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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Split-Step Simulations to Assess the Effects of Atmospheric Boundary Layer Turbulence on the Dose Variability of N-Waves and Shaped Booms

The effects of atmospheric boundary layer turbulence on the loudness variability of a sonic boom N-wave and shaped boom are examined with split-step simulations. The shaped boom is representative of a design iteration of the NASA X-59 aircraft. Inhomogeneous atmospheric boundary layer turbulence is generated in the computational domain by a Fourier synthesis method. The N-wave and shaped boom are propagated through turbulent fields representing eight different convection levels measured at the NASA Kennedy Space Center and the NASA Armstrong Flight Research Center. Probability density functions of the formation of caustic regions along the propagation direction are computed from the N-wave results, and a parameter to collapse the caustic PDFs that accounts for both fluctuation intensities and length scales is proposed. Statistical results concerning loudness metric variability are presented, and the standard deviations of several metrics are shown to collapse across different convection levels of turbulence for small nondimensional propagation distances. The loudness metric distributions are observed to be well approximated by a normal distribution for a given range of propagation distances, and become increasingly skewed as distance increases. A model function for the dose variability is proposed, and the function parameters are found to be related to the convection level of the turbulence. The model for the dose variability distribution is compared to simulation data that were not used to find the regression parameters of the model. At several nondimensional propagation distances, agreement is observed between the model and the simulation data. These results indicate that the model may be suitable for providing quick estimates of noise dose variability in the primary carpet region across a wide range of atmospheric boundary layer conditions.

Sonic boom↗
DOE OSTI2018

Diacylglycerol kinase γ predicts prognosis and functions as a tumor suppressor by negatively regulating glucose transporter 1 in hepatocellular carcinoma

Highlights: • DGKγ expression was downregulated in hepatocellular carcinoma (HCC). • Lower DGKγ expression in HCC tumor tissues was an independent prognostic factor for poor post-surgical overall survival. • Cell proliferation and migration of HCC cells were suppressed by DGKγ ectopic expression. • DGKγ overexpression led to down regulation of GLUT1 protein level and AMPK activity. Diacylglycerol kinases (DGK) are a family of enzymes catalyzing the transformation of diacylglycerol into phosphatidic acid, which have been recognized as key regulators in cell signaling pathways. The role of DGKγ in human malignancies has seldom been studied. In this study, we investigated the role of DGKγ in hepatocellular carcinoma (HCC). We found that DGKγ was down-regulated in HCC tumor tissues and cell lines as compared to that in non-tumor tissues. The prognostic value of DGKγ expression was evaluated by Cox regression and Kaplan-Meier analyses. Lower DGKγ expression in tumor tissues was an independent prognostic factor for poor post-surgical overall survival. By using HDACs inhibitors treatment and ChIP-PCR, we discovered that histone H3 and H4 deacetylation mainly contributed to the downregulation of DGKγ expression. Functional studies revealed that ectopic expression of DGKγ inhibited cell proliferation and cell migration in HCC cells. Mechanism studies showed that DGKγ overexpression led to down regulation of GLUT1 protein level and AMPK activity, which result in glucose uptake suppression as well as lactate and ATP production declination. The decrease of GLUT1 level could be partially rescued by treatments with either DGK inhibitor and lysosome inhibitor, indicating DGKγ may down-regulate GLUT1 through its kinase activity and lysosome degradation process. Together, this study demonstrated that DGKγ plays a tumor suppressor role in HCC by negatively regulating GLUT1. DGKγ could be a novel prognostic indicator and therapeutic target for HCC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Dynamic mapping of conical intersection seams: A general method for incorporating the geometric phase in adiabatic dynamics in polyatomic systems

