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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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Enabling machine learning-ready HPC ensembles with Merlin
With the growing complexity of computational and experimental facilities, many scientific researchers are turning to machine learning (ML) techniques to analyze large scale ensemble data. With complexities such as multi-component workflows, heterogeneous machine architectures, parallel file systems, and batch scheduling, care must be taken to facilitate this analysis in a high performance computing (HPC) environment. Here, we present Merlin, a workflow framework to enable large ML-friendly ensembles of scientific HPC simulations. By augmenting traditional HPC with distributed compute technologies, Merlin aims to lower the barrier for scientific subject matter experts to incorporate ML into their analysis. As a producer–consumer workflow model, Merlin enables multi-machine, cross-batch job, dynamically allocated yet persistent workflows capable of utilizing surge-compute resources. Key features of Merlin are a flexible HPC-centric interface, low per-task overhead, multi-tiered fault recovery, and a hierarchical sampling algorithm that allows for $\mathscr{O}$(N) task execution and $\mathscr{O}$(N ln N) task queuing to ensembles of millions of tasks. In addition to Merlin’s design, we test the algorithm’s performance in an HPC center and demonstrate the ability to enqueue 40 million simulations in 100 s, with a 30 millisecond per-task overhead that is independent of ensemble size. Finally, we describe some example applications that Merlin has enabled on leadership-class HPC resources, such as the ML-augmented optimization of nuclear fusion experiments and the calibration of infectious disease models to study the progression of and possible mitigation strategies for COVID-19.
Self-supervised and multi-fidelity learning for extended predictive soil spectroscopy
Infrared spectroscopy is a cost-effective, non-destructive, and environmentally benign technology that is increasingly recognized as an important solution for meeting the global demand for soil data. While both near-infrared (NIR) and mid-infrared (MIR) diffuse reflectance spectroscopy enable rapid estimation of soil properties, they present a significant trade-off: NIR offers superior scalability and lower operational costs, whereas MIR provides higher analytical fidelity by capturing fundamental molecular vibrations. In this study, we propose a self-supervised, multi-fidelity learning framework designed to bridge this gap. Our approach leverages large-scale MIR spectral libraries to learn a compact, transferable latent representation, into which NIR spectra are subsequently aligned for downstream prediction. The workflow consists of pretraining a latent model on a large MIR library, adapting the representation using a smaller paired NIR–MIR dataset, and evaluating generalization on an independent external test set. Across a range of chemical and physical soil properties, we found that MIR-derived embeddings improved prediction accuracy relative to baseline models that used raw MIR inputs. Predictions derived from the spectrum conversion (NIR to MIR) task did not match the performance of the original MIR spectra but were similar or superior to predictive performance of NIR-only models, suggesting the unified spectral latent space can effectively leverage the larger and more diverse MIR dataset for prediction of soil properties not well represented in current NIR libraries.
Benchmarking CO₂ storage simulations: Results from the 11 th Society of Petroleum Engineers Comparative Solution Project
The 11 th Society of Petroleum Engineers Comparative Solution Project (shortened SPE11 herein) benchmarked simulation tools for geological carbon dioxide (CO 2 ) storage. A total of 45 groups from leading research institutions and industry across the globe signed up to participate, with 18 ultimately contributing valid results that were included in the comparative study reported here. This paper summarizes the SPE11 results. A comprehensive introduction and qualitative discussion of the submitted data are provided, together with an overview of online resources for accessing the full depth of data. A global metric for analyzing the relative distance between submissions is proposed and used to conduct a quantitative analysis of the submissions. This analysis attempts to statistically resolve the key aspects influencing the variability between submissions. The study shows that the major qualitative variation between the submitted results is related to thermal effects, dissolution-driven convective mixing, and resolution of facies discontinuities. Moreover, a strong dependence on grid resolution is observed across all three versions of the SPE11. However, our quantitative analysis suggests that the observed variations are predominantly influenced by factors not documented in the technical responses provided by the participants. We therefore identify that unreported variations due to human choices within the process of setting up, conducting, and reporting on the simulations underlying each SPE11 submission are at least as impactful as the computational choices reported.
