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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.

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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Unveiling the Mechanism of Mn Dissolution Through a Dynamic Cathode‐Electrolyte Interphase on LiMn2O4

Abstract Understanding the formation and evolution of the cathode‐electrolyte interphase (CEI), which forms at the interface between the cathode and electrolyte, is crucial for revealing degradation mechanisms in cathode materials, especially for developing strategies to stabilize the interphase in the strongly oxidizing conditions that evolve at high operating voltages in next‐generation Li‐ion batteries. However, The present understanding of the CEI is challenged by its complex and dynamic nature. In this work, near‐edge X‐ray absorption fine structure spectroscopy, electrochemical characterization, and reactive molecular dynamics simulations are combined to reveal a mechanism for CEI formation and evolution above model LiMn 2 O 4 (LMO) thin‐film electrodes in contact with conventional carbonate‐based electrolytes. It is found that Mn dissolution from LMO can be understood in terms of repetitive Mn 3 O 4 formation and dissolution behavior during cycling, which is closely connected to electrolyte decomposition and a key aspect of the CEI formation and growth. The behavior of the CEI in this model system offers detailed insight into the dynamic chemistry of the interphase, underscoring the important role of electrolyte composition and cathode surface structure in interphase degradation.

Ou, Wenhan↗
DOE OSTI2021

Emergent Recursive Multiscale Interaction in Complex Systems

This project studied the potential for multiscale group dynamics in complex social systems, including emergent recursive interaction. Current social theory on group formation and interaction focuses on a single scale (individuals forming groups) and is largely qualitative in its explanation of mechanisms. We combined theory, modeling, and data analysis to find evidence that these multiscale phenomena exist, and to investigate their potential consequences and develop predictive capabilities. In this report, we discuss the results of data analysis showing that some group dynamics theory holds at multiple scales. We introduce a new theory on communicative vibration that uses social network dynamics to predict group life cycle events. We discuss a model of behavioral responses to the COVID-19 pandemic that incorporates influence and social pressures. Finally, we discuss a set of modeling techniques that can be used to simulate multiscale group phenomena.

42 ENGINEERING↗
DOE OSTI2021

Bayesian force fields from active learning for simulation of inter-dimensional transformation of stanene

Abstract We present a way to dramatically accelerate Gaussian process models for interatomic force fields based on many-body kernels by mapping both forces and uncertainties onto functions of low-dimensional features. This allows for automated active learning of models combining near-quantum accuracy, built-in uncertainty, and constant cost of evaluation that is comparable to classical analytical models, capable of simulating millions of atoms. Using this approach, we perform large-scale molecular dynamics simulations of the stability of the stanene monolayer. We discover an unusual phase transformation mechanism of 2D stanene, where ripples lead to nucleation of bilayer defects, densification into a disordered multilayer structure, followed by formation of bulk liquid at high temperature or nucleation and growth of the 3D bcc crystal at low temperature. The presented method opens possibilities for rapid development of fast accurate uncertainty-aware models for simulating long-time large-scale dynamics of complex materials.

Chemistry↗
DOE OSTI2021

Disentangling Rotational Dynamics and Ordering Transitions in a System of Self-Organizing Protein Nanorods via Rotationally Invariant Latent Representations

The dynamics of complex ordering systems with active rotational degrees of freedom exemplified by protein self-assembly is explored using a machine learning workflow that combines deep learning-based semantic segmentation and rotationally invariant variational autoencoder-based analysis of orientation and shape evolution. The latter allows for disentanglement of the particle orientation from other degrees of freedom and compensates for lateral shifts. The disentangled representations in the latent space encode the rich spectrum of local transitions that can now be visualized and explored via continuous variables. The time dependence of ensemble averages allows insight into the time dynamics of the system and, in particular, illustrates the presence of the potential ordering transition. Finally, analysis of the latent variables along the single-particle trajectory allows tracing these parameters on a single-particle level. The proposed approach is expected to be universally applicable for the description of the imaging data in optical, scanning probe, and electron microscopy seeking to understand the dynamics of complex systems where rotations are a significant part of the process.

