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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Real-time infrared spectroscopy coupled with blind source separation for nuclear waste process monitoring

On-line infrared absorbance spectroscopy enables rapid measurement of solution-phase molecular species. Many spectra-to-concentration models exist for spectral data, with some models able to handle overlapping spectral bands and nonlinearities. However, model accuracy is limited by the quality of training data used in model fitting. The process spectra of nuclear waste simulants at the Savannah River Site display incongruity between training and process spectra; the glycolate spectral signature in the training data does not match the glycolate signature in Savannah River National Laboratory process data. A novel blind source separation algorithm is proposed that preprocesses spectral data so that process spectra more closely resemble training spectra, thereby improving model quantification accuracy when unexpected sources of variation appear in process spectra. The novel blind source separation preprocessing algorithm is shown to improve nitrate quantification from an R 2 of 0.934 to 0.988 and from 0.267 to 0.978 in two instances analyzing nuclear waste simulants from the Slurry Receipt Adjustment Tank and Slurry Mix Evaporator cycle at the Savannah River Site.

Crouse, Steven H.↗
DOE OSTI2021

KS4A-Omics1.0_FspDS682

Soil fungi facilitate the translocation of inorganic nutrients from soil minerals to other microorganisms and plants. This ability is particularly advantageous in impoverished soils, because fungal mycelial networks can bridge otherwise spatially disconnected and inaccessible nutrient hotspots. However, the molecular mechanisms underlying fungal mineral weathering and transport through soil remains poorly understood. Here, we addressed this knowledge gap by directly visualizing nutrient acquisition and transport through fungal hyphae in a mineral doped soil micromodel using a multimodal imaging approach. Here, we observed how a representative of common saprotrophic soil fungi, Fusarium sp. DS 682, exhibited a mechanosensory response (thigmotropism) around obstacles and through pore spaces (~12 μm) in the presence of minerals.This study establishes the significance of fungal biology and nutrient translocation mechanisms in maintaining fungal growth under water and nutrient limitations in a soil-like microenvironment, using a high-throughput multi-omic analysis approach. Data package KS4A-Omics.1.0_FspDS682 (Publication: Fungal Mineral Weathering Mechanisms Revealed Through Direct Molecular Visualization) contents reported here are the first version (1.0) and contain pre- and post-processed data using high throughput data capture technologies for multi-omic analysis and integration, this data package contains raw and post-processed experimental data for X-Ray Absorption Near Edge Structure Spectroscopy (XANES/XRF), Optical Microscopy, Proteomics, Scanning Electron Microscope (SEM), Time-of-Flight Secondary Ion Mass Spectroscopy (ToF-SIMS), X-Ray Diffraction Spectroscopy (XRD) files, and X- Ray Photoelectron Spectroscopy (XPS) using EMSL capabilities. This data package DOI contains a comprehensive collection of high-throughput multi-omics data and process metadata catalog. Support files include additional data download contents “Read Me” with dataset descriptor information and data source method application ontologies (see data dictionary section). Reported data download content is structured for compliance with reported guidelines provided by community standard initiatives and publisher stakeholder policies supporting FAIR data principles.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Crystal structure of dihydrofolate reductase from the filarial nematode W. bancrofti in complex with NADPH and folate

Lymphatic filariasis is a debilitating illness with an estimated 50 million cases as of 2018. The majority of cases are caused by the parasitic worm W. bancrofti and additional cases by the worms B. malayi and B. timori. Dihydrofolate reductase (DHFR) is an established target in the treatment of cancer, bacterial, and protozoal infections and may be a potential target for drugs targeting parasitic worm infections, including filariasis. Recent studies have shown that known antifolate compounds, including methotrexate, inhibit the activity of W. bancrofti DHFR (WbDHFR). However, the absence of structural information for filarial DHFRs has limited the study of more in-depth structure-function relationships. We report the structure of WbDHFR complexed with NADPH and folate using X-ray diffraction data measured to 2.47 Å resolution. The structure of WbDHFR reveals the usual DHFR fold and is currently only the second nematode DHFR structure in the Protein Data Bank. The equilibrium dissociation constants for NADPH (90 ± 29 nM) and folate (23 ± 4 nM) were determined by equilibrium titrations. The interactions of known antifolates with WbDHFR were analyzed using molecular docking programs and molecular dynamics simulations. Antifolates with a hydrophobic core and extended linker formed favorable interactions with WbDHFR. These combined data should now facilitate the rational design of filarial DHFR inhibitors, which in turn can be used to determine whether DHFR is a viable drug target for filariasis and whether existing antifolates may be repurposed for its treatment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Data Generation for Machine Learning Interatomic Potentials and Beyond

