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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 1,279 records · Page 71
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Aqueous-Phase Acetic Acid Ketonization over Monoclinic Zirconia

The effect of aqueous phase on the acetic acid ketonization over monoclinic zirconia has been investigated using first-principles based density functional theory (DFT) calculations. To capture the aqueous phase chemistry over the solid zirconia catalyst surface, the aqueous phase is represented by 111 explicit water molecules with a liquid water density of 0.93 g/cm3 and the monoclinic zirconia is modeled by the most stable surface structure . The dynamic nature of aqueous phase/ interface was studied using ab initio molecular dynamics simulation, indicating that nearly half of the surface Zr sites are occupied by either adsorbed water molecules or hydroxyl groups at 550 K. DFT calculations show that the adsorption process of acetic acid from the liquid water phase to the surface is nearly thermodynamically neutral with a Gibbs free energy of -2.3 kJ/mol although the adsorption strength of acetic acid on the surface in aqueous phase is much stronger than in vapor phase. Therefore it is expected that the adsorption of acetic acid will dramatically affects aqueous phase ketonization reactivity over the monoclinic zirconia catalyst. Using the same ketonization mechanism via the β-keto acid intermediate, we have compared acetic acid ketonization to acetone in both vapor and aqueous phases. Our DFT calculation results show although the rate-determining step of the β-keto acid formation via the C-C coupling is not pronouncedly affected, the presence of liquid water molecules will dramatically affect dehydrogenation and hydrogenation steps via proton transfer mechanism. This work was financially supported by the United States Department of Energy (DOE)’s Bioenergy Technologies Office (BETO) and performed at the Pacific Northwest National Laboratory (PNNL). PNNL is a multi-program national laboratory operated for DOE by Battelle Memorial Institute. Computing time and advanced catalyst characterization use was granted by a user proposal at the William R. Wiley Environmental Molecular Sciences Laboratory (EMSL). EMSL is a national scientific user facility sponsored by the Department of Energy’s Office of Biological and Environmental Research and located at PNNL.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Surface Reconstructions of Metal Oxides and the Consequences on Catalytic Chemistry

Catalysts are inherently vital in nature, as they respond to the environment by changing their local and extended structures. Surface reconstruction is among such dynamic behaviors of catalysts and greatly affects the physical, chemical, and electronic properties of catalysts and consequently the catalytic performances. Thus, understanding the nature of the catalytic sites of the reconstructed surfaces is essential for establishing the structure–catalysis relations and has attracted much interest in catalysis research. Knowledge of the reconstruction of metal oxides has been quite limited in comparison to that of metal surfaces because the nature of oxide surfaces is generally more complex. However, significant progress has been made in recent years in oxide surfaces thanks to the advances in the ability to synthesize model oxide nanocrystals and to characterize the surface reconstruction behaviors under in situ and operando conditions with advanced spectroscopy and microscopy aided by computational modeling. In this Perspective, such advances in understanding the surface reconstruction behaviors of metal oxide nanoshapes and thin films with well-defined surface structures under as-synthesized, various pretreatment, and reaction environments will be summarized, and the catalytic consequences of these reconstructions will be highlighted. The results from these studies clearly show that the combination of model oxides and in situ/operando investigations is imperative in shaping the fundamental understanding of the dynamic reconstructions of oxide surfaces. Yet, it is still difficult to study the surface reconstructions of oxide catalysts with meaningful temporal and spatial resolution under operating conditions. Future opportunities are discussed on how to address the challenges and eventually help to design more efficient catalysts by taking advantages of the surface reconstructions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Rationalization of Reduced Penetration of Drugs through Ceramide Gel Phase Membrane