KRAS4a and KRAS4b show distinct lipid-dependent regulation of RAS-RAF membrane dynamics
KRAS4a and KRAS4b are important regulators of signaling, and their interactions with the plasma membrane are dynamic and influenced by lipid composition. KRAS 4a and 4b have nearly identical globular domains but differ in their membrane-associated hyper variable region (HVR). The functional distinctions between these isoforms remain unclear, particularly with regards to their dependence on specific lipids and the membrane environment. Previous work showed that the membrane orientation of KRAS4b affects its ability to bind to RAF kinase RBDCRD and that the KRAS–RBDCRD complex adopts different poses on the membrane as well as influences the size and composition of the lipid environment. To model differences between KRAS 4a and 4b protein–lipid interactions, we extended the Multiscale Machine-Learned Modeling Infrastructure (MuMMI) to incorporate continuum simulations in the grand canonical ensemble, enabling sampling across macroscopic, coarse-grained, and all-atom resolutions. Using this framework, we systematically altered PIP2 concentrations, KRAS 4a versus 4b, and RAF RBDCRD complexation to assess impacts on membrane–protein interactions and dynamics. Our results reveal that reducing PIP2 shifts and broadens the membrane orientational preference of both KRAS 4b and 4a, with stronger effects on 4b HVR localization versus 4a. We demonstrate that with depletion of the strong negatively charged PIP2 lipid, the less charged phosphatidylserine replaces PIP2. Our findings highlight similarities and distinctions in the dynamics and lipid dependency of KRAS isoforms and suggest that ordering of the local lipid composition by HVRs is a shared property and key modulator of RAS-mediated signaling at the plasma membrane.
High-energy and ultra-high-energy neutrinos: A Snowmass white paper
Astrophysical neutrinos are excellent probes of astroparticle physics and high-energy physics. With energies far beyond solar, supernovae, atmospheric, and accelerator neutrinos, high-energy and ultra-high-energy neutrinos probe fundamental physics from the TeV scale to the EeV scale and beyond. They are sensitive to physics both within and beyond the Standard Model through their production mechanisms and in their propagation over cosmological distances. They carry unique information about their extreme non-thermal sources by giving insight into regions that are opaque to electromagnetic radiation. Here in this white paper describes the opportunities astrophysical neutrino observations offer for astrophysics and high-energy physics, today and in coming years.
Ab initio study of nuclear clustering in hot dilute nuclear matter
Not Available
Next generation Arctic vegetation maps: Aboveground plant biomass and woody dominance mapped at 30 m resolution across the tundra biome
The Arctic is warming faster than anywhere else on Earth, placing tundra ecosystems at the forefront of global climate change. Plant biomass is a fundamental ecosystem attribute that is sensitive to changes in climate, closely tied to ecological function, and crucial for constraining ecosystem carbon dynamics. However, the amount, functional composition, and distribution of plant biomass are only coarsely quantified across the Arctic. Therefore, we developed the first moderate resolution (30 m) maps of live aboveground plant biomass (g m −2 ) and woody plant dominance (%) for the Arctic tundra biome, including the mountainous Oro Arctic. We modeled biomass for the year 2020 using a new synthesis dataset of field biomass harvest measurements, Landsat satellite seasonal synthetic composites, ancillary geospatial data, and machine learning models. Additionally, we quantified pixel-wise uncertainty in biomass predictions using Monte Carlo simulations and validated the models using a robust, spatially blocked and nested cross-validation procedure. Observed plant and woody plant biomass values ranged from 0 to ∼6000 g m −2 (mean ≈ 350 g m −2 ), while predicted values ranged from 0 to ∼4000 g m −2 (mean ≈ 275 g m −2 ), resulting in model validation root-mean-squared-error (RMSE) ≈ 400 g m −2 and R 2 ≈ 0.6. Our maps not only capture large-scale patterns of plant biomass and woody plant dominance across the Arctic that are linked to climatic variation (e.g., thawing degree days), but also illustrate how fine-scale patterns are shaped by local surface hydrology, topography, and past disturbance. By providing data on plant biomass across Arctic tundra ecosystems at the highest resolution to date, our maps can significantly advance research and inform decision-making on topics ranging from Arctic vegetation monitoring and wildlife conservation to carbon accounting and land surface modeling.