The field of data-driven chemistry is undergoing an evolution, driven by innovations in machine learning models for predicting molecular properties and behavior. Recent strides in ML-based interatomic potentials have paved the way for accurate modeling of diverse chemical and structural properties at the atomic level. The key determinant defining MLIP reliability remains the quality of the training data. A paramount challenge lies in constructing training sets that capture specific domains in the vast chemical and structural space. This Review navigates the intricate landscape of essential components and integrity of training data that ensure the extensibility and transferability of the resulting models. We delve into the details of active learning, discussing its various facets and implementations. We outline different types of uncertainty quantification applied to atomistic data acquisition and the correlations between estimated uncertainty and true error. The role of atomistic data samplers in generating diverse and informative structures is highlighted. Furthermore, we discuss data acquisition via modified and surrogate potential energy surfaces as an innovative approach to diversify training data. The Review also provides a list of publicly available data sets that cover essential domains of chemical space.

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

Machine Learning Potentials with the Iterative Boltzmann Inversion: Training to Experiment

Methodologies for training machine learning potentials (MLPs) with quantum-mechanical simulation data have recently seen tremendous progress. Experimental data have a very different character than simulated data, and most MLP training procedures cannot be easily adapted to incorporate both types of data into the training process. Here, we investigate a training procedure based on iterative Boltzmann inversion that produces a pair potential correction to an existing MLP using equilibrium radial distribution function data. By applying these corrections to an MLP for pure aluminum based on density functional theory, we observe that the resulting model largely addresses previous overstructuring in the melt phase. Interestingly, the corrected MLP also exhibits improved performance in predicting experimental diffusion constants, which are not included in the training procedure. The presented method does not require autodifferentiating through a molecular dynamics solver and does not make assumptions about the MLP architecture. Our results suggest a practical framework for incorporating experimental data into machine learning models to improve the accuracy of molecular dynamics simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Data for A Generalized Platform for Artificial Intelligence-powered Autonomous Protein Engineering

Proteins are the molecular machines of life with numerous applications in energy, health, and sustainability. However, engineering proteins with desired functions for practical applications remains slow, expensive, and specialist-dependent. Here we report a generally applicable platform for autonomous enzyme engineering that integrates machine learning and large language models with biofoundry automation to eliminate the need for human intervention, judgement, and domain expertise. Requiring only an input protein sequence and a quantifiable way to measure fitness, this automated platform can be applied to engineer a wide array of proteins. As a proof of concept, we engineer Arabidopsis thaliana halide methyltransferase (AtHMT) for a 90-foldimprovement in substrate preference and 16-fold improvement in ethyl-transferase activity, along with developing a Yersinia mollaretii phytase (YmPhytase) variant with 26-fold improvement in activity at neutral pH. This is accomplished in four rounds over 4 weeks, while requiring construction and characterization of fewer than 500 variants for each enzyme. This platform for autonomous experimentation paves the way for rapid advancements across diverse industries, from medicine and biotechnology to renewable energy and sustainable chemistry.

AI/ML↗
DOE OSTI2022

Investigation of the thermal conductivity of molten LiF-NaF-KF with experiments, theory, and equilibrium molecular dynamics

