Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Dynamic range”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Dynamics and bifurcations of laminar annular swirling and non-swirling jets

This paper presents bifurcation analyses characterising the nonlinear dynamics of fully developed laminar annular jets with respect to the centrebody diameter ${ {d}}$ , Reynolds number ${ {Re}}$ , and swirl ratio ${ {S}}$ . Similar flows appear in numerous applications and feature a vibrant range of topological and dynamical characteristics associated with phenomena including shear layer separation and vortex breakdown. Our results begin by describing the non-monotonic evolution of the axisymmetric jet's steady topology under varying ${ {S}}$ . In accord with earlier reports, the jet progresses through a sequence of wake, breakdown and wall jet regimes in a qualitatively similar manner across a wide span of ${ {d}}$ and ${ {Re}}$ values. In the wake regime, the non-swirling jet bifurcates to a plane-symmetric, but not axisymmetric, steady flow pattern beyond a ${ {d}}$ -dependent critical ${ {Re}}$ value. With further increase in ${ {Re}}$ , the steady non-swirling jet destabilises subsequently via multiple distinct Hopf bifurcations. Introducing ${ {S}}>0$ to the jet also induces unsteadiness by twisting the singly azimuthally periodic ( $|m|=1$ ) asymmetric wake structure and causing it to precess periodically in time about the central axis. Intermediate swirl stabilises this unsteady dynamics and restores the jet's axisymmetry. This stabilising effect is then reversed in the breakdown regime at higher ${ {S}}$ , where a variety of different $|m|=1$ and $|m|=2$ instabilities bifurcate from the steady flow as ${ {S}}$ is increased. Several instances of hysteresis and subcritical behaviour are reported and discussed, including one that manifests precessing vortex core oscillations.

Douglas, Christopher M. (ORCID:0000000259683315)↗
DOE OSTI2021

Data-driven learning of Mori–Zwanzig operators for isotropic turbulence

Developing reduced-order models for turbulent flows, which contain dynamics over a wide range of scales, is an extremely challenging problem. In statistical mechanics, the Mori–Zwanzig (MZ) formalism provides a mathematically exact procedure for constructing reduced-order representations of high-dimensional dynamical systems, where the effects due to the unresolved dynamics are captured in the memory kernel and orthogonal dynamics. Turbulence models based on MZ formalism have been scarce due to the limited knowledge of the MZ operators, which originates from the difficulty in deriving MZ kernels for complex nonlinear dynamical systems. In this work, we apply a recently developed data-driven learning algorithm, which is based on Koopman's description of dynamical systems and Mori's linear projection operator, on a set of fully resolved isotropic turbulence datasets to extract the Mori–Zwanzig operators. With data augmentation using known turbulence symmetries, the extracted Markov term, memory kernel, and orthogonal dynamics are statistically converged and the generalized fluctuation–dissipation relation can be verified. The properties of the memory kernel and orthogonal dynamics, and their dependence on the choices of observables are investigated to address the modeling assumptions that are commonly used in MZ-based models. A series of numerical experiments are then constructed using the extracted kernels to evaluate the memory effects on prediction. The results show that the prediction errors are strongly affected by the choice of observables and can be further reduced by including the past history of the observables in the memory kernel.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Tuning between continuous time crystals and many-body scars in long-range XYZ spin chains.

Persistent oscillatory dynamics in nonequilibrium many-body systems is a tantalizing manifestation of ergodicity breakdown that continues to attract much attention. Recent works have focused on two classes of such systems: discrete time crystals and quantum many-body scars (QMBS). While both systems host oscillatory dynamics, its origin is expected to be fundamentally different: discrete time crystal is a phase of matter which spontaneously breaks the Z 2 symmetry of the external periodic drive, while QMBS span a subspace of nonthermalizing eigenstates forming an su(2) algebra representation. Here, we ask a basic question: is there a physical system that allows us to tune between these two dynamical phenomena? In contrast to much previous work, we investigate the possibility of a continuous time crystal (CTC) in undriven, energy-conserving systems exhibiting prethermalization. We introduce a long-range XYZ spin model and show that it encompasses both a CTC phase as well as QMBS. We map out the dynamical phase diagram using numerical simulations based on exact diagonalization and time-dependent variational principle in the thermodynamic limit. We identify a regime where QMBS and CTC order coexist, and we discuss experimental protocols that reveal their similarities as well as key differences.

