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.

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.

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.

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.

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.

Fabrication of a Textile-Based Wearable Blood Leakage Sensor Using Screen-Offset Printing.

We fabricate a wearable blood leakage sensor on a cotton textile by combining two newly developed techniques. First, we employ a screen-offset printing technique that avoids blurring, short circuiting between adjacent conductive patterns, and electrode fracturing to form an interdigitated electrode structure for the sensor on a textile. Furthermore, we develop a scheme to distinguish blood from other substances by utilizing the specific dielectric dispersion of blood observed in the sub-megahertz frequency range. The sensor can detect blood volumes as low as 15 µL, which is significantly lower than those of commercially available products (which can detect approximately 1 mL of blood) and comparable to a recently reported value of approximately 10 µL. In this study, we merge two technologies to develop a more practical skin-friendly sensor that can be applied for safe, stress-free blood leakage monitoring during hemodialysis.

Hydrogen-on-demand using metallic alloy nanoparticles in water.

Hydrogen production from water using Al particles could provide a renewable energy cycle. However, its practical application is hampered by the low reaction rate and poor yield. Here, large quantum molecular dynamics simulations involving up to 16,611 atoms show that orders-of-magnitude faster reactions with higher yields can be achieved by alloying Al particles with Li. A key nanostructural design is identified as the abundance of neighboring Lewis acid-base pairs, where water-dissociation and hydrogen-production require very small activation energies. These reactions are facilitated by charge pathways across Al atoms that collectively act as a "superanion" and a surprising autocatalytic behavior of bridging Li-O-Al products. Furthermore, dissolution of Li atoms into water produces a corrosive basic solution that inhibits the formation of a reaction-stopping oxide layer on the particle surface, thereby increasing the yield. These atomistic mechanisms not only explain recent experimental findings but also predict the scalability of this hydrogen-on-demand technology at industrial scales.

A divide-conquer-recombine algorithmic paradigm for large spatiotemporal quantum molecular dynamics simulations.

We introduce an extension of the divide-and-conquer (DC) algorithmic paradigm called divide-conquer-recombine (DCR) to perform large quantum molecular dynamics (QMD) simulations on massively parallel supercomputers, in which interatomic forces are computed quantum mechanically in the framework of density functional theory (DFT). In DCR, the DC phase constructs globally informed, overlapping local-domain solutions, which in the recombine phase are synthesized into a global solution encompassing large spatiotemporal scales. For the DC phase, we design a lean divide-and-conquer (LDC) DFT algorithm, which significantly reduces the prefactor of the O(N) computational cost for N electrons by applying a density-adaptive boundary condition at the peripheries of the DC domains. Our globally scalable and locally efficient solver is based on a hybrid real-reciprocal space approach that combines: (1) a highly scalable real-space multigrid to represent the global charge density; and (2) a numerically efficient plane-wave basis for local electronic wave functions and charge density within each domain. Hybrid space-band decomposition is used to implement the LDC-DFT algorithm on parallel computers. A benchmark test on an IBM Blue Gene/Q computer exhibits an isogranular parallel efficiency of 0.984 on 786 432 cores for a 50.3 × 10(6)-atom SiC system. As a test of production runs, LDC-DFT-based QMD simulation involving 16 661 atoms is performed on the Blue Gene/Q to study on-demand production of hydrogen gas from water using LiAl alloy particles. As an example of the recombine phase, LDC-DFT electronic structures are used as a basis set to describe global photoexcitation dynamics with nonadiabatic QMD (NAQMD) and kinetic Monte Carlo (KMC) methods. The NAQMD simulations are based on the linear response time-dependent density functional theory to describe electronic excited states and a surface-hopping approach to describe transitions between the excited states. A series of techniques are employed for efficiently calculating the long-range exact exchange correction and excited-state forces. The NAQMD trajectories are analyzed to extract the rates of various excitonic processes, which are then used in KMC simulation to study the dynamics of the global exciton flow network. This has allowed the study of large-scale photoexcitation dynamics in 6400-atom amorphous molecular solid, reaching the experimental time scales.

Molecular Autonomous Pathfinder Using Deep Reinforcement Learning.

Diffusion in solids is a slow process that dictates rate-limiting processes in key chemical reactions. Unlike crystalline solids that offer well-defined diffusion pathways, the lack of similar structural motifs in amorphous or glassy materials poses great challenges in bridging the slow diffusion process and material failures. To tackle this problem, we propose an AI-guided long-term atomistic simulation approach: molecular autonomous pathfinder (MAP) framework based on deep reinforcement learning (DRL), where the RL agent is trained to uncover energy efficient diffusion pathways. We employ a Deep Q-Network architecture with distributed prioritized replay buffer, enabling fully online agent training with accelerated experience sampling by an ensemble of asynchronous agents. After training, the agents provide atomistic configurations of diffusion pathways with their energy profile. We use a piecewise nudged elastic band to refine the energy profile of the obtained pathway and the corresponding diffusion time on the basis of transition-state theory. With the MAP framework, we demonstrate atomistic diffusion mechanisms in amorphous silica with time scales comparable to experiments.

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.

Nanobubble collapse on a silica surface in water: billion-atom reactive molecular dynamics simulations.

Cavitation bubbles occur in fluids subjected to rapid changes in pressure. We use billion-atom reactive molecular dynamics simulations on a 163,840-processor BlueGene/P supercomputer to investigate damage caused by shock-induced collapse of nanobubbles in water near an amorphous silica surface. Collapse of an empty bubble generates a high-speed nanojet, which causes pitting on the silica surface. We find pit radii are close to bubble radii, and experiments also indicate linear scaling between them. The gas-filled bubbles undergo partial collapse and, consequently, the damage on the silica surface is mitigated.

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.

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.

Enhanced charge transfer by phenyl groups at a rubrene/C60 interface.

Exciton dynamics at an interface between an electron donor, rubrene, and a C(60) acceptor is studied by nonadiabatic quantum molecular dynamics simulation. Simulation results reveal an essential role of the phenyl groups in rubrene in increasing the charge-transfer rate by an order-of-magnitude. The atomistic mechanism of the enhanced charge transfer is found to be the amplification of aromatic breathing modes by the phenyl groups, which causes large fluctuations of electronic excitation energies. These findings provide insight into molecular structure design for efficient solar cells, while explaining recent experimental observations.

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.

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.

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.

Optimal design of a spectral readout type planar waveguide-mode sensor with a monolithic structure.

Optical planar waveguide-mode sensor is a promising candidate for highly sensitive biosensing techniques in fields such as protein adsorption, receptor-ligand interaction and surface bacteria adhesion. To make the waveguide-mode sensor system more realistic, a spectral readout type waveguide sensor is proposed to take advantage of its high speed, compactness and low cost. Based on our previously proposed monolithic waveguide-mode sensor composed of a SiO2 waveguide layer and a single crystalline Si layer [1], the mechanism for achieving high sensitivity is revealed by numerical simulations. The optimal achievable sensitivities for a series of waveguide structures are summarized in a contour map, and they are found to be better than those of previously reported angle-scan type waveguide sensors.