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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A two-directional vibrational probe reveals different electric field orientations in solution and an enzyme active site

We report the catalytic power of an electric field depends on its magnitude and orientation with respect to the reactive chemical species. Understanding and designing new catalysts for electrostatic catalysis thus requires methods to measure the electric field orientation and magnitude at the molecular scale. We demonstrate that electric field orientations can be extracted using a two-directional vibrational probe by exploiting the vibrational Stark effect of both the C=O and C–D stretches of a deuterated aldehyde. Combining spectroscopy with molecular dynamics and electronic structure partitioning methods, we demonstrate that, despite distinct polarities, solvents act similarly in their preference for electrostatically stabilizing large bond dipoles at the expense of destabilizing small ones. In contrast, we find that for an active-site aldehyde inhibitor of liver alcohol dehydrogenase, the electric field orientation deviates markedly from that found in solvents, which provides direct evidence for the fundamental difference between the electrostatic environment of solvents and that of a preorganized enzyme active site.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Quantum simulation of conical intersections using trapped ions

Conical intersections often control the reaction products of photochemical processes and occur when two electronic potential energy surfaces intersect. Theory predicts that the conical intersection will result in a geometric phase for a wavepacket on the ground potential energy surface, and although conical intersections have been observed experimentally, the geometric phase has not been directly observed in a molecular system. Herein we use a trapped atomic ion system to perform a quantum simulation of a conical intersection. The ion’s internal state serves as the electronic state, and the motion of the atomic nuclei is encoded into the motion of the ions. The simulated electronic potential is constructed by applying state-dependent optical forces to the ion. We experimentally observe a clear manifestation of the geometric phase using adiabatic state preparation followed by motional state measurement. Our experiment shows the advantage of combining spin and motion degrees for quantum simulation of chemical reactions.

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

Nature of polariton transport in a Fabry-Perot cavity

Fabry-Perot microcavities can strongly enhance interactions between light and molecules, leading to the formation of hybrid light-matter states known as polaritons. Polaritons possess much smaller effective masses and much larger group velocities when the molecules are resonant with cavity modes that have finite (nonzero) in-plane wave vectors, giving rise to the possibilities of long-range and ultrafast ballistic transport. In this paper, we present the results of numerical simulations of the ultrafast ballistic transport phenomenon in real space and time during and after initialization with a short, spatially localized pulse. Here, we address this problem with two approaches: the standard transfer-matrix method (TMM) for planar structures and a second simulation based on the numerical solution of the Maxwell-Bloch equations that can be used for general configurations (complex metasurfaces, for instance) and excitation modes. The agreement between the TMM and the full numerical calculation with the Maxwell-Bloch equations when applied to multilayer planar configurations provides proof of the validity of both approaches for the present analyses. Overall, we find that the transport of the molecular excitons inside the cavity synchronize with the evolution of the enhanced electromagnetic field inside the cavity. Moreover, the synchronized transport rate is in good agreement with the group velocities predicted from a calculated dispersion relation across a wide range of frequencies. Finally, we relate the group velocity to the Hopfield coefficient obtained from quantum modeling and suggest that the dependence of light-matter coupling on the in-plane wave vector can be an important but overlooked factor for understanding the transport behavior of polaritons. These simulations provide an intuitive tool for understanding the collective motion of light and excitons and helps us to better understand how experimental observations of polaritons should be interpreted.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Resonant Inelastic X-Ray Scattering Reveals Hidden Local Transitions of the Aqueous OH Radical

Resonant inelastic x-ray scattering (RIXS) provides remarkable opportunities to interrogate ultrafast dynamics in liquids. In this work we use RIXS to study the fundamentally and practically important hydroxyl radical in liquid water, OH ( a q ) . Impulsive ionization of pure liquid water produced a short-lived population of OH ( a q ) , which was probed using femtosecond x-rays from an x-ray free-electron laser. We find that RIXS reveals localized electronic transitions that are masked in the ultraviolet absorption spectrum by strong charge-transfer transitions—thus providing a means to investigate the evolving electronic structure and reactivity of the hydroxyl radical in aqueous and heterogeneous environments. First-principles calculations provide interpretation of the main spectral features.