The confluence of machine learning and multiscale simulations
Multiscale modeling has a long history of use in structural biology, as computational biologists strive to overcome the time- and length-scale limits of atomistic molecular dynamics. Contemporary machine learning techniques, such as deep learning, have promoted advances in virtually every field of science and engineering and are revitalizing the traditional notions of multiscale modeling. Deep learning has found success in various approaches for distilling information from fine-scale models, such as building surrogate models and guiding the development of coarse-grained potentials. However, perhaps its most powerful use in multiscale modeling is in defining latent spaces that enable efficient exploration of conformational space. In conclusion, this confluence of machine learning and multiscale simulation with modern high-performance computing promises a new era of discovery and innovation in structural biology.
Data-driven model for divertor plasma detachment prediction
We present a fast and accurate data-driven surrogate model for divertor plasma detachment prediction leveraging the latent feature space concept in machine learning research. Our approach involves constructing and training two neural networks: an autoencoder that finds a proper latent space representation (LSR) of plasma state by compressing the multi-modal diagnostic measurements and a forward model using multi-layer perception (MLP) that projects a set of plasma control parameters to its corresponding LSR. By combining the forward model and the decoder network from autoencoder, this new data-driven surrogate model is able to predict a consistent set of diagnostic measurements based on a few plasma control parameters. In order to ensure that the crucial detachment physics is correctly captured, highly efficient 1D UEDGE model is used to generate training and validation data in this study. The benchmark between the data-driven surrogate model and UEDGE simulations shows that our surrogate model is capable of providing accurate detachment prediction (usually within a few per cent relative error margin) but with at least four orders of magnitude speed-up, indicating that performance-wise, it has the potential to facilitate integrated tokamak design and plasma control. Comparing with the widely used two-point model and/or two-point model formatting, the new data-driven model features additional detachment front prediction and can be easily extended to incorporate richer physics. This study demonstrates that the complicated divertor and scrape-off-layer plasma state has a low-dimensional representation in latent space. Understanding plasma dynamics in latent space and utilising this knowledge could open a new path for plasma control in magnetic fusion energy research.
Pedestal origin and extrapolation of high-density small edge-localised-modes peak parallel energy fluence in ITER and SPARC
Experimental analysis and simulations with the BOUT++ code show that small edge-localised modes (ELMs) in reactor-relevant high-density regimes originate in a region close to the separatrix and only marginally perturb the pedestal structure. The measured divertor peak parallel energy fluence (ε ∥,peak ) for a database of small ELM scenarios in DIII-D and ASDEX Upgrade can be reproduced, within 40 % accuracy on average, if an ad hoc modification of the Eich peak parallel ELM energy fluence model is applied to account for the small ELM pedestal birth location. This allows for first-order extrapolation of small-ELM divertor ε ∥,peak to ITER and SPARC, resulting in values that satisfy the nominal melting threshold of tungsten monoblocks of 12 MJ m −2 . The findings reported in this study, both via modelling and direct measurements, constitute a step forward in assessing small ELMs in high edge-collisionality scenarios as a viable plasma regime for the operation of next-generation fusion machines.
Narrow operator models of stellarator equilibria in Fourier Zernike basis
Numerical computation of the ideal magnetohydrodynamic (MHD) equilibrium magnetic field is at the base of stellarator optimisation and provides the starting point for solving more sophisticated partial differential equations like transport or turbulence models. Conventional approaches solve for a single stationary point of the ideal MHD equations, which is fully defined by three invariants and the numerical scheme employed by the solver. We present the first numerical approach that can solve for a continuous distribution of equilibria with fixed boundary and rotational transform, varying only the pressure invariant. This approach minimises the force residual by optimising parameters of multilayer perceptrons that map from a scalar pressure multiplier to the Fourier Zernike basis as implemented in the modern stellarator equilibrium solver DESC.
Decoding the Gate Opening Mechanism of the Flexible Framework RPM3–Zn upon Hydrocarbon Inclusion
Although the existence of a gate-opening phenomenon in the flexible RPM3–Zn is well known, the actual mechanism remains a mystery. Here, we provide a full picture that unambiguously identifies and explains the gate-opening mechanism in RPM3–Zn upon exposure to various hydrocarbons—acetylene, ethylene, ethane, propane, and butane—by combining insights gained from calorimetry, adsorption isotherms, PXRD, in situ infrared spectroscopy, and ab initio simulations. We find that the key to gate opening in this framework is the stretching of a bond between an O and the Zn metal center (COO–Zn), acting as a “stabilizer”, which weakens the necessary support required for the structure to remain intact. Consequently, an increasing concentration of guest molecules exerts sufficient internal pressure to induce strong structural transformations in the unit cell shape and volume, thus triggering the gate opening. Here, our results are critical to understanding the gate opening in several other flexible frameworks and provide an opportunity to fine- tune hydrocarbon separation.