Bull, Kieran↗
DOE OSTI2022

The weathered bedrock vadose zone: A hidden control on water availability in the western United States (Final Technical Report)

This report summarizes the research completed during the project entitled "The weathered bedrock vadose zone: A hidden control on water availability in the western United States" (Award: DE-SC0018039). The report covers work completed between 9/15/17 and 9/14/20. The objective of the exploratory project work was to evaluate the overarching hypothesis that the subsurface underlying actively eroding landscapes is systematically structured as a result of a balance between weathering and erosion and that this structure leads to dynamic moisture storage within weathered rock. To accomplish project objectives, we characterized hillslope weathering profile structure and conducted subsurface moisture monitoring across four study sites with comparable bedrock types but differing erosion rate and hydroclimate: The Angelo Coast Range Reserve (ACRR), Sagehorn-Russell Ranch (SRR), Antelope Valley Ranch (AVR), and the LBNL Watershed Function Scientific Focus Area (SFA). New characterization and monitoring infrastructure at the AVR and SFA sites was established through this project and enables long-term monitoring of hydrologic and carbon fluxes at the sites for comparison with long-term monitoring in other sites. Deep drilling, downhole logging, and characterization of chemistry and porosity of rock recovered from drilling reveals two weathering fronts across the sites. A shallower front is marked by the extent of pervasive fracturing and oxidation of pyrite and organic carbon. A deeper weathering front is marked by the appearance of open fractures and discoloration. At the AVR site, multiple ridge tops of different hillslope length were investigated to establish the relationship between the weathering front and topography. Drilling reveals that the fraction of the hillslope relief that is weathered scales with hillslope length, consistent with the theoretical framework that guided the project relating groundwater drainage with landscape evolution. With the establishment of new boreholes, we have documented significant water storage in weathered bedrock using low-field borehole nuclear magnetic resonance (NMR) across all four sites. In the upper 4 m of bedrock, the average water contents at ACRR, AVR, SRR, and SFA are approximately 25%, 16%, 25%, 23% respectively. At the ACRR site, where long-term monitoring via neutron probe surveys and time-domain transmission sensors are available, we have demonstrated that NMR reliably records moisture content and importantly, moisture content dynamics in weathered bedrock. Across all sites, our NMR monitoring has also revealed that a considerable fraction of the water storage occurs in fractures or pores that are significantly larger than the fine-grained bedrock matrix of the parent rock. To identify the proportion of water storage occurring in fractures and large pores, we analyzed the NMR signal by evaluating both the sum of echoes and an inversion of the NMR signal for a distribution of T2 relaxation times. Both methods reveal that at least 20% of the water storage occurs in fractures and larger pores across all sites. To constrain the interpretation of the in-situ field-scale NMR measurements, we compare the NMR response in the field to independent pore-structure information obtained via helium pycnometry. Results support the inference that dynamic seasonal water storage is dominantly restricted to the fracture network, which can reach 7% of the total volume. This dynamic range in water content is comparable to that of some soils, underscoring the need to understand how this region of the Earth System is structured, and functions as a hydrologic reservoir.

58 GEOSCIENCES↗
DOE OSTI2020

Dynamic Manipulation in Piezoresponse Force Microscopy: Creating Nonequilibrium Phases with Large Electromechanical Response

We discuss how domain walls and topological defects in ferroelectric materials have emerged as a powerful tool for functional electronic devices including memory and logic. Similarly, wall interactions and dynamics underpin a broad range of mesoscale phenomena ranging from giant electromechanical responses to memory effects. Exploring the functionalities of individual domain walls, their interactions, and controlled modifications of the domain structures is crucial for applications and fundamental physical studies. However, the dynamic nature of these features severely limits studies of their local physics since application of local biases or pressures in piezoresponse force microscopy induce wall displacement as a primary response. Here, we introduce an approach for the control and modification of domain structures based on automated experimentation, whereby real-space image-based feedback is used to control the tip bias during ferroelectric switching, allowing for modification routes conditioned on domain states under the tip. This automated experiment approach is demonstrated for the exploration of domain wall dynamics and creation of metastable phases with large electromechanical response.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