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

Active and Transfer Learning of High-Dimensional Neural Network Potentials for Transition Metals

Classical molecular dynamics (MD) simulations represent a very popular and powerful tool for materials modeling and design. The predictive power of MD hinges on the ability of the interatomic potential to capture the underlying physics and chemistry. There have been decades of seminal work on developing interatomic potentials, albeit with a focus predominantly on capturing the properties of bulk materials. Such physics-based models, while extensively deployed for predicting the dynamics and properties of nanoscale systems over the past two decades, tend to perform poorly in predicting nanoscale potential energy surfaces (PESs) when compared to high-fidelity first-principles calculations. These limitations stem from the lack of flexibility in such models, which rely on a predefined functional form. Machine learning (ML) models and approaches have emerged as a viable alternative to capture the diverse size-dependent cluster geometries, nanoscale dynamics, and the complex nanoscale PESs, without sacrificing the bulk properties. Here, in this study, we introduce an ML workflow that combines transfer and active learning strategies to develop high-dimensional neural networks (NNs) for capturing the cluster and bulk properties for several different transition metals with applications in catalysis, microelectronics, and energy storage, to name a few. Our NN first learns the bulk PES from the high-quality physics-based models in literature and subsequently augments this learning via retraining with a higher-fidelity first-principles training data set to concurrently capture both the nanoscale and bulk PES. Our workflow departs from status-quo in its ability to learn from a sparsely sampled data set that nonetheless covers a diverse range of cluster configurations from near-equilibrium to highly nonequilibrium as well as learning strategies that iteratively improve the fingerprinting depending on model fidelity. All the developed models are rigorously tested against an extensive first-principles data set of energies and forces of cluster configurations as well as several properties of bulk configurations for 10 different transition metals. Our approach is material agnostic and provides a methodology to transfer and build upon the learnings from decades of seminal work in molecular simulations on to a new generation of ML-trained potentials to accelerate materials discovery and design.

36 MATERIALS SCIENCE↗
DOE OSTI2022

LDRD External Report Summary

Matrix-Assisted Ionization is a new and highly promising mass spectrometry technique for rapid analysis of uranium; however, the chemistry and physics underlying this ionization mechanism are largely unknown. This effort seeks to understand the fundamental ionization phenomena by characterizing both ionization matrix materials themselves and analyte chemistry ionization trends.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2024

A linear response framework for quantum simulation of bosonic and fermionic correlation functions

Response functions are a fundamental aspect of physics; they represent the link between experimental observations and the underlying quantum many-body state. However, this link is often under-appreciated, as the Lehmann formalism for obtaining response functions in linear response has no direct link to experiment. Within the context of quantum computing, and via a linear response framework, we restore this link by making the experiment an inextricable part of the quantum simulation. This method can be frequency- and momentum-selective, avoids limitations on operators that can be directly measured, and can be more efficient than competing methods. As prototypical examples of response functions, we demonstrate that both bosonic and fermionic Green’s functions can be obtained, and apply these ideas to the study of a charge-density-wave material on the ibm_auckland superconducting quantum computer. The linear response method provides a robust framework for using quantum computers to study systems in physics and chemistry.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Obtaining bulk-like correlated oxide surfaces with protective caps

Functional oxides exhibit a diverse range of correlated electron phenomena, some of which are highly attractive for novel electronic, magnetic, and optical devices. Despite decades of advancement of our fundamental understanding of these materials, they consistently fall short of realizing their promise in functional devices. We identify a significant bottleneck toward device realization to be surface overoxidation. Protective caps can effectively prevent overoxidation, but their interfaces with functional oxides are not well understood. These interfaces are critical for effectively using functional oxides in field-effect devices, where “the interface is the device.” This work addresses the chemistry and physics of the interface between protective caps and the correlated metal SrVO3, a model functional oxide. Our comparison of five different cap materials reveals effective protection and similar SrVO3 surface chemistry in all cases. Systematic comparisons of surface and bulk-sensitive photoelectron spectra reveal that negligible interface redox takes place, elucidating the cap-SrVO3 interface chemistry. This work demonstrates a robust and simple solution to the surface overoxidation problem in vanadates, paving the way toward effectively using these materials in field-effect devices. Our conclusions are general and can be applied to numerous other systems, thus moving oxide electronics closer to the realization of functional devices.