Since computing resources have advanced enough to allow routine molecular simulation studies of drug molecules interacting with biologically relevant membranes, a considerable amount of work has been carried out with fluid phospholipid systems. However, there is very little work in the literature on drug interactions with gel phase lipids. This poses a significant limitation for understanding permeation through the stratum corneum where the primary pathway is expected to be through a highly ordered lipid matrix. To address this point, we analyzed the interactions of p-aminobenzoic acid (PABA) and its ethyl (benzocaine) and butyl (butamben) esters with two membrane bilayers, which differ in their fluidity at ambient conditions. We considered a dioleoylphosphatidylcholine (DOPC) bilayer in a fluid state and a ceramide 2 (CER2, ceramide NS) bilayer in a gel phase. We carried out unbiased (100 ns long) and biased z-constraint molecular dynamics simulations and calculated the free energy profiles of all molecules along the bilayer normal. The free energy profiles converged significantly slower for the gel phase. While the compounds have comparable affinities for both membranes, they exhibit penetration barriers almost 3 times higher in the gel phase CER2 bilayer. This elevated barrier and slower diffusion in the CER2 bilayer, which are caused by the high ordering of CER2 lipid chains, explain the low permeability of the gel phase membranes. We also compared the free energy profiles from MD simulations with those obtained from COSMOmic. This method provided the same trends in behavior for the guest molecules in both bilayers; however, the penetration barriers calculated by COSMOmic did not differ between membranes. Finally, we show how membrane fluid properties affect the interaction of drug-like molecules with membranes.

chemistry↗
DOE OSTI2025

Perspectives on Systematic Cloud Microphysics Scheme Development With Machine Learning

Cloud microphysics—the collection of processes that govern the small‐scale formation, evolution, and interactions of liquid droplets and ice crystals in clouds and precipitation—remains a major source of uncertainty in weather and climate models. Although too small in scale to be explicitly resolved in any large‐eddy simulation, weather, or climate model, the representation of cloud microphysical processes has significant impact at the climate scale. Current microphysical schemes are limited by both parametric uncertainty, linked to uncertainty in physical parameter values, and structural uncertainty, arising from incomplete physical understanding of the processes at play or approximations made for computational efficiency. Recent advances in the application of machine learning (ML) to the physical sciences show significant potential for minimizing these limitations by leveraging high‐fidelity simulations and observations. Here we outline the challenges that must be addressed to apply ML toward cloud microphysics scheme development. This perspectives paper synthesizes recent progress in using data‐driven methods, including ML, to improve cloud microphysics parameterizations and highlights opportunities to address key uncertainties. We discuss the roles of aleatoric (irreducible, or statistical) and epistemic (reducible, or systematic) errors in contributing to microphysics parameterization uncertainty. ML can leverage observations to improve microphysical schemes via bottom‐up and top‐down constraints. Methods such as differentiable programming and ML‐enhanced sampling strategies and the creation of large scale benchmark data sets promise to bridge the gap between observations and models and to improve the consistency of cloud microphysical representation across temporal and spatial scales.

Lamb, Kara D. [Columbia Univ., New York, NY (Unite↗
DOE OSTI2015

The structure of SpnF a standalone enzyme that catalyzes [4 + 2] cycloaddition

In the biosynthetic pathway of the spinosyn insecticides, the tailoring enzyme SpnF performs a [4 + 2] cycloaddition on a 22-membered macrolactone to forge an embedded cyclohexene ring. To learn more about this reaction, which could potentially proceed through a Diels-Alder mechanism, in this paper we determined the 1.50-Å-resolution crystal structure of SpnF bound to S-adenosylhomocysteine. Finally, this sets the stage for advanced experimental and computational studies to determine the precise mechanism of SpnF-mediated cyclization.

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

Down-selection of biomolecules to assemble “reverse micelle” with perovskites

Abstract Biological molecule-semiconductor interfacing has triggered numerous opportunities in applied physics such as bio-assisted data storage and computation, brain-computer interface, and advanced distributed bio-sensing. The introduction of electronics into biological embodiment is being quickly developed as it has great potential in providing adaptivity and improving functionality. Reciprocally, introducing biomaterials into semiconductors to manifest bio-mimetic functionality is impactful in triggering new enhanced mechanisms. In this study, we utilize the vulnerable perovskite semiconductors as a platform to understand if certain types of biomolecules can regulate the lattice and endow a unique mechanism for stabilizing the metastable perovskite lattice. Three tiers of biomolecules have been systematically tested and the results reveal a fundamental mechanism for the formation of a “reverse-micelle” structure. Systematic exploration of a large set of biomolecules led to the discovery of guiding principle for down-selection of biomolecules which extends the classic emulsion theory to this hybrid systems. Results demonstrate that by introducing biomaterials into semiconductors, natural phenomena typically observed in biological systems can also be incorporated into semiconducting crystals, providing a new perspective to engineer existing synthetic materials.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Chirality manipulation of ultrafast phase switches in a correlated CDW-Weyl semimetal

