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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Emulating the impact of additional proton-proton interactions in the ATLAS simulation by pre-sampling sets of inelastic Monte Carlo events

The accurate simulation of additional interactions at the ATLAS experiment for the analysis of proton-proton collisions delivered by the Large Hadron Collider presents a significant challenge to the computing resources. During the LHC Run (2015-2018) there were up to 70 inelastic interactions per bunch crossing, which need to be accounted for in Monte Carlo (MC) production. In this document, a new method to account for these additional interactions in the simulation chain is described. Instead of sampling the inelastic interactions and adding their energy deposits to a hard-scatter interaction one-by-one, the inelastic interactions are presampled, independent of the hard scatter, and stored as combined events. Consequently, for each hard-scatter interaction only one such presampled event needs to be added as part of the simulation chain. For the Run 2 simulation chain, with an average of 35 interactions per bunch crossing, this new method provides a substantial reduction in MC production CPU needs of around 20%, while reproducing the properties of the reconstructed quantities relevant for physics analyses with good accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Benchmarking the multiconfigurational Hartree method by the exact wavefunction of two harmonically trapped bosons with contact interaction

Highlights: • Comparison of exact and numerical single-particle density matrices. • Benchmark for multiconfigurational Hartree. • Convergence for large number of orbitals. We consider two bosons in a one-dimensional harmonic trap, interacting by a contact potential, and compare the exact solution of this problem to a self-consistent numerical solution by using the multiconfigurational time-dependent Hartree (MCTDH) method. We thereby benchmark the predictions of the MCTDH method with a few-body problem that has an analytical solution for the most commonly experimentally realized interaction potential in ultracold quantum gases. It is found that exact ground state energy and first order correlations are accurately reproduced by MCTDH up to the intermediate dimensionless coupling strengths corresponding to typical background scattering lengths of magnetically trapped ultracold dilute Bose gases. For larger couplings, established for example by (a combination of) Feshbach resonances and optical trapping, the MCTDH approach overestimates the depth of the trap-induced correlation dip of first order correlations in position space, as well as overestimates the fragmentation, defined as the average relative occupation of orbitals other than the energetically lowest one. We anticipate that qualitatively similar features in the correlation function may arise for larger particle numbers, paving the way for a quantitative assessment of the accuracy of MCTDH by experiments with ultracold atoms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Large Eddy Simulation of Low-Reynolds-Number Turbulent Flow of Low-Prandtl-Number Fluid in a Tight Lattice Bundle for Assessment of Reynolds-Averaged Navier-Stokes Turbulence Model

The MARVEL (Microreactor Applications Research Validation and Evaluation) microreactor utilizes natural circulation as core cooling mechanism and liquid metal as a primary coolant. Moreover, the reactor core has a pitch-to-diameter ratio of 1.056, which is considered a tight lattice configuration. Numerous studies have widely reported that Reynolds-Averaged Navier-Stokes (RANS) turbulence models inaccurately predict heat transfer in liquid metals and fail to capture flow pulsations that can occur within tight lattices, leading to further inaccuracies in simulation results. Therefore, evaluating the accuracy of RANS turbulence models in the thermal-hydraulic analysis of the MARVEL microreactor core is crucial for assessing reactor safety. In this study, a Large Eddy Simulation (LES) of the MARVEL microreactor core subchannel was conducted and compared with a RANS simulation to evaluate the accuracies and conservatism of the RANS model. The flow pulsation in a tight lattice predicted by LES enhanced the heat transfer, whereas the RANS model underpredicted it. Consequently, the RANS model predicted the peak cladding temperature higher than the LES model, but the discrepancy between the two approaches was not significant due to the good thermal characteristics of the liquid metal. It can be concluded that the steady-state RANS model is effective for the thermal analysis of liquid-metal-cooled MARVEL microreactor core and can provide conservative predictions from a safety analysis perspective.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2023

Quantifying resonance behavior in the fusion of O 17 with C 12 at above-barrier energies

Recent measurement of the fusion excitation function for 17 O + 12 C reported the significant suppression of the fusion cross section at E c.m. ≈ 14 MeV. This suppression was hypothesized to signal the existence of a 16 O + n + 12 C molecular configuration. Using the active-target detector MuSIC@Indiana provided an effective means of reexamining the fusion excitation function for 17 O + 12 C.The accuracy of this thick-target measurement is strengthened through comparison with the thin-target measurement of the excitation function for 17 F + 12 C. Furthermore, the result provides important information about the dependence of the average fusion cross section for the oxygen isotopic chain on neutron excess.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Air quality and health impacts of the 2020 wildfires in California