Competitive Adsorption of NH 3 and H 2 O in Metal–Organic Framework Materials: MOF-74
Elucidating the interaction between coadsorbed H 2 O and NH 3 in metal–organic frameworks (MOFs) is of paramount importance to uncover mechanistic details of their competitive coadsorption behavior as well as to guide the design of new materials for enhanced NH 3 adsorption in humid environments. Nevertheless, molecular competition between NH 3 and H 2 O within the confined nanopores of MOFs was rarely explored and is poorly understood due to challenges in characterization. Here, we combine in situ infrared spectroscopy with ab initio calculations to unveil the competition of NH 3 and H 2 O for occupying active adsorption sites in the representative MOF-74 material by analyzing the kinetics and energetics of the molecular exchange process. We find that at a high NH 3 /H 2 O ratio, the incoming NH 3 is capable of displacing metal-bound H 2 O and moving it to secondary adsorption sites due to the stronger binding of NH 3 compared with H 2 O. Interestingly, the reverse process of H 2 O displacing metal-bound NH 3 is also possible upon increasing water concentration. Our calculations show that H 2 O exchanging the preabsorbed NH 3 at the metal site is driven not only by a reduced kinetic barrier but also by a favorable energetical state resulting from the formation of water clusters at metal sites and intermolecular H-bonding between the metal-coordinated H 2 O and displaced NH 3 . Our finding emphasizes that the description of molecular occupation in MOFs at equilibrium cannot simply be established by comparing molecules’ binding energies at their strongest binding sites derived by single-component measurements; rather, intermolecular interactions can greatly affect molecular distribution at equilibrium. Furthermore, we show that vibrational modes of adsorbed NH 3 are markedly perturbed upon contact with water molecules, accompanied by a large frequency shift (>30 cm –1 ) and considerable intensity decrease, which arises from the freezing of NH 3 vibrations by coadsorbed H 2 O. As a result, the mechanistic insight obtained through our study sheds light on molecular coadsorption processes in MOFs and helps to assess NH 3 removal efficiency of MOFs containing open-metal sites under realistic conditions, particularly in the presence of humidity.
Heterolytic Scission of Hydrogen Within a Crystalline Frustrated Lewis Pair
We report the heterolysis of molecular hydrogen under ambient conditions by the crystalline frustrated Lewis pair (FLP) 1-{2-[bis(pentafluorophenyl)boryl]phenyl}-2,2,6,6-tetramethylpiperidine (KCAT). The gas–solid reaction provides an approach to prepare the solvent-free, polycrystalline ion pair KCATH2 through a single crystal to single crystal transformation. The crystal lattice of KCATH2 increases in size relative to the parent KCAT by approximately 2%. Microscopy was used to follow the transformation of the highly colored red/orange KCAT to the colorless KCATH2 over a period of 2 h at 300 K under a flow of H2 gas. There is no evidence of crystal decrepitation during hydrogen uptake. Inelastic neutron scattering employed over a temperature range from 4–200 K did not provide evidence for the formation of polarized H2 in a precursor complex within the crystal at low temperatures and high pressures. However, at 300 K, the INS spectrum of KCAT transformed to the INS spectrum of KCATH2. Calculations suggest that the driving force is more favorable in the solid state compared to the solution or gas phase, but the addition of H2 into the KCAT crystal is unfavorable. Ab Initio methods were used to calculate the INS spectra of KCAT, KCATH2, and a possible precursor complex of H2 in the pocket between the B and N of crystalline KCAT. Ex-situ NMR showed that the transformation from KCAT to KCATH2 is quantitative and our results suggest that the hydrogen heterolysis process occurs via H2 diffusion into the FLP crystal with a rate-limiting movement of H2 from inactive positions to reactive sites.