Light engineering of correlated states in topological materials provides a new avenue of achieving exotic topological phases inaccessible by conventional tuning methods. Here we demonstrate a light control of correlation gaps in a model charge-density-wave (CDW) and polaron insulator (TaSe 4 ) 2 I recently predicted to be an axion insulator. Our ultrafast terahertz photocurrent spectroscopy reveals a two-step, non-thermal melting of polarons and electronic CDW gap via the fluence dependence of a longitudinal circular photogalvanic current. This helicity-dependent photocurrent reveals continuous ultrafast phase switches from the polaronic state to the CDW (axion) phase, and finally to a hidden Weyl phase as the pump fluence increases. Additional distinctive attributes aligning with the light-induced switches include: the mode-selective coupling of coherent phonons to the polaron and CDW modulation, and the emergence of a non-thermal chiral photocurrent above the pump threshold of CDW-related phonons. The demonstrated ultrafast chirality control of correlated topological states here holds large potentials for realizing axion electrodynamics and advancing quantum-computing applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Proton-exchange induced reactivity in layered oxides for lithium-ion batteries

LiNi x Co y Mn 1-x-y O 2 (0 < x, y < 1, NCM) is the dominant positive material for the state-of-the-art lithium-ion batteries. However, the sensitivity of NCM materials to moisture makes their manufacturing, storage, transportation, electrode processing and recycling complicated. Although it is recognized that protons play a critical role in their structure stability and performance, proton exchange with Li + in NCM materials has not been well understood. Here, we employ advanced characterizations and computational studies to elucidate how protons intercalate into the layered structure of NCM, leading to the leaching of Li + and the formation of protonated NCM. It is found that protonation facilitates cation rearrangement and formation of impurity phases in NCM, significantly deteriorating structural stability. The adverse effects induced by protons become increasingly pronounced with a higher Ni content in NCM. Through a comprehensive investigation into the thermodynamics and kinetics of protonation, we discover that Li deficiencies in NCM materials can be resolved via solution process in the presence of Li + ions and controlled proton concentration. The underlying mechanism of relithiation is further explored through materials characterizations and kinetics modeling. This work provides crucial insights into controlling structural and compositional defects of Li-ion battery positive material in complicated processing environment.

batteries↗
DOE OSTI2023

Predicting electronic structures at any length scale with machine learning

The properties of electrons in matter are of fundamental importance. They give rise to virtually all material properties and determine the physics at play in objects ranging from semiconductor devices to the interior of giant gas planets. Modeling and simulation of such diverse applications rely primarily on density functional theory (DFT), which has become the principal method for predicting the electronic structure of matter. While DFT calculations have proven to be very useful, their computational scaling limits them to small systems. We have developed a machine learning framework for predicting the electronic structure on any length scale. It shows up to three orders of magnitude speedup on systems where DFT is tractable and, more importantly, enables predictions on scales where DFT calculations are infeasible. Our work demonstrates how machine learning circumvents a long-standing computational bottleneck and advances materials science to frontiers intractable with any current solutions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Knowledge graph-aided Bayesian active learning for top- K genetic interaction discovery

In silico methods for predicting the effects of multi-gene perturbations hold great promise for advancing functional genomics, computational drug discovery, and disease modeling. However, the development of these predictive algorithms for mammalian systems has been hampered by limited datasets and high experimental costs. In this study, we present a Bayesian active learning framework designed to discover pairwise host gene knockdowns that effectively inhibit viral proliferation in an in vitro HIV-1 infection model. Our method leverages a biological knowledge graph as side information and employs a computationally efficient batch diversification approach. We evaluated this framework using a dataset of viral load measurements obtained from multi-day dual-gene depletion experiments, encompassing all possible pairwise knockdowns of over 350 host genes associated with HIV infection. We demonstrate that our framework rapidly identifies the most effective gene knockdown pairs for reducing viral load. Furthermore, we show that incorporating side information enhances performance during the early stages of active learning (low data regime), while our batch diversification strategy significantly boosts performance in later stages (high data regime). This framework is general and can be adapted to explore gene interactions in other contexts, such as synthetic lethality prediction and mapping epistatic effects across quantitative trait loci.