Molten salts are being proposed for numerous advanced energy applications, including advanced nuclear reactors, concentrating solar power plants, thermal energy storage, and fusion reactors. Accurate knowledge of the thermophysical properties of molten salts directly impact the performance of these energy systems and are essential for design and safety analyses. Thermal conductivity data for fluoride molten salts and mixtures are especially lacking. In this work, experimental measurements of thermal conductivity using the steady-state variable gap technique were performed on eutectic LiF-NaF-KF from 834 to 1195 K. The experiment accounts for radiative, convective, and conductive heat losses. In addition, theoretical and molecular dynamics models are used, from 750 K up to 1300 K, to estimate the thermal conductivity for comparison with the experimental results. The results of experiments show a weak negative deviation of thermal conductivity with temperature, unlike previous experimental results in the literature. The measured thermal conductivity magnitudes agree with the theoretical and molecular dynamics predictions, aside from the data above 1100 K, where heat losses and radiative errors are the most significant, having a 16% maximum deviation from theory. These experimental results provide new thermal conductivity data for the LiF-NaF-KF system and further validation of the predictive models. The theoretical model was used to map the composition and temperature dependent thermal conductivity of LiF-NaF-KF and the mapping’s deviation from a linear additivity estimation of thermal conductivity. Additionally, this mapping showed the highest deviations from linearity for KF-LiF rich mixtures and increasing deviation with temperature. Notably, the deviation from linearity near the LiF-NaF-KF eutectic composition was around 25%.

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

WHONDRS Summer 2019 Sampling Campaign: Global River Corridor Surface Water FTICR-MS, NPOC, TN, Anions, Stable Isotopes, Bacterial Abundance, and Dissolved Inorganic Carbon (v8)

The WHONDRS Summer 2019 Sampling (S19S) Campaign collected samples in 97 globally distributed river corridor systems between July and September 2019. Surficial streambed sediments were collected at three locations within each site (upstream, midstream, and downstream). Surface water was collected at the downstream site. This dataset includes a portion of the data types produced from the surface water samples and does not include any results from the sediment. Sediment data can be found at https://data.ess-dive.lbl.gov/view/doi:10.15485/1729719. The S19S campaign was designed with the science community to ask questions associated with links among core/transient metabolomes, microbial metabolism, biogeochemical function, and physical properties of watershed and river corridor systems. This data package was originally published in March 2020. It was updated in October 2020 (v2, new and modified files), July 2021 (v3, new and modified files), January 2023 (v4, new and modified files), February 2023 (v5, new and modified files), August 2023 (v6, new and modified files), July 2025 (v7,new and modified files), and August 2026 (v8, new and modified files). Version 8 includes major restructuring of the files and their formats. See the change history section in readme for more details. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset contains (1) readme, (2) data dictionary, (3) file level metadata, (4) sampling protocol, (5) a data checks report, (6) field metadata, (7) international generic sample number (IGSN) mapping file, (8) joint genome institute (JGI) mapping file, and (9) a folder of sample data. The sample data folder contains (1) dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) and FTICR methods, (2) dissolved inorganic carbon (DIC), (3) stable water isotopes (O and H), (4) specific conductivity, (5) total dissolved nitrogen (TN), (6) anions, (7) flow cytometry bacterial abundance data, (8) laboratory methods, and (9) a folder containing unaligned, peak-picked high resolution characterization of dissolved organic matter from sediment via 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) through the Environmental Molecular Sciences Laboratory (EMSL, https://www.pnnl.gov/environmental-molecular-sciences-laboratory), and (12) a folder containing processed FTICR-MS data. All files are .msa, .txt, .spx, .xml, .csv, .pdf, .txt, .html, .jpg, or .jpeg.

18-O↗
DOE OSTI2024

DOE BSSD Performance Management Metrics Report Q1

Microbes play key roles in our biosphere, from driving global nutrient cycling to impacting plant, animal and human health and disease. Complex data from microbial genomes, proteins, and metabolites provide a window into these tiny engines that drive life on our planet. Yet these data are dispersed among researchers’ laboratories and various repositories, making it difficult to access. This calls for new ways of managing data, improving data interoperability, advancing community standards, and creating an infrastructure where data are shared efficiently. We have built the National Microbiome Data Collaborative (NMDC) to advance how scientists create, use, and reuse data to redefine the way we understand and harness the power of microbes. The vision of the National Microbiome Data Collaborative (NMDC) is to drive a microbiome data sharing network connecting data, people, and ideas to advance microbiome innovation and discovery. The NMDC was launched in 2019 and brought together DOE National Laboratories to collaborate across resources, capabilities, and expertise. The NMDC team was strategically assembled to include software developers, microbial researchers, metadata experts, and multi-omics specialists. The diversity of the NMDC team reflects the inherently interdisciplinary nature of microbiome science, and we leverage the strengths of the DOE National Laboratory system. Towards BER’s goal of advancing an iterative systems biology approach to the understanding of microbial genomes, the NMDC serves as a foundation for infrastructure, data standards, and community building. Together with the flagship DOE User Facilities, the Joint Genome Institute (JGI) and the Environmental Molecular Sciences Laboratory (EMSL), we are developing core capabilities in metadata standards for environmental descriptors and sample handling and processing; standardized bioinformatic workflows; an interface for data search and access; and robust community engagement activities. The NMDC production platform supports long-term data infrastructure and community building for BER’s bioenergy and environmental research goals. Our approach leverages lessons learned and an ambitious framework for collaborative, interdisciplinary data infrastructure to support microbiome research. The NMDC supports data, information, and knowledge access through three defined software tools – the Submission Portal, NMDC EDGE, and the Data Portal – driven by community needs. Herein, we describe the value proposition for the microbiome research community, our overarching strategy, and challenges and opportunities for developing the NMDC as both an infrastructure and community engagement program.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Competing ionization and dissociation in the H 2 gerade system