Cohen, Amit (ORCID:0009000276477510)↗
DOE OSTI2023

Accurate prediction of global-density-dependent range-separation parameters based on machine learning

In this work, we develop an accurate and efficient XGBoost machine learning model for predicting the global-density-dependent range-separation parameter, ωGDD, for long-range corrected functional (LRC)-ωPBE. This ωGDDML model has been built using a wide range of systems (11 466 complexes, ten different elements, and up to 139 heavy atoms) with fingerprints for the local atomic environment and histograms of distances for the long-range atomic correlation for mapping the quantum mechanical range-separation values. The promising performance on the testing set with 7046 complexes shows a mean absolute error of 0.001 117 a0−1 and only five systems (0.07%) with an absolute error larger than 0.01 a0−1, which indicates the good transferability of our ωGDDML model. In addition, the only required input to obtain ωGDDML is the Cartesian coordinates without electronic structure calculations, thereby enabling rapid predictions. LRC-ωPBE(ωGDDML) is used to predict polarizabilities for a series of oligomers, where polarizabilities are sensitive to the asymptotic density decay and are crucial in a variety of applications, including the calculations of dispersion corrections and refractive index, and surpasses the performance of all other popular density functionals except for the non-tuned LRC-ωPBE. Finally, LRC-ωPBE (ωGDDML) combined with (extended) symmetry-adapted perturbation theory is used in calculating noncovalent interactions to further show that the traditional ab initio system-specific tuning procedure can be bypassed. The present study not only provides an accurate and efficient way to determine the range-separation parameter for LRC-ωPBE but also shows the synergistic benefits of fusing the power of physically inspired density functional LRC-ωPBE and the data-driven ωGDDML model.

Chemistry↗
DOE OSTI2026

Maximizing efficiency of dataset compression for machine learning potentials with information theory

Machine learning interatomic potentials (MLIPs) balance high accuracy and lower costs compared to density functional theory calculations, but their performance often depends on the size and diversity of training datasets. Large datasets improve model accuracy and generalization but are computationally expensive to produce and train on, while smaller datasets risk discarding rare but important atomic environments and compromising MLIP accuracy/reliability. Here, we develop an information-theoretical framework to quantify the efficiency of dataset compression methods and propose an algorithm that maximizes this efficiency. By framing atomistic dataset compression as an instance of the minimum set cover (MSC) problem over atom-centered environments, our method identifies the smallest subset of structures that contains as much information as possible from the original dataset while pruning redundant information. The approach is extensively demonstrated on the GAP-20 and TM23 datasets and validated on 64 varied datasets from the ColabFit repository. Across all cases, MSC consistently retains outliers, preserves dataset diversity, and reproduces the long-tail distributions of forces even at high compression rates, outperforming other subsampling methods. Furthermore, MLIPs trained on MSC-compressed datasets exhibit reduced error for out-of-distribution data even in low-data regimes. We explain these results using an outlier analysis and show that such quantitative conclusions could not be achieved with conventional dimensionality reduction methods. The algorithm is implemented in the open-source QUESTS package and can be used for several tasks in atomistic modeling, from data subsampling, outlier detection, and training improved MLIPs at a lower cost.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Plasma thermal-chemical instability of low-temperature dimethyl ether oxidation in a nanosecond-pulsed dielectric barrier discharge

Plasma stability in reactive mixtures is critical for various applications from plasma-assisted combustion to gas conversion. To generate stable and uniform plasmas and control the transition towards filamentation, the underlying physics and chemistry need a further look. Here, this work investigates the plasma thermal-chemical instability triggered by dimethyl-ether (DME) low-temperature oxidation in a repetitive nanosecond pulsed dielectric barrier discharge. First, a plasma-combustion kinetic mechanism of DME/air is developed and validated using temperature and ignition delay time measurements in quasi-uniform plasmas. Then the multi-stage dynamics of thermal-chemical instability is experimentally explored: the DME/air discharge was initially uniform, then contracted to filaments, and finally became uniform again before ignition. By performing chemistry modeling and analyzing the local thermal balance, it is found that such nonlinear development of the thermal-chemical instability is controlled by the competition between plasma-enhanced low-temperature heat release and the increasing thermal diffusion at higher temperature. Further thermal-chemical mode analysis identifies the chemical origin of this instability as DME low-temperature chemistry. This work connects experiment measurements with theoretical analysis of plasma thermal-chemical instability and sheds light on future chemical control of the plasma uniformity.