The incorporation of the geometric phase in single-state adiabatic dynamics near a conical intersection (CI) seam has so far been restricted to molecular systems with high symmetry or simple model Hamiltonians. This is due to the fact that the ab initio determined derivative coupling (DC) in a multi-dimensional space is not curl-free, thus making its line integral path dependent. In a recent work [C. L. Malbon et al. , J. Chem. Phys. 145, 234111 (2016)], we proposed a new and general approach based on an ab initio determined diabatic representation consisting of only two electronic states, in which the DC is completely removable, so that its line integral is path independent in the simply connected domains that exclude the CI seam. Then with the CIs included, the line integral of the single-valued DC can be used to construct the complex geometry-dependent phase needed to exactly eliminate the double-valued character of the real-valued adiabatic electronic wavefunction. This geometry-dependent phase gives rise to a vector potential which, when included in the adiabatic representation, rigorously accounts for the geometric phase in a system with an arbitrary locus of the CI seam and an arbitrary number of internal coordinates. Here, we demonstrate this approach in a three-dimensional treatment of the tunneling facilitated dissociation of the S 1 state of phenol, which is affected by a C s symmetry allowed but otherwise accidental seam of CI. Here, since the space is three-dimensional rather than two-dimensional, the seam is a curve rather than a point. The nodal structure of the ground state vibronic wavefunction is shown to map out the seam of CI.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1975

Determination of discontinuities in the derivative of a piecewise-smooth function on the basis of the results of measurements

A piecewise-smooth function with discontinuity in the first derivative on a given interval is considered. The values of the function are measured at a sequence of points in the interval and a random error is included in the measurements. A method is proposed to estimate the position of the discontinuity in the derivative. Regression lines are associated with each measurement point and account for k - 1 points preceding or following the point. The estimate for the position of the discontinuity is the measurement point with the largest angle between the regression lines. The error in the estimate is analyzed and the results are verified.

Gurin, L. S.↗
NASA NTRS1992

Active and passive microwave measurements of soil moisture in FIFE

During the intensive field campaigns of the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) in May-October of 1987, several nearly simultaneous measurements were made with low-altitude flights of the L-band radiometer and C- and X-band scatterometers over two transects in the Konza Prairie Natural Research Area, some 8 km south of Manhattan, Kansas. These measurements showed that although the scatterometers were sensitive to soil moisture variations in most regions under the flight path, the L-band radiometer lost most of its sensitivity in regions unburned for many years. The correlation coefficient derived from the regression between the radar backscattering coefficient and the soil moisture was found to improve with the increase in antenna incidence angle. This is attributed to a steeper falloff of the backscattering coefficient as a function of local incidence at angles near nadir than at angles greater than 30 deg.

Wang, J. R.↗
DOE OSTI2024

Application of machine learning to discover new intermetallic catalysts for the hydrogen evolution and the oxygen reduction reactions

The adsorption energies for hydrogen, oxygen, and hydroxyl were calculated by means of density functional theory on the lowest energy surface of 24 pure metals and 332 binary intermetallic compounds with stoichiometries AB, A 2 B, and A 3 B taking into account the effect of biaxial elastic strains. This information was used to train two random forest regression models, one for the hydrogen adsorption and another for the oxygen and hydroxyl adsorption, based on 9 descriptors that characterized the geometrical and chemical features of the adsorption site as well as the applied strain. All the descriptors for each compound in the models could be obtained from physico-chemical databases. The random forest models were used to predict the adsorption energy for hydrogen, oxygen, and hydroxyl of ≈2700 binary intermetallic compounds with stoichiometries AB, A 2 B, and A 3 B made of metallic elements, excluding those that were environmentally hazardous, radioactive, or toxic. This information was used to search for potential good catalysts for the HER and ORR from the criteria that their adsorption energy for H and O/OH, respectively, should be close to that of Pt. Further, this investigation shows that the suitably trained machine learning models can predict adsorption energies with an accuracy not far away from density functional theory calculations with minimum computational cost from descriptors that are readily available in physico-chemical databases for any compound. Moreover, the strategy presented in this paper can be easily extended to other compounds and catalytic reactions, and is expected to foster the use of ML methods in catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2002