A numerically solvable two-dimensional (2D) model, employed by the authors to study the dissociative recombination of H$^+_2$ in the ungerade symmetry, is extended to describe the collision process in the gerade symmetry of H$_2$. In this symmetry, the ionization and dissociation processes are driven primarily by the direct, curve-crossing mechanism. The model is represented by a set of three coupled electronic channels in 2D, in the space of $\textit{s, p, d}$ partial waves of the colliding electron. In this work, we demonstrate that the Born-Oppenheimer properties of the H$_2$ molecule in the relevant range of internuclear distances can be described by such a model. The molecular rotational degrees of freedom are accounted for by the rotational frame transformation. The numerical solution of the model is discussed, and the resulting rovibrationally inelastic and dissociative recombination cross sections are compared with the available data.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Adjusting the Energy Profile for CH–O Interactions Leads to Improved Stability of RNA Stem-Loop Structures in MD Simulations

The role of ribonucleic acid (RNA) in biology continues to grow, but insight into important aspects of RNA behavior is lacking, such as dynamic structural ensembles in different environments, how flexibility is coupled to function, and how function might be modulated by small molecule binding. In the case of proteins, much progress in these areas has been made by complementing experiments with atomistic simulations, but RNA simulation methods and force fields are less mature. It remains challenging to generate stable RNA simulations, even for small systems where well-defined, thermostable structures have been established by experiments. Further many different aspects of RNA energetics have been adjusted in force fields, seeking improvements that are transferable across a variety of RNA structural motifs. In this work, the role of weak CH···O interactions is explored, which are ubiquitous in RNA structure but have received less attention in RNA force field development. By comparing data extracted from high-resolution RNA crystal structures to energy profiles from quantum mechanics and force field calculations, it is shown that CH···O interactions are overly repulsive in the widely used Amber RNA force fields. A simple, targeted adjustment of CH···O repulsion that leaves the remainder of the force field unchanged was developed. Then, the standard and modified force fields were tested using molecular dynamics (MD) simulations with explicit water and salt, amassing over 300 μs of data for multiple RNA systems containing important features such as the presence of loops, base stacking interactions as well as canonical and noncanonical base pairing. In this work and others, standard force fields lead to reproducible unfolding of the NMR-based structures. Including a targeted CH···O adjustment in an otherwise identical protocol dramatically improves the outcome, leading to stable simulations for all RNA systems tested.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

The mechanical response of micron-sized molecular crystals

Abstract Microstructures and corresponding properties of compacted powders ultimately depend on the mechanical response of individual particles. In principle, computational simulations can predict the results of powder compaction processes, but the selection of appropriate models for both particle–particle interactions and particle deformations across all relevant length scales remain nontrivial tasks, especially in material systems lacking detailed mechanical property information. The work presented here addresses these issues by conducting uniaxial compressions in situ inside of a scanning electron microscope to characterize the mechanical response of individual micron-sized particles of a molecular crystal, hexanitrohexaazaisowurtzitane (CL-20). This experimental approach enabled the collection of quantitative force and displacement data alongside simultaneous imaging to capture morphology changes. The results reveal information about elastic deformation, yield, plastic deformation, creep, and fracture phenomena. Accordingly, this work demonstrates a generalizable approach for assessing the mechanical response of individual micron-sized molecular crystal particles and utilizing those responses in particle-level models. Graphic abstract

36 MATERIALS SCIENCE↗
DOE OSTI2025

Seed coat transcriptomic profiling of 5-593, a genotype important for genetic studies of seed coat color and patterning in common bean ( Phaseolus vulgaris L.)