Computational biology and bioinformatics↗
DOE OSTI2024

Atomic-scale identification of active sites of oxygen reduction nanocatalysts

Heterogeneous nanocatalysts play a crucial role in both the chemical and energy industries. Despite substantial advancements in theoretical, computational and experimental studies, identifying their active sites remains a major challenge. Here we utilize atomic electron tomography to determine the three-dimensional atomic structure of PtNi and Mo-doped PtNi nanocatalysts for the electrochemical oxygen reduction reaction. We then employ the experimental atomic structures as input to first-principles-trained machine learning to identify the active sites of the nanocatalysts. Through the analysis of the structure–activity relationships, we formulate an equation termed the local environment descriptor, which balances the strain and ligand effects to provide physical and chemical insights into active sites in the oxygen reduction reaction. The ability to determine the three-dimensional atomic structure and chemical composition of realistic nanoparticles, combined with machine learning, could transform our fundamental understanding of the active sites of catalysts and guide the rational design of optimal nanocatalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Advances in geophysical forensic event monitoring

Forensic analysis of man-made, non-nuclear events (such as industrial accidents, explosion experiments and mine collapses) has become more frequent and detailed owing to advancements in geophysical monitoring. Here, in this Technical Review, we demonstrate how geophysical forensic monitoring using seismic, infrasound and hydroacoustic recordings provides insights on events in the solid earth, atmosphere and underwater. Advanced techniques, including machine-learning-based models, have been developed to detect, identify and investigate these events, providing information on location, subevents, sources and explosive yield. The increase in data availability, application of advanced methods and computation and the growth of multitechnology approaches have increased the accuracy of forensic event analysis and enabled more realistic characterization of uncertainties. For example, the 2020 Beirut explosion in Lebanon demonstrated that various seismic, acoustic and other methods could be used to estimate explosive yield (and yield uncertainties) of about 1 ktonne, providing confidence in the application of these methods to smaller events where data are available. However, forensic investigations remain largely limited to known events with identified sources. Increased access to data, sophisticated analysis methods and high-resolution earth models will improve forensic event analysis further, enabling civil and scientific applications, such as localization in the search for the lost ARA San Juan submarine.

geophysics↗
DOE OSTI2020

A review of composite solid-state electrolytes for lithium batteries: fundamentals, key materials and advanced structures

All-solid-state lithium ion batteries (ASSLBs) are considered next-generation devices for energy storage due to their advantages in safety and potentially high energy density. As the key component in ASSLBs, solid-state electrolytes (SSEs) with non-flammability and good adaptability to lithium metal anodes have attracted extensive attention in recent years. Among the current SSEs, composite solid-state electrolytes (CSSEs) with multiple phases have greater flexibility to customize and combine the advantages of single-phase electrolytes, which have been widely investigated recently and regarded as promising candidates for commercial ASSLBs. Based on existing investigations, herein, we present a comprehensive overview of the recent developments in CSSEs. Initially, we introduce the historical development from solid-state ionic conductors to CSSEs, and then summarize the fundamentals including mechanisms of lithium ion transport, key evaluation parameters, design principles, and key materials. Finally, four main types of advanced structures for CSSEs are classified and highlighted according to the recent progress. Moreover, advanced characterization and computational simulation techniques including machine learning are reviewed for the first time, and the main challenges and perspectives of CSSEs are also provided for their future development.