repetitive nanosecond pulses↗
DOE OSTI2021

Demonstration of neutron radiation-induced nucleation of supercooled water

In this study, we present direct evidence for neutrons causing nucleation of supercooled water. Highly purified water (20 nm filtration) is cooled to well below freezing (as low as −20 °C) with a radioactive calibration source of neutrons/gamma-rays either present or removed during each of many control cooling runs for the same volume of water. When it is primarily neutrons irradiating the sample bulk, the non-equilibrium freezing point (also known as the “supercooling point”) is, on average, +0.7 °C warmer than the control equivalent, with a statistical significance of greater than 5 Sigma, with systematic uncertainty included. This effect is not observed with water in the presence of gamma-rays instead of neutrons. While these neutrons should have theoretically had sufficient energy to mount the energy barrier, corroborating our results, their raising of supercooling temperature has never been reported experimentally to the best of our knowledge. The potential to use deeply supercooled solutions, not only water, as metastable detectors for radiation and perhaps dark matter or neutrino physics presents now a new avenue for exploration.

Szydagis, Matthew↗
DOE OSTI2024

Insights into the structure and dynamics of K + ions at the muscovite–water interface from machine learning potential simulations

The surfaces of many minerals are covered by naturally occurring cations that become partially hydrated and can be replaced by hydronium or other cations when the surface is exposed to water or an aqueous solution. These ion exchange processes are relevant to various chemical and transport phenomena, yet elucidating their microscopic details is challenging for both experiments and simulations. Here, in this work, we make a first step in this direction by investigating the behavior of the native K + ions at the interface between neat water and the muscovite mica (001) surface with ab-initio -based machine learning molecular dynamics and enhanced sampling simulations. Our results show that the desorption of the surface K + ions in pure ion-free water has a significant free energy barrier irrespective of their local surface arrangement. In contrast, facile K + diffusion between mica’s ditrigonal cavities characterized by different Al/Si orderings is observed. This behavior suggests that the K + ions may favor a dynamic disordered surface arrangement rather than complete desorption when exposed to deionized water.

Ab-initio molecular dynamics↗
DOE OSTI2026

Connecting collisional and photofragmentation resonances in the ungerade symmetry states of H 2

A recently developed energy-dependent frame transformation theory that incorporates both ionisation and dissociation channels of the H 2 molecule, is extended to treat the ungerade states that occur both in dissociative recombination and as the final state in ground state photoabsorption. The theoretical treatment includes the rotational degrees of freedom and is benchmarked against a two-dimensional model that can be solved with high accuracy and also compared with photoabsorption experiments. Analysis of the resulting spectra demonstrates how the same resonances appear in very different observables, often with quite different line shapes.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Competing ionization and dissociation: Extension of the energy-dependent frame transformation to the gerade symmetry of H 2

This article solves two major tasks that frequently arise in the theory of electron collisions with a target molecular cation. First, it extends the energy-dependent frame transformation (EDFT) treatment, which is needed to map fixed-nuclei electron-molecule scattering matrices into an energy-dependent laboratory-frame scattering matrix with vibrational channel indices. The EDFT mapping can now be carried out even when the target molecule possesses multiple low-energy potential curves, significantly transcending previous applications. Second, it implements a method to extract the rest of the full laboratory-frame scattering matrix, i.e., the columns and rows describing input and/or output dissociation channels. The treatment is benchmarked in this article against the essentially exact solution of a refined two-dimensional model of the singlet gerade Σ symmetry of H 2 . Our tests demonstrate that the theory accurately maps fixed-nuclei scattering information, of the type provided by existing electron-molecule computer codes, into a laboratory-frame scattering matrix that includes both ionization and dissociation. Furthermore, this treatment can provide a general framework applicable to a broad class of electron collision processes involving diatomic target ions, suitable for an accurate description of challenging processes such as dissociative recombination.

74 ATOMIC AND MOLECULAR PHYSICS↗