Common bean (Phaseolus vulgaris L.) market classes have distinct seed coat colors, which are directly related to the diverse flavonoids found in the mature seed coat. To understand and elucidate the molecular mechanisms underlying the regulation of seed coat color, RNA-Seq data was collected from the black bean 5-593 and used for a differential gene expression and enrichment analysis from four different seed coat color development stages. 5-593 carries dominant alleles for 10 of the 11 major genes that control seed coat color and expression and has historically been used to develop introgression lines used for seed coat genetic analysis. Pairwise comparison among the four stages identified 6,294 differentially expressed genes (DEGs) varying from 508 to 5,780 DEGs depending on the compared stages. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that phenylpropanoid biosynthesis, flavonoid biosynthesis, and plant hormone signal transduction comprised the principal pathways expressed during bean seed coat pigment development. Transcriptome analysis suggested that most structural genes for flavonoid biosynthesis and some potential regulatory genes were significantly differentially expressed. Further studies detected 29 DEGs as important candidate genes governing the key enzymatic flavonoid biosynthetic pathways for common bean seed coat color development. Additionally, four gene models, Pv5-593.02G016100, 593.02G078700, Pv5-593.02G090900, and Pv5-593.06G121300, encode MYB-like transcription factor family protein were identified as strong candidate regulatory genes in anthocyanin biosynthesis which could regulate the expression levels of some important structural genes in flavonoid biosynthesis pathway. These findings provide a framework to draw new insights into the molecular networks underlying common bean seed coat pigment development.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Bayesian automated weighting of aggregated DFT, MD, and experimental data for candidate thermodynamic models of aluminum with uncertainty quantification

Atomic-scale modeling methods such as density functional theory (DFT) and molecular dynamics (MD) can predict the thermodynamic properties of materials at a lower cost than experimental measurements. However, their regular usage in thermodynamic model construction is hampered by the lack of quantitative agreement with experimental measurements and the lack of uncertainty estimates on the data. To make regular usage of this atomistic simulation data, it is important to assess whether the atomistic simulation datasets, by themselves or in combination with experimental measurements, result in the same physics-informed models best supported by experimental measurements alone. Here, models of aluminum thermodynamic properties are discussed using three data sources: atomistic calculations (DFT and MD), experiments, and a combination of atomistic calculations and experiments. The study shows that, after ensuring self-consistency in predicting key invariant points, both experimental measurements and atomistic calculations can significantly contribute to an optimal model.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Prediction of dislocation - grain boundary interactions in FCC aluminum bicrystals using a modified continuum criterion and machine learning methods

Mechanical properties of metals such as strength and toughness are strongly correlated to complex interactions between various defects in the crystalline structure. While elementary interactions between these defects have been investigated using recent micro- and nano-characterization techniques, understanding of the detailed interaction mechanisms has hardly been obtained. To understand defect-driven plasticity at various time and length scales, it is necessary to formulate a general guideline to predict both the interaction type (transmission or reflection) and the dislocation's subsequent slip system after the interaction. Many criteria based on the geometric alignment of the defects have been developed to predict this phenomenon, but these have yet to be found to be accurate when applied to general data sets of grain boundaries (GBs). With this motivation, we conduct a systematic study using molecular dynamics (MD) models of bicrystals to analyze defect interaction process between a prismatic dislocation loop and eleven different grain boundaries of the following character: three tilt, three twist, and five mixed. Based on the MD observations, two new prediction methods are developed: the first is a new data-driven parametric score function based on the classical geometric criteria, and the second is by applying Gaussian process machine learning methods to find the probability distribution of a hidden function. In conclusion, the proposed data-driven prediction methods could pave a new way to predict the unit interaction of dislocations with various GBs, which could show much higher accuracy compared to pre-existing geometric criteria.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A combined experimental and computational study on the reaction dynamics of the 1-propynyl (CH 3 CC, X 2 A 1 ) – propylene (CH 3 CHCH 2 , X 1 A') system: formation of 1,3-dimethylvinylacetylene (CH 3 CCCHCHCH 3 , X 1 A') under single collision conditions