representation learning↗
DOE OSTI2022

Strongly Anharmonic Phonons and Their Role in Superionic Diffusion and Ultralow Thermal Conductivity of Cu 7 PSe 6

We report the quest for advanced superionic materials requires understanding their complex atomic dynamics, but detailed studies of the interplay between lattice vibrations and ionic diffusion remain scarce. Here inelastic and quasielastic neutron scattering measurements in the superionic argyrodite Cu 7 PSe 6 are reported, combined with molecular dynamics (MD) based on ab initio and machine-learned potentials, providing critical insights into the atomistic mechanisms underlying fast ion conduction. The results reveal how long-range Cu diffusion is limited by intercluster hopping, controlled by selective anharmonic phonons of the crystalline framework. Further, the Green–Kubo simulations reproduce the ultralow lattice thermal conductivity and identify contributions from mobile ions, phonons, and their cross-correlations. The mode resolved analysis shows that the thermal conductivity is dominated by low-energy acoustic phonon modes of the overall crystal framework. The analysis of mode-resolved spectral functions further show that vibrational modes with significant Cu contributions are strongly damped, corresponding to the breakdown of associated phonon quasiparticles. These results highlight the importance of strongly anharmonic effects in superionic systems, in which the traditional quasiharmonic phonon picture is insufficient, and pave the way toward combining machine-learning accelerated simulations with neutron scattering experiments to rationalize the complex atomic dynamics underlying ionic and thermal transport.

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

Attosecond delays in X-ray molecular ionization

The photoelectric effect is not truly instantaneous, but exhibits attosecond delays that can reveal complex molecular dynamics. Sub-femtosecond duration light pulses provide the requisite tools to resolve the dynamics of photoionization. Accordingly, the past decade has produced a large volume of work on photoionization delays following single photon absorption of an extreme ultraviolet (XUV) photon. However, the measurement of time-resolved core-level photoionization remained out of reach. The required x-ray photon energies needed for core-level photoionization were not available with attosecond tabletop sources. We have now measured the x-ray photoemission delay of core-level electrons, and here report unexpectedly large delays, ranging up to 700 attoseconds in NO near the oxygen K-shell threshold. These measurements exploit attosecond soft x-ray pulses from a free-electron laser (XFEL) to scan across the entire region near the K-shell threshold. Furthermore, we find the delay spectrum is richly modulated, suggesting several contributions including transient trapping of the photoelectron due to shape resonances, collisions with the Auger-Meitner electron that is emitted in the rapid non-radiative relaxation of the molecule, and multi-electron scattering effects. Finally, the results demonstrate how x-ray attosecond experiments, supported by comprehensive theoretical modelling, can unravel the complex correlated dynamics of core-level photoionization.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Colliding Pulse Mode Locking and Nonequilibrium Carrier Dynamics within Vertical External-Cavity Surface-Emitting Lasers

The complex nonlinear dynamics of colliding counterpropagating ultrashort laser pulses circulating in a vertical external-cavity surface-emitting ring laser cavity are captured by a first-principles quantum-mechanical microscopic model coupled to an intracavity beam propagator. Furthermore, our numerical simulations establish that overall mode locking stability is sensitive to relative phases and delays of the pulses impinging on the semiconductor saturable absorber mirror. Delay offsets of the counterpropagating pulses lead to asymmetric carrier recovery and instability, often leading in turn to single unidirectional circulating pulses. Although computationally challenging, we also include transverse effects and show that the influence of interference patterns from spatial gratings written by the counterpropagating pulses is significant in stabilizing the modelocked pulses.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Comprehensive Dynamics in a Polyelectrolyte Complex Coacervate