A statistical model for interpreting computerized dynamic posturography data

Computerized dynamic posturography (CDP) is widely used for assessment of altered balance control. CDP trials are quantified using the equilibrium score (ES), which ranges from zero to 100, as a decreasing function of peak sway angle. The problem of how best to model and analyze ESs from a controlled study is considered. The ES often exhibits a skewed distribution in repeated trials, which can lead to incorrect inference when applying standard regression or analysis of variance models. Furthermore, CDP trials are terminated when a patient loses balance. In these situations, the ES is not observable, but is assigned the lowest possible score--zero. As a result, the response variable has a mixed discrete-continuous distribution, further compromising inference obtained by standard statistical methods. Here, we develop alternative methodology for analyzing ESs under a stochastic model extending the ES to a continuous latent random variable that always exists, but is unobserved in the event of a fall. Loss of balance occurs conditionally, with probability depending on the realized latent ES. After fitting the model by a form of quasi-maximum-likelihood, one may perform statistical inference to assess the effects of explanatory variables. An example is provided, using data from the NIH/NIA Baltimore Longitudinal Study on Aging.

NASA Discipline Neuroscience↗
DOE OSTI2020

Phonon-mediated lipid raft formation in biological membranes

Short-wavelength collective molecular motions, also known as phonons, have recently attracted much interest in revealing dynamic properties of biological membranes through the use of neutron and x-ray scattering, infrared and Raman spectroscopies, and molecular dynamics simulations. Experimentally detecting unique vibrational patterns such as, shear phonon excitations, viscoelastic crossovers, transverse acoustic phonon gaps, and continuous and truncated optical phonon modes in cellular membranes, to name a few, has proven non-trivial. Here, we review recent advances in liquid thermodynamics that have resulted in the development of the phonon theory of 1 liquids. The theory has important predictions regarding the shear vibrational spectra of fluids, namely the emergence of viscoelastic crossovers and transverse acoustic phonon gaps. Furthermore, we show that these vibrational patterns are common in soft (non-crystalline) materials, including, but not limited to liquids, colloids, liquid crystals (mesogens), block copolymers, and biological membranes. The existence of viscoelastic crossovers and acoustic phonon gaps define the self-diffusion properties of cellular membranes and provide a molecular picture of the transient nature of lipid rafts.1 Importantly, the timescales (picoseconds) for the formation and dissolution of transient lipid rafts match the lifetime of the formation and breakdown of interfacial water hydrogen bonds. Apart from acoustic propagating phonon modes, biological membranes can also support more energetic non-propagating optical phonon excitations, also known as standing waves or breathing modes. Importantly, optical phonons can be truncated due to the existence of finite size nanodomains made up of strongly correlated lipid-cholesterol molecular pairs. These strongly coupled molecular pairs can serve as nucleation centers for the formation of stable rafts at larger length scales, due to correlations of spontaneous fluctuations (Onsager’s regression hypothesis). Finally and importantly, molecular level viscoelastic crossovers, acoustic phonon gaps, and continuous and truncated optical phonon modes may offer insights as to how lipid-lipid and lipid-protein interactions enable biological function.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Modeling pre-Exascale AMR Parallel I/O Workloads via Proxy Applications

The present work investigates the modeling of preexascale input/output (I/O) workloads of Adaptive Mesh Refinement (AMR) simulations through a simple proxy application. We collect data from the AMReX Castro framework running on the Summit supercomputer for a wide range of scales and mesh partitions for the hydrodynamic Sedov case as a baseline to provide sufficient coverage to the formulated proxy model. The non-linear analysis data production rates are quantified as a function of a set of input parameters such as output frequency, grid size, number of levels, and the Courant-Friedrichs-Lewy (CFL) condition number for each rank, mesh level and simulation time step. Linear regression is then applied to formulate a simple analytical model which allows to translate AMReX inputs into MACSio proxy I/O application parameters, resulting in a simple “kernel” approximation for data production at each time step. Results show that MACSio can simulate actual AMReX nonlinear “static” I/O workloads to a certain degree of confidence on the Summit supercomputer using the present methodology. The goal is to provide an initial level of understanding of AMR I/O workloads via lightweight proxy applications models to facilitate autotune data management strategies in anticipation of exascale systems.