The reaction of the 1-propynyl radical (CH 3 CC; X 2 A 1 ) with propylene (CH 3 CHCH 2 ; X 1 A') was studied in a crossed molecular beam machine at a collision energy of 37 ± 1 kJ mol –1 . Experimental data combined with high-level electronic structure (CCSD(T)-F12/cc-pVTZ-F12//ωB97X-D/6-311G(d,p)) and RRKM calculations reveal the reaction mechanism. Here, the overall barrierless and exoergic reaction involves indirect reaction dynamics and commences preferentially with addition of 1-propynyl with its radical centre to the carbon–carbon double bond at the terminal carbon atom of propylene. This work focuses on molecular mass growth process (hydrogen loss channels) although theory suggests methyl loss as a prevalent channel. In these processes, the C6H9 collision complexes either emit atomic hydrogen or undergo isomerisation followed by atomic hydrogen loss to preferentially yield the cis/trans isomers of 1,3-dimethylvinylacetylene (2-hexen-4-yne) as the primary product. Analysis of reaction dynamics of 1-propynyl and ethynyl radicals with propylene along with their fractional abundance in deep space suggests formation of methyl- and dimethyl derivatives of vinylacetylene in cold molecular clouds. Once formed they may engage in fundamental molecular mass growth processes via the barrierless Hydrogen Abstraction Vinylacetylene Addition mechanism that leads to the formation of methyl- and dimethylnaphthalenes thus providing a versatile route to methyl-substituted PAHs in interstellar medium.

1-propynyl↗
DOE OSTI2025

Mondo: integrating disease terminology across communities

Precision medicine aims to enhance diagnosis, treatment, and prognosis by integrating multimodal data at the point of care. However, challenges arise due to the vast number of diseases, differing methods of classification, and conflicting terminological coding systems and practices used to represent molecular definitions of disease. This lack of interoperability artificially constrains the potential for diagnosis, clinical decision support, care outcome analysis, as well as data linkage across research domains to support the development or repurposing of therapeutics. There is a clear and pressing need for a unified system for managing disease entities⁠—including identifiers, synonyms, and definitions. To address these issues, we created the Mondo disease ontology—a community-driven, open-source, unified disease classification system that harmonizes diverse terminologies into a consistent, computable framework. Mondo integrates key medical and biomedical terminologies, including Online Mendelian Inheritance in Man (OMIM), Orphanet, Medical Subject Headings (MeSH), National Cancer Institute Thesaurus (NCIt), and more, to provide a comprehensive and accurate representation of disease concepts with fully provenanced and attributed links back to the sources. Mondo can be used as the handle for curation of gene–disease associations utilized in diagnostic applications, research applications such as computational phenotyping, and in clinical coding systems in clinical decision support by pointing the clinician to the numerous knowledge resources linked to the Mondo identifier. Mondo's community-centric approach, stewarded by the Monarch Initiative's expertise in ontologies, ensures that the ontology remains adaptable to the evolving needs of biomedical research and clinical communities, as well as the knowledge providers.

biomedical informatics↗
DOE OSTI2022

A molecular dynamics study of laser-excited gold

The structural evolution of laser-excited systems of gold has previously been measured through ultrafast MeV electron diffraction. However, there has been a long-standing inability of atomistic simulations to provide a consistent picture of the melting process, leading to large discrepancies between the predicted threshold energy density for complete melting, as well as the transition between heterogeneous and homogeneous melting. We make use of two-temperature classical molecular dynamics simulations utilizing three highly successful interatomic potentials and reproduce electron diffraction data presented by Mo et al. [Science 360, 1451–1455 (2018)]. We recreate the experimental electron diffraction data, employing both a constant and temperature-dependent electron–ion equilibration rate. In all cases, we are able to match time-resolved electron diffraction data, and find consistency between atomistic simulations and experiments, only by allowing laser energy to be transported away from the interaction region. This additional energy-loss pathway, which scales strongly with laser fluence, we attribute to hot electrons leaving the target on a timescale commensurate with melting.

36 MATERIALS SCIENCE↗