AI-NERD: Elucidation of relaxation dynamics beyond equilibrium through AI-informed X-ray photon correlation spectroscopy

Abstract Understanding and interpreting dynamics of functional materials in situ is a grand challenge in physics and materials science due to the difficulty of experimentally probing materials at varied length and time scales. X-ray photon correlation spectroscopy (XPCS) is uniquely well-suited for characterizing materials dynamics over wide-ranging time scales. However, spatial and temporal heterogeneity in material behavior can make interpretation of experimental XPCS data difficult. In this work, we have developed an unsupervised deep learning (DL) framework for automated classification of relaxation dynamics from experimental data without requiring any prior physical knowledge of the system. We demonstrate how this method can be used to accelerate exploration of large datasets to identify samples of interest, and we apply this approach to directly correlate microscopic dynamics with macroscopic properties of a model system. Importantly, this DL framework is material and process agnostic, marking a concrete step towards autonomous materials discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A foundation model for atomistic materials chemistry

Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab initio quality over unprecedented time and length scales. However, early machine-learning (ML) force fields have largely been limited by (i) the substantial computational and human effort required to develop and validate potentials for each particular system of interest and (ii) a general lack of transferability from one chemical system to the next. Here, we show that it is possible to create a general-purpose atomistic ML model, trained on a public dataset of moderate size, that is capable of running stable molecular dynamics for a wide range of molecules and materials. We demonstrate the power of the MACE-MP-0 model-and its qualitative and at times quantitative accuracy-on a diverse set of problems in the physical sciences, including properties of solids, liquids, gases, chemical reactions, interfaces, and even the dynamics of a small protein. The model can be applied out of the box as a starting or "foundation" model for any atomistic system of interest and, when desired, can be fine-tuned on just a handful of application-specific data points to reach ab initio accuracy. Establishing that a stable force-field model can cover almost all materials changes atomistic modeling in a fundamental way: experienced users obtain reliable results much faster, and beginners face a lower barrier to entry. Foundation models thus represent a step toward democratizing the revolution in atomic-scale modeling that has been brought about by ML force fields.

Batatia, Ilyes↗
DOE OSTI2024

Modeling graphene sheet growth and dynamical matrix calculations using molecular dynamics

Molecular dynamics (MD) has been an incredibly useful tool to model physical processes that were synthesized experimentally but not fully understood. MD, through the use of semi-empirical inter-atomic potentials, has allowed understanding of different physical processes in materials science. Yet as well as providing useful insights into materials science, molecular dynamics has a wider range of usability. In this report, I will be detailing how MD can be used to study graphene formation from a carbon liquid which requires high temperatures and pressures. Beyond this, I will describe the usefulness of MD for understanding the physics for phonon transport quantum sensors. To do this, MD was employed to determine the dynamical matrix by treating atoms as coupled oscillators. An accurate understanding of the dynamical matrix of a system is required to calculate the non-equilibrium Green’s function used to describe the phonon transport within phonon wave-guides. I found that, across multiple pressures and temperatures, randomly placed carbon atoms will show evidence of pent-first formation with semi-empirical models. Density functional theory (DFT), on the other hand, was too computationally expensive to use for full scale MD simulations, but we have the possibility of training a machine learned interatomic potential to approximate DFT for carbon in the environments being studied for pent-first graphene sheet formation.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Heterogeneous Polymer Dynamics Explored Using Static 1H NMR Spectra

NMR spectroscopy continues to provide important molecular level details of dynamics in different polymer materials, ranging from rubbers to highly crosslinked composites. It has been argued that thermoset polymers containing dynamic and chemical heterogeneities can be fully cured at temperatures well below the final glass transition temperature (Tg). In this paper, we described the use of static solid-state 1H NMR spectroscopy to measure the activation of different chain dynamics as a function of temperature. Near Tg, increasing polymer segmental chain fluctuations lead to dynamic averaging of the local homonuclear proton-proton (1H-1H) dipolar couplings, as reflected in the reduction of the NMR line shape second moment (M2) when motions are faster than the magnitude of the dipolar coupling. In general, for polymer systems, distributions in the dynamic correlation times are commonly expected. To help identify the limitations and pitfalls of M2 analyses, the impact of activation energy or, equivalently, correlation time distributions, on the analysis of 1H NMR M2 temperature variations is explored. It is shown by using normalized reference curves that the distributions in dynamic activation energies can be measured from the M2 temperature behavior. An example of the M2 analysis for a series of thermosetting polymers with systematically varied dynamic heterogeneity is presented and discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Dynamic response functions of two-dimensional Dirac fermions with screened Coulomb and short-range interactions