The linear viscoelastic response, LVR, of a hydrated polyelectrolyte complex coacervate, PEC, was evaluated over a range of frequencies, temperatures, and salt concentrations. The PEC was a nearly stoichiometric blend of a quaternary ammonium poly([3-(methacrylamido)propyl]trimethylammonium chloride), PMAPTAC, and poly(2-acrylamido-2-methyl-1-propanesulfonic acid sodium salt), PAMPS, an aliphatic sulfonate, selected because they remain fully charged over the conditions of use. Narrow molecular weight distribution polyelectrolytes were prepared using fractionation techniques. A partially deuterated version of PMAPTAC was incorporated to determine its coil radius of gyration, R g , within PECs using small-angle neutron scattering. Chain dimensions were determined to be Gaussian with a Kuhn length of 2.37 nm, which remained constant from 25 to 65 °C. The LVR for a series of matched molecular weight PECs, mostly above the entanglement threshold, exhibited crossovers of modulus versus frequency classically attributed to the reptation time, relaxation between entanglements, and the relaxation of a Kuhn length of units (the “monomer” time). The scaling for zero shear viscosity, η 0 , versus chain length, N, was η 0 ~ N 3.1 , in agreement with “sticky reptation” theory. The lifetime and activation energy, E p , of a pair between polyanion and polycation repeat units, Pol + Pol – , were determined from diffusion coefficients of salt ions within the PEC. The activation energy for LVR of salt-free PECs was 2E p , showing that the key mechanism limiting the dynamics of undoped PECs is pair exchange. An FTIR technique was used to distinguish whether SCN– acts as a counterion or a co-ion within PECs. Doping of PECs with NaSCN breaks Pol + Pol – pairing efficiently, which decreases effective cross-linking and decreases viscosity. Finally, an equation was derived that quantitatively predicts this effect.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

From IMT Device Measurements to Network-Level Consequences: When Learning Suppresses Beyond-LIF Neuron Dynamics

Emerging neuromorphic devices such as insulator--metal transition (IMT) devices exhibit complex temporal dynamics, including slow internal state memory, hysteresis, and burst-like firing, which are poorly captured by conventional leaky integrate-and-fire (LIF) neurons. However, it remains unclear when such dynamics influence learning and inference at the network level, particularly under commonly used unsupervised plasticity rules. We present a controlled, full-stack co-design study spanning experimental characterization of individual IMT devices, compact neuron model development, and large-scale spiking network simulations with identical architectures and learning rules. Rather than optimizing benchmark accuracy, our goal is to diagnose when neuron-level dynamics survive learning and competition, and when they are suppressed, to inform the co-design of devices, networks, and learning rules that can exploit beyond-LIF complexity.

42 ENGINEERING↗
DOE OSTI2023

Probing Ultrafast Electron (De)localization Dynamics in Mixed Valence Complexes Using Femtosecond X-ray Spectroscopy (Final Report)

The central goal of this proposal was to directly visualize and quantify how electronic correlations, vibronic couplings, and local solute-solvent interactions control intramolecular electron motion on the femtosecond time scale. The proposed research program focused on solvated Ruthenium (Ru) based mixed-valence complexes, a prototypical class of transition-metal complexes, which are of significant interest for their potential applications in photochemical energy conversion. Electron movements following photochemical excitation are closely coupled with atomic/vibrational and solvent motion. To disentangle these various components requires tools designed to directly probe electron correlations and vibronic coupling (coherently coupled motions of electronic and vibrational coordinates) on the timescale of electron motion in the solution phase.

36 MATERIALS SCIENCE↗
DOE OSTI2022

High-precision inversion of dynamic radiography using hydrodynamic features

While radiography is routinely used to probe complex, evolving density fields in research areas ranging from materials science to shock physics to inertial confinement fusion and other national security applications, complications resulting from noise, scatter, complex beam dynamics, etc. prevent current methods of reconstructing density from being accurate enough to identify the underlying physics with sufficient confidence. In this work, we show that using only features that are robustly identifiable in radiographs and combining them with the underlying hydrodynamic equations of motion using a machine learning approach of a conditional generative adversarial network (cGAN) provides a new and effective approach to determine density fields from a dynamic sequence of radiographs. In particular, we demonstrate the ability of this method to outperform a traditional, direct radiograph to density reconstruction in the presence of scatter, even when relatively small amounts of scatter are present. Our experiments on synthetic data show that the approach can produce high quality, robust reconstructions. We also show that the distance (in feature space) between a testing radiograph and the training set can serve as a diagnostic of the accuracy of the reconstruction.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