25 ENERGY STORAGE↗
DOE OSTI2026

ToPolyAgent: AI agents for coarse-grained bead-spring topological polymer simulations

We introduce ToPolyAgent, a multi-agent AI framework for performing coarse-grained molecular dynamics (MD) simulations of topological polymers through natural language instructions. By integrating large language models (LLMs) with domain-specific computational tools, ToPolyAgent supports both interactive and autonomous simulation workflows across diverse polymer architectures, including linear, ring, brush, and star polymers, as well as dendrimers. The system consists of four LLM-powered agents: a Config Agent for generating initial polymer–solvent configurations, a Simulation Agent for executing LAMMPS-based MD simulations and conformational analyses, a Report Agent for compiling markdown reports, and a Workflow Agent for streamlined autonomous operations. Interactive mode incorporates user feedback loops for iterative refinements, while autonomous mode enables end-to-end task execution from detailed prompts. We demonstrate ToPolyAgent's versatility through case studies involving diverse polymer architectures under varying solvent conditions, thermostats, and simulation lengths. Furthermore, we highlight its potential as a research assistant by directing it to investigate the effect of interaction parameters on the linear polymer conformation, and the influence of grafting density on the persistence length of the brush polymer. By coupling natural language interfaces with rigorous simulation tools, ToPolyAgent lowers barriers to complex computational workflows and advances AI-driven materials discovery in polymer science. It lays the foundation for autonomous and extensible multi-agent scientific research ecosystems.

Ding, Lijie [Oak Ridge National Laboratory (ORNL),↗
DOE OSTI2020

Quantum simulations employing connected moments expansions

Further advancement of quantum computing (QC) is contingent on enabling many-body models that avoid deep circuits and excessive use of CNOT gates. To this end, we develop a QC approach employing finite-order connected moment expansions (CMX) and affordable procedures for initial state preparation. We demonstrate the performance of our approach employing several quantum variants of CMX through the classical emulations on the H2 molecule potential energy surface and the Anderson model with a broad range of correlation strength. The results show that our approach is robust and flexible. Good agreements with exact solutions can be maintained even at the dissociation and strong correlation limits.

quantum computing, quantum chemistry, Electron cor↗
DOE OSTI2021

Particle resonances in stellarators

Resonances of high energy particles in magnetic confinement devices due to electromagnetic instabilities can strongly modify the particle distribution, leading to a reduction in fusion power and even discharge termination and particle loss to the device walls through an avalanche. The existence of a mode particle resonance depends on the properties of the equilibrium and particle parameters, and their number, location, and density can vary with device design. Recently, the advent of more powerful computing capabilities and advanced theoretical understanding has led to the design of non-axisymmetric devices or stellarators, which could prove to be more advantageous than tokamaks. Stellarators have the advantage of being immune to major disruptions because of the very low plasma current. One of the problems shared by both types of devices is the existence of resonances in particle orbits, which can lead to large amplitude high frequency instabilities and subsequent induced particle loss. We examine the number of resonances, their location, and dependence on particle energy for some stellarator designs.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Ultraefficient resistance switching between charge ordered phases in 1 T -TaS 2 with a single picosecond electrical pulse

Progress in high-performance computing demands significant advances in memory technology. Among novel memory technologies that promise efficient device operation on a sub-ns timescale, resistance switching between charge ordered phases of 1T-TaS 2 has shown to be potentially useful for development of high-speed, energy efficient nonvolatile memory devices. Measurement of the electrical operation of such devices in the picosecond regime is technically challenging and hitherto still largely unexplored. Here, we use an optoelectronic “laboratory-on-a-chip” experiment for measurement of ultrafast memory switching, enabling accurate measurement of electrical switching parameters with 100 fs temporal resolution. Photoexcitation and electro-optic sampling on a (Cd,Mn)Te substrate are used to generate and, subsequently, measure electrical pulse propagation with intra-band excitation and sub-gap probing, respectively. We demonstrate high contrast nonvolatile resistance switching from high to low resistance states of a 1T-TaS 2 device using single sub-2ps electrical pulses. Using detailed modeling, we find that the switching energy density per unit area is exceptionally small, E A =9.4 fJ/μm 2 . Finally, the speed and energy efficiency of an electronic “write” process place the 1T-TaS 2 devices into a category of their own among new generation nonvolatile memory devices.

42 ENGINEERING↗