Here, we consider a screened Coulomb interaction between electrons in graphene and determine their dynamic response functions, such as a longitudinal and a transverse electric conductivity and a polarization function and compare them to the corresponding quantities in the short-range interaction model. The calculations are performed to all orders for short-range interaction by taking into account the self-energy renormalization of the electron velocity and using a ladder approximation to account for the vertex corrections, ensuring that the Ward identity (charge conservation law) is satisfied. Our findings predict a resonant response of interacting electron-hole pairs at a particular frequency below the threshold $\textit{qv = ω}$ and further predict an instability for sufficiently strong interactions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Turbulent Regimes in Collisions of 3D Alfvén-wave Packets

Using three-dimensional gyrofluid simulations, we revisit the problem of Alfvén-wave (AW) collisions as building blocks of the Alfvénic turbulent cascade and their interplay with magnetic reconnection at magnetohydrodynamic (MHD) scales. Depending on the large-scale value of the nonlinearity parameter χ 0 (the ratio between the AW linear propagation time and nonlinear turnover time), different regimes are observed. For strong nonlinearities (χ 0 ~ 1), turbulence is consistent with a dynamically aligned, critically balanced cascade—fluctuations exhibit a scale-dependent alignment $\sin {\theta }_{{k}_{\perp }}\propto {k}_{\perp }^{-1/4}$, resulting in a ${k}_{\perp }^{-3/2}$ spectrum and ${k}_{\parallel }\propto {k}_{\perp }^{1/2}$ spectral anisotropy. At weaker nonlinearities (small χ 0 ), a spectral break marking the transition between a large-scale weak regime and a small-scale ${k}_{\perp }^{-11/5}$ tearing-mediated range emerges, implying that dynamic alignment occurs also for weak nonlinearities. At χ 0 < 1 the alignment angle ${\theta }_{{k}_{\perp }}$ shows a stronger scale dependence than in the χ 0 ~ 1 regime, namely $\sin {\theta }_{{k}_{\perp }}\propto {k}_{\perp }^{-1/2}$ at χ 0 ~ 0.5, and $\sin {\theta }_{{k}_{\perp }}\propto {k}_{\perp }^{-1}$ at χ 0 ~ 0.1. Dynamic alignment in the weak regime also modifies the large-scale spectrum, scaling approximately as ${k}_{\perp }^{-3/2}$ for χ 0 ~ 0.5 and as ${k}_{\perp }^{-1}$ for χ 0 ~ 0.1. A phenomenological theory of dynamically aligned turbulence at weak nonlinearities that can explain these spectra and the transition to the tearing-mediated regime is provided; at small χ 0 , the strong scale dependence of the alignment angle combines with the increased lifetime of turbulent eddies to allow tearing to onset and mediate the cascade at scales that can be larger than those predicted for a critically balanced cascade by several orders of magnitude. Such a transition to tearing-mediated turbulence may even supplant the usual weak-to-strong transition.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Phase II Final Technical Report: Dynamic Gamma-ray Imaging for In Vivo Tracking of Microelement Transport Across Plant-Microbial Systems

The Department of Energy Office of Biological and Environmental Research (DOE BER) Mesoscale to Molecules Bioimaging Technology Program aims to develop new imaging and measurement technology to enable in situ and dynamic imaging across a range of spatial and temporal scales. Various imaging modalities are required to span the complete spatiotemporal landscape for bioenergy and environmental bioimaging needs. The high-resolution gamma-ray spectroscopy, imaging, and sensitivity of new high-purity germanium (HPGe) instruments provide a unique opportunity to complement and enhance these research goals. The HPGe-based Gamma-ray Imager for Plant Research (GIPR) developed here provides non-invasive, in vivo measurements to dynamically track the uptake and distribution of multiple gamma-emitting radioactive elements simultaneously as they move from the soil microbiome into living plants. The hand portable GIPR utilizes commercially available radioisotopes to provide spatial and temporal imaging of plant microelement exchange for the broader scientific community.