Godoy, William↗
DOE OSTI2021

Code and Solution Verification Assessment of the CTF Thermal Hydraulic Subchannel Code

CTF is a thermal-hydraulics subchannel code jointly developed by Oak Ridge National Laboratory and North Carolina State University. Over the past seven years, the Consortium for Advanced Simulation of Light Water Reactors (CASL) has made a significant investment in developing CTF so it can be used to model light water reactors, including nominal operating conditions, departure from nucleate boiling analysis, and transients ranging from loss of flow to reactivity insertion accidents. In addition to implementing new modeling capabilities and developing the user input and output interface, extensive work has been performed to improve the code’s quality assurance program, resulting in a development process that conforms with NQA-1 requirements. The CASL program follows the Predictive Capability Maturity Model (PCMM) approach for assessing code quality, which emphasizes performing code verification(ensuring the code converges to the correct answer) and solution verification (ensuring the code converges for the intended application). Code and solution verification are used to identify uncertainty errors introduced by numerical approximations in the code and are important for demonstrating that the model is coded without error, which is an important aspect of the Best Estimate plus Uncertainty method. This paper presents a comprehensive overview of the code and solution verification testing that has been performed on CTF. A top-down approach is taken in which the intended CTF applications are presented, followed by the code features required for their modeling. These features are then linked to the applicable code and solution verification tests that demonstrate proper functioning. Past testing efforts are summarized, and new tests are added to help close gaps in the presented test matrix. Rather than performing “one-off” exercises, these tests are added to the automated CTF regression test suite to ensure continual code quality.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS1989

Use of anomolous thermal imaging effects for multi-mode systems control during crystal growth

Real time image processing techniques, combined with multitasking computational capabilities are used to establish thermal imaging as a multimode sensor for systems control during crystal growth. Whereas certain regions of the high temperature scene are presently unusable for quantitative determination of temperature, the anomalous information thus obtained is found to serve as a potentially low noise source of other important systems control output. Using this approach, the light emission/reflection characteristics of the crystal, meniscus and melt system are used to infer the crystal diameter and a linear regression algorithm is employed to determine the local diameter trend. This data is utilized as input for closed loop control of crystal shape. No performance penalty in thermal imaging speed is paid for this added functionality. Approach to secondary (diameter) sensor design and systems control structure is discussed. Preliminary experimental results are presented.

Wargo, Michael J.↗
NASA NTRS2001

Behavior of Three Metallic Alloys Under Combined Axial-Shear Stress at 650 C

Three materials, Inconel 718, Haynes 188, and 316 stainless steel, were tested under an axial-torsional stress state at 650 C. The objective of this study was to quantify the evolution of the material while in the viscoplastic domain. Initial and subsequent yield surfaces were experimentally determined to quantify hardening. Subsequent yield surfaces (yield surfaces taken after a preload) had a well-defined front side, in the prestrain direction, but a poorly defined back side, opposite the prestrain direction. Subsequent yield surfaces exhibited isotropic hardening by expansion of the yield surface, kinematic hardening by translation of the yield surface, and distortional hardening by flattening of the yield surface in the direction opposite to the last prestrain. An existing yield function capable of representing isotropic, kinematic, and distortional hardening was used to fit each yield surface. Four variables are used to describe each surface. These variables evolve as the material state changes and have been regressed to the yield surface data.