Identifying the Gate-Opening Mechanism in the Flexible Metal–Organic Framework UTSA-300
Atomic-level understanding of the gate-opening phenomenon in flexible porous materials is an important step toward learning how to control, design, and engineer them for applications such as the separation of gases from complex mixtures. Here, we report such mechanistic insight through an in-depth study of the pressure-induced gate-opening phenomenon in our earlier reported metal–organic framework (MOF) Zn(dps) 2 (SiF 6 ) (dps = 4,4'-dipyridylsulfide), also called UTSA-300, using isotherm and calorimetry measurements, in situ infrared spectroscopy, and ab initio simulations. UTSA-300 is shown to selectively adsorb acetylene (C 2 H 2 ) over ethylene (C 2 H 4 ) and ethane (C 2 H 6 ) and undergoes an abrupt gate-opening phenomenon, making this framework a highly selective gas separator of this complex mixture. The selective adsorption is confirmed by pressure-dependent in situ infrared spectroscopy, which, for the first time, shows the presence of multiple C 2 H 2 species with varying strengths of bonding. A rare energetic feature at the gate-opening condition of the flexible MOF is observed in our differential heat energies, directly measured by calorimetry, showcasing the importance of this tool in adsorption property exploration of flexible frameworks and offering an energetic benchmark for further energy-based fundamental studies. Based on the agreement of this feature with ab initio-based adsorption energies of C 2 H 2 in the closed-pore structure UTSA-300a (“a” refers to the activated form), this feature is assigned to the weakening of the H-bond C–H···F formed between C 2 H 2 and fluorine of the MOF. Furthermore, our analysis identifies the weakening of this H-bond, the expansion of the closed-pore MOF upon successive C 2 H 2 coadsorption until its volume is close to that of the open-pore MOF, and the spontaneous gate opening to energetically favor C 2 H 2 adsorption in the open-pore structure as crucial steps in the gate-opening mechanism in this system.
Revisiting Competitive Adsorption of Small Molecules in the Metal–Organic Framework Ni-MOF-74
To precisely evaluate the potential of metal-organic frameworks (MOFs) for gas separation and purification applications, it is crucial to understand how various molecules competitively adsorb inside MOFs. In this paper, we combine in situ infrared spectroscopy with ab initio calculations to investigate the mechanisms associated with co-adsorption of a number of small molecules including CO, NO, and CO 2 inside the prototypical framework Ni-MOF-74. Surprisingly, we find that the displacement of CO bound inside Ni-MOF-74 (binding energy of 53 kJ/mol) is readily driven by CO 2 exposure, even though CO 2 has a noticeably weaker binding energy of only 41 kJ/mol; meanwhile, the significantly more strongly binding NO molecule (90 kJ/mol) is not able to easily displace bound CO inside Ni-MOF74. These results show that single-phase binding energies of a molecule inside the MOF cannot completely describe their interaction with the MOF in the presence of other guest molecules. Here, we unveil a number of crucial factors such as the kinetic barrier, partial pressure, secondary binding sites, and guest-host/lateral interactions that control the co-adsorption process and combined with the binding energy are better descriptors of the behavior and adsorption of gas mixtures inside MOFs.
Luminescent Metal–Organic Framework for the Selective Detection of Aldehydes
The detection of toxic, hazardous chemical species is an important task because they pose serious risks to either the environment or human health. Luminescent metal–organic frameworks (LMOFs) as alternative sensors offer rapid and sensitive detection of chemical species. Interactions between chemical species and LMOFs result in changes in the photoluminescence (PL) profile of the LMOFs which can be readily detected using a simple fluorometer. Herein, we report the use of a robust, Zn-based LMOF, [Zn 5 (μ 3 -OH) 2 (adtb) 2 (H 2 O) 5 ·5 DMA] (Zn-adtb, LMOF-341), for the selective detection of benzaldehyde. Upon exposure to benzaldehyde, Zn-adtb experiences significant luminescent quenching, as characterized through PL experiments. Photoluminescent titration experiments reveal that LMOF-341 has a detection limit of 64 ppm and a Ksv value of 179 M –1 for benzaldehyde. Furthermore, we study the guest–host interactions that occur between LMOF-341 and benzaldehyde through in situ Fourier transform infrared and computational modeling employing density functional theory. The results show that benzaldehyde interacts more strongly with LMOF-341 compared to formaldehyde and propionaldehyde. Furthermore, our combined studies also reveal that the mechanism of luminescence quenching originates from an electron-transfer process.