Kiser, Matthew↗
DOE OSTI2023

Imaging Nanoscale Energy Transport and Conversion with Ultrafast Electron Microscopy (Final Technical Report)

Light-matter interactions are ubiquitous in nature and reside at the heart of innumerable technologies. The cascade of processes that occur when a material absorbs a photon of light are exceedingly complex and are interwoven in both space and time, rendering precise determination of the atomic-scale and ultrafast mechanisms immensely challenging. The advent of methods for generating short pulses of light several decades ago led to major advances in understanding the initial moments of light absorption and the resultant effects, though directly interrogating the response of the atoms within the material continued to prove challenging. More recently, methods for generating ultrashort pulses of X-rays and fast electrons have opened the way to probing photoinduced structural dynamics of a wide range of matter in multiple phases. An especially promising laboratory-scale method is ultrafast electron microscopy (UEM), wherein the modalities of conventional transmission electron microscopes (imaging, diffraction, spectroscopy) are extended into the femtosecond temporal regime. Here, the ultrafast imaging and diffraction modalities of UEM were used to study the transient structural aspects of photoexcitation of semiconducting materials (e.g., spatially-resolved electron-phonon coupling, excitation and emission of acoustic phonons, and discrete nanoscale scattering processes). The project had three main objectives: (1) determination of the excitation mechanisms of dense, hypersonic charge-carrier waves and the spatially mediated means by which they couple to the lattice via coherent phonon emission, especially with UEM imaging, wherein effects of nanoscale structural and morphological features on the coupling and relaxation dynamics are expected to dictate nucleation sites and preferred wave vectors, (2) elucidation of photoinduced acoustic-phonon seeding, emergence, propagation, and decay over nanoscale crystal regions and especially with respect to local strain fields and atomic-scale disruptions in lattice order, and (3) interwoven with the first two were efforts aimed at realizing combined angstrom-femtosecond spatiotemporal imaging with UEM. The outcomes and impacts of this project were the generation of new knowledge with respect to fundamental light-matter interactions and, especially, the spatially-mediated excitation and evolution of the structural response of materials following coherent photoexcitation. Importantly, the spatial and temporal resolutions of the UEM imaging modalities used are well-suited for such studies and enable spatially-resolved mechanisms to be determined as a function of atomic order, structural features, and morphology. In addition, ultrafast crystallographic measurements were used to correlate real- and reciprocal-space dynamics in order to determine atomic-scale preferential wave vectors and ultrafast scattering mechanisms, especially as dictated by specimen boundary conditions. The obtained results, as detailed in peer-reviewed publications and presentations, illustrate the importance of ultrafast, angstrom-scale real-space imaging for developing a comprehensive understanding of energy transport and conversion in materials.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Cosmological probes of Dark Radiation from Neutrino Mixing

Abstract Models of stepped dark radiation have recently been found to have an important impact on the anisotropies of the cosmic microwave background, aiding in easing the Hubble tension. In this work, we study models with a sector of dark radiation with a step in its abundance, which thermalizes after big bang nucleosynthesis by mixing with the standard model neutrinos. For this, we extend an earlier work which has focused on the background evolution only until the dark sector thermalizes by deriving the full background and perturbation equations of the model and implementing them in an Einstein-Boltzmann solving code. We expound on the behavior of this model, discussing the wide range of parameters that result in interesting and viable cosmologies that dynamically generate dark radiation during a range of epochs. We find that for the strongly self-coupled regime, there is no large cosmological impact for a tight prior on the mass, whereas larger mass ranges allow a smooth interpolation between a behavior close to the ΛCDM cosmological standard model and close to an additional component of strongly self-interacting dark radiation. In the weakly self-coupled regime we find that we can accommodate a parameter space relevant for the neutrino anomalies as well as one relevant to easing the Hubble tension.