Colaiuta, Jason F.↗
NASA NTRS2021

The Far Ultraviolet M-dwarf Evolution Survey. I. The Rotational Evolution of High-energy Emissions

M-dwarf stars are prime targets for exoplanet searches because of their close proximity and favorable properties for both planet detection and characterization. However, the potential habitability and atmospheric characterization of these exoplanetary systems depends critically on the history of high-energy stellar radiation from X-rays to NUV, which drive atmospheric mass loss and photochemistry in the planetary atmospheres. With the Far Ultraviolet M-dwarf Evolution Survey, we have assessed the evolution of the FUV radiation, specifically eight prominent emission lines, including Lyα, of M-dwarf stars with stellar rotation period and age. We demonstrate tight power-law correlations between the spectroscopic FUV features, and measure the intrinsic scatter of the quiescent FUV emissions. The luminosity evolution with rotation of these spectroscopic features is well-described by a broken power law, saturated for fast rotators, and decays with increasing Rossby number, with a typical power-law slope of −2, although likely shallower for Lyα. Our regression fits enable FUV emission-line luminosity estimates relative to bolometric from known rotation periods to within ∼0.3 dex, across eight distinct UV emission lines, with possible trends in the fit parameters as a function of source layer in the stellar atmosphere. Our detailed analysis of the UV luminosity evolution with age further shows that habitable-zone planets orbiting lower-mass stars experience much greater high-energy radiative exposure relative the same planets orbiting more massive hosts. Around early- to mid-M dwarfs, these exoplanets, at field ages, accumulate up to 10–20× more EUV energy, relative to modern Earth. Moreover, the bulk of this UV exposure likely takes place within the first Gyr of the stellar lifetime.

J Sebastian Pineda↗
NASA NTRS2002

Neural Network and Regression Soft Model Extended for PAX-300 Aircraft Engine

In fiscal year 2001, the neural network and regression capabilities of NASA Glenn Research Center's COMETBOARDS design optimization testbed were extended to generate approximate models for the PAX-300 aircraft engine. The analytical model of the engine is defined through nine variables: the fan efficiency factor, the low pressure of the compressor, the high pressure of the compressor, the high pressure of the turbine, the low pressure of the turbine, the operating pressure, and three critical temperatures (T(sub 4), T(sub vane), and T(sub metal)). Numerical Propulsion System Simulation (NPSS) calculations of the specific fuel consumption (TSFC), as a function of the variables can become time consuming, and numerical instabilities can occur during these design calculations. "Soft" models can alleviate both deficiencies. These approximate models are generated from a set of high-fidelity input-output pairs obtained from the NPSS code and a design of the experiment strategy. A neural network and a regression model with 45 weight factors were trained for the input/output pairs. Then, the trained models were validated through a comparison with the original NPSS code. Comparisons of TSFC versus the operating pressure and of TSFC versus the three temperatures (T(sub 4), T(sub vane), and T(sub metal)) are depicted in the figures. The overall performance was satisfactory for both the regression and the neural network model. The regression model required fewer calculations than the neural network model, and it produced marginally superior results. Training the approximate methods is time consuming. Once trained, the approximate methods generated the solution with only a trivial computational effort, reducing the solution time from hours to less than a minute.

