rational synthesis of MoS2 and other TMDC crystals by the CVD process.