Astronomy & Astrophysics↗
DOE OSTI2022

MISPR : an open-source package for high-throughput multiscale molecular simulations

Computational tools provide a unique opportunity to study and design optimal materials by enhancing our ability to comprehend the connections between their atomistic structure and functional properties. However, designing materials with tailored functionalities is complicated due to the necessity to integrate various computational-chemistry software (not necessarily compatible with one another), the heterogeneous nature of the generated data, and the need to explore vast chemical and parameter spaces. The latter is especially important to avoid bias in scattered data points-based models and derive statistical trends only accessible by systematic datasets. Here, we introduce a robust high-throughput multi-scale computational infrastructure coined MISPR (Materials Informatics for Structure–Property Relationships) that seamlessly integrates classical molecular dynamics (MD) simulations with density functional theory (DFT). By enabling high-performance data analytics and coupling between different methods and scales, MISPR addresses critical challenges arising from the needs of automated workflow management and data provenance recording. The major features of MISPR include automated DFT and MD simulations, error handling, derivation of molecular and ensemble properties, and creation of output databases that organize results from individual calculations to enable reproducibility and transparency. In this work, we describe fully automated DFT workflows implemented in MISPR to compute various properties such as nuclear magnetic resonance chemical shift, binding energy, bond dissociation energy, and redox potential with support for multiple methods such as electron transfer and proton-coupled electron transfer reactions. The infrastructure also enables the characterization of large-scale ensemble properties by providing MD workflows that calculate a wide range of structural and dynamical properties in liquid solutions. MISPR employs the methodologies of materials informatics to facilitate understanding and prediction of phenomenological structure–property relationships, which are crucial to designing novel optimal materials for numerous scientific applications and engineering technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Control of monodomain polymer-stabilized cuboidal nanocrystals of chiral nematics by confinement.

Liquid crystals are important components of optical technologies. Cuboidal crystals consisting of chiral liquid crystals-the so-called blue phases (BPs), are of particular interest due to their crystalline structures and fast response times, but it is critical that control be gained over their phase behavior as well as the underlying dislocations and grain boundaries that arise in such systems. Blue phases exhibit cubic crystalline symmetries with lattice parameters in the 100 nm range and a network of disclination lines that can be polymerized to widen the range of temperatures over which they occur. Here, we introduce the concept of strain-controlled polymerization of BPs under confinement, which enables formation of strain-correlated stabilized morphologies that, under some circumstances, can adopt perfect single-crystal monodomain structures and undergo reversible crystal-to-crystal transformations, even if their disclination lines are polymerized. We have used super-resolution laser confocal microscopy to reveal the periodic structure and the lattice planes of the strain and polymerization stabilized BPs in 3D real space. Our experimental observations are supported and interpreted by relying on theory and computational simulations in terms of a free energy functional for a tensorial order parameter. Simulations are used to determine the orientation of the lattice planes unambiguously. The findings presented here offer opportunities for engineering optical devices based on single-crystal, polymer-stabilized BPs whose inherent liquid nature, fast dynamics, and long-range crystalline order can be fully exploited.

confinement↗
DOE OSTI2022

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.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Reagent-Triggered Isomerization of Fluxional Cluster Catalyst via Dynamic Coupling

Metallic cluster catalysts have many thermodynamically accessible isomers with diverse active sites and low reaction barriers, and lately a strong hypothesis emerged that the many catalyst states collectively drive the catalysis. Furthermore, it remained a hypothesis that catalyst isomerization is actually kinetically feasible under the current reaction conditions. Using high-temperature dynamics simulations and sampling, a range of orientations, and vibrational energy distributions, we probe how thermal effects and molecular events affect cluster catalyst dynamics. We show that even such a delicate affair as the dissociation or scattering of a methane molecule on the heavy and thus slow Pt 13 cluster triggers substantial isomerization of the catalyst, far beyond thermal at 700 K. A kinetic coupling between the methane activity and cluster catalyst dynamics is observed. In return, the thermal dynamics of the cluster affects the methane reaction and scattering probabilities. Hence, molecular events at the surfaces of fluxional cluster catalysts should facilitate the population of an ensemble of catalyst states under the current reaction conditions, with implications for available active sites, reaction mechanisms, and apparent rates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