Patnaik, Surya N.↗
NASA NTRS2026

Machine Learning for Predicting Team Functioning in HERA Missions

Team functioning is integral to success in future long term space exploration missions. Proactively detecting declines in team functioning can mitigate conflict and ensure mission success. This project developed a speech-based artificial intelligence (AI) system that unobtrusively predicts degradation in team functioning, including performance and cohesion, in the Human Exploration Research Analog (HERA) Campaigns 4 and 5. The AI system conducted automated analysis of the prosodic (tone of voice) and linguistic (language content) components of speech, modeling interpersonal dynamics at both the turn-taking and day-wide levels. We investigated team functioning via observing structured interactions (i.e., multi-mission space exploration vehicle-extra vehicular activity [MMSEV-EVA], team interaction battery [TIB]) and unstructured interactions before the MMSEV-EVA task. We developed machine learning models to predict team functioning (objective task accuracy, self reported team efficacy and self reported team cohesion) by analyzing OpenSmile acoustic features, linguistic descriptors extracted via the linguistic inquiry and word count (LIWC) dictionary, and semantic embeddings. In the TIB, static models using logistic regression and random forests were not able to predict task accuracy, but predicted team efficacy and cohesion during both the decision making and relational tasks to a moderate level (60-70%). Majority voting on the individual turns to predict day long team efficacy further increased accuracies (70-80%). Finally, long short-term memory (LSTM) models showed the best performance across all variables (80-91%), including task performance. In the MMSEV-EVA, static models achieved an accuracy of 60% with majority voting, which increased to 80% through the incorporation of mission day as a variable, accounting for the learning effect. A key finding across both tasks was the "team-dependent" nature of these interactions; models achieved much higher accuracy when trained on prior days of the same team's data rather than attempting to generalize across entirely different teams, with even 1-2 days of prior data per team achieving 5-15% improvement over team-independent models. In addition, the incorporation of pre-task data from the same team also improves model performance, e.g., incorporating data from the decision-making task of the TIB, which preceded the relational task, improved the prediction of team efficacy and cohesion during the latter. We compared model performance when trained on machine-generated data compared to data that had been further corrected by human annotators. Overall, models trained on human-corrected data exhibited a modest improvement in performance, particularly when acoustic features were used. We found no significant correlation between word error rate (WER) and model accuracy (r(55) = -0.08, p = 0.51), but model’s accuracy was significantly higher for medium/high quality transcription (0.74 (SD = 0.48)) compared to the low-quality group (0.64 (SD = 0.36)) (t(63)=2.82, p = 0.006). Based on these, several design recommendation emerge, that could inform Standards at NASA. Models predicting team functioning should incorporate at least one to two days of historical interaction data, include brief pre-task discussions, and explicitly model temporal learning effects, especially for longer operational tasks. Minimum quality standards for automated speech-processing pipelines are needed, given the performance gains observed with manually corrected acoustic data. Finally, systems should leverage both acoustic features and language embeddings in complementary ways, with modality choices and fusion strategies tailored to mission context, task demands, and data quality requirements.

Shrivatsa Mishra↗
DOE OSTI2025

The impact of kidney function on Alzheimer’s disease blood biomarkers: implications for predicting amyloid-β positivity

Impaired kidney function has a potential confounding effect on blood biomarker levels, including biomarkers for Alzheimer’s disease (AD). Given the imminent use of certain blood biomarkers in the routine diagnostic work-up of patients with suspected AD, knowledge on the potential impact of comorbidities on the utility of blood biomarkers is important. We aimed to evaluate the association between kidney function, assessed through estimated glomerular filtration rate (eGFR) calculated from plasma creatinine and AD blood biomarkers, as well as their influence over predicting Aβ-positivity. We included 242 participants from the Translational Biomarkers in Aging and Dementia (TRIAD) cohort, comprising cognitively unimpaired individuals (CU; n = 124), mild cognitive impairment (MCI; n = 58), AD dementia (n = 34), and non-AD dementia (n = 26) patients all characterized by [ 18 F] AZD-4694. Plasma samples were analyzed for Aβ42, Aβ40, glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), tau phosphorylated at threonine 181 (p-tau181), 217 (p-tau217), 231 (p-tau231) and N-terminal containing tau fragments (NTA-tau) using Simoa technology. Kidney function was assessed by eGFR in mL/min/1.73 m 2 , based on plasma creatinine levels, age, and sex. Participants were also stratified according to their eGFR-indexed stages of chronic kidney disease (CKD). We evaluated the association between eGFR and blood biomarker levels with linear models and assessed whether eGFR provided added predictive value to determine Aβ-positivity with logistic regression models. Biomarker concentrations were highest in individuals with CKD stage 3, followed by stages 2 and 1, but differences were only significant for NfL, Aβ42, and Aβ40 (not Aβ42/Aβ40). All investigated biomarkers showed significant associations with eGFR except plasma NTA-tau, with stronger relationships observed for Aβ40 and NfL. However, after adjusting for either age, sex or Aβ-PET SUVr, the association with eGFR was no longer significant for all biomarkers except Aβ40, Aβ42, NfL, and GFAP. When evaluating whether accounting for kidney function could lead to improved prediction of Aβ-positivity, we observed no improvements in model fit (Akaike Information Criterion, AIC) or in discriminative performance (AUC) by adding eGFR to a base model including each plasma biomarker, age, and sex. While covariates like age and sex improved model fit, eGFR contributed minimally, and there were no significant differences in clinical discrimination based on AUC values. We found that kidney function seems to be associated with AD blood biomarker concentrations. However, these associations did not remain significant after adjusting for age and sex, except for Aβ40, Aβ42, NfL, and GFAP. While covariates such as age and sex improved prediction of Aβ-positivity, including eGFR in the models did not lead to improved prediction for any biomarker. Our findings indicate that renal function, within the normal to mild impairment range, does not seem to have a clinically relevant impact when using highly accurate blood biomarkers, such as p-tau217, in a biomarker-supported diagnosis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Machine-learning and first-principles investigation of lightweight medium-entropy alloys for hydrogen-storage applications

The transition to a low-carbon economy demands efficient and sustainable energy-storage solutions, with hydrogen emerging as a promising clean-energy carrier and with metal hydrides recognized for their hydrogen-storage capacity. Here, we leverage machine learning (ML) to predict hydrogen-to-metal (H/M) ratios and solution energy by incorporating thermodynamic parameters and local lattice distortion (LLD) as key features. Our best-performing ML model provides improvements to H/M ratios and solution energies over a broad class of medium-entripy alloys (easily extendable to multi-principal-element alloys), such as Ti–Nb-X (X = Mo, Cr, Hf, Ta, V, Zr) and Co–Ni-X (X = Al, Mg, V). Ti–Nb–Mo alloys reveal compositional effects in H-storage behavior, in particular Ti, Nb, and V enhance H-storage capacity, while Mo reduces H/M and hydrogen weight percent by 40–50 %. We attributed results in molybdenum-rich alloys to slow hydrogen kinetics, as validated by our pressure-composition-temperature (PCT) isotherm experiments on pure Ti and Ti 5 Mo 95 alloys. Density functional theory (DFT) and molecular dynamics (MD) simulations also confirm that Ti and Nb promote H diffusion, whereas Mo hinders it, highlighting the interplay between electronic structure, lattice distortions, and hydrogen uptake. Notably, our Gradient Boosting Regression model identifies LLD as a critical factor in H/M predictions. Here, to aid material selection, we present two periodic tables illustrating elemental effects on (a) H 2 wt% and (b) solution energy, derived from ML, and provide a reference for identifying alloying elements that enhance hydrogen solubility and storage.

08 HYDROGEN↗
DOE OSTI2022

Autonomous scanning probe microscopy investigations over WS2 and Au{111}

Abstract Individual atomic defects in 2D materials impact their macroscopic functionality. Correlating the interplay is challenging, however, intelligent hyperspectral scanning tunneling spectroscopy (STS) mapping provides a feasible solution to this technically difficult and time consuming problem. Here, dense spectroscopic volume is collected autonomously via Gaussian process regression, where convolutional neural networks are used in tandem for spectral identification. Acquired data enable defect segmentation, and a workflow is provided for machine-driven decision making during experimentation with capability for user customization. We provide a means towards autonomous experimentation for the benefit of both enhanced reproducibility and user-accessibility. Hyperspectral investigations on WS 2 sulfur vacancy sites are explored, which is combined with local density of states confirmation on the Au{111} herringbone reconstruction. Chalcogen vacancies, pristine WS 2 , Au face-centered cubic, and Au hexagonal close-packed regions are examined and detected by machine learning methods to demonstrate the potential of artificial intelligence for hyperspectral STS mapping.

36 MATERIALS SCIENCE↗