Abstract Background Wildfires in 2020 ravaged California to set the annual record of area burned to date. Clusters of wildfires in Northern California surrounded the Bay Area covering the skies with smoke and raising the air pollutant concentrations to hazardous levels. This study uses the Fire Inventory from the National Center for Atmospheric Research database and the Community Multiscale Air Quality model to estimate the effects of wildfire emissions on air quality during the period from August 16 to October 28 of 2020. In addition, low-cost sensor data for fine particulate matter (PM 2.5 ) from the PurpleAir network is used to enhance modeled PM 2.5 concentrations. The resulting impacts on ozone and PM 2.5 are used to quantify the health impacts caused by wildfires using the Benefits Mapping and Analysis Program – Community Edition. Results Wildfire activity significantly increased direct PM 2.5 emissions and emissions of PM 2.5 and ozone precursors. Direct PM 2.5 emissions surged up to 38 times compared to an average day. Modeling results indicated that wildfires alone led to a rise in ozone daily maximum 8-h average by up to 10 ppb and exceeded PM 2.5 air quality standards in numerous locations by up to 10 times. While modeled PM 2.5 concentrations were lower than measurements, correcting these with PurpleAir data improved the accuracy. The correction using PurpleAir data increased estimates of wildfire-induced mortality due to PM 2.5 exposure by up to 16%. Conclusions The increased hospital admissions and premature mortality attributed to wildfires were found to be comparable to the health impacts avoided by strategies aimed at meeting ozone and PM2.5 air quality standards. This suggests that widespread wildfire emissions can negate years of efforts dedicated to controlling air pollution. The integration of low-cost sensor data proved invaluable in refining the estimates of health impacts from PM2.5 resulting from wildfires.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

FPGA-accelerated SpeckleNN with SNL for real-time X-ray single-particle imaging

We present the implementation of a specialized version of our previously published unified embedding model, SpeckleNN, for real-time speckle pattern classification in X-ray Single-Particle Imaging (SPI), using the SLAC Neural Network Library (SNL) on an FPGA platform. This hardware realization transitions SpeckleNN from a prototypic model into a practical edge solution, optimized for running inference near the detector in high-throughput X-ray free-electron laser (XFEL) facilities, such as those found at the Linac Coherent Light Source (LCLS). To address the resource constraints inherent in FPGAs, we developed a more specialized version of SpeckleNN. The original model, which was designed for broader classification across multiple biological samples, comprised ~5.6 million parameters. The new implementation, while reducing the parameter count to 64.6K (a 98.8% reduction), focuses on maintaining the model's essential functionality for real-time operation, achieving an accuracy of 90%. Furthermore, we compressed the latent space from 128 to 50 dimensions. This implementation was demonstrated on the KCU1500 FPGA board, utilizing 71% of available DSPs, 75% of LUTs, and 48% of FFs, with an average power consumption of 9.4W according to the Vivado post-implementation report. The FPGA performed inference on a single image with a latency of 45.015 microseconds at a 200 MHz clock rate. In comparison, running the same inference on an NVIDIA A100 GPU resulted in an average power consumption of ~73W and an image processing latency of around 400 microseconds. Our FPGA-accelerated version of SpeckleNN demonstrated significant improvements, achieving an 8.9 × speedup and a 7.8 × reduction in power consumption compared to the GPU implementation. Key advancements include model specialization and dynamic weight loading through SNL, which eliminates the need for time-consuming FPGA design re-synthesis, allowing fast and continuous deployment of models (re)trained online. These innovations enable real-time adaptive classification and efficient vetoing of speckle patterns, making SpeckleNN more suited for deployment in XFEL facilities. This implementation has the potential to significantly accelerate SPI experiments and enhance adaptability to evolving experimental conditions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Development of interatomic potential and effect of ordering on defect properties in CrMnV

Developing materials that can withstand extreme environments, such as high radiation doses and elevated temperatures, is crucial for next-generation particle accelerators, including the 2.4 MW Long-Baseline Neutrino Facility. High-Entropy Alloys have emerged as promising candidates for beam window materials due to their superior mechanical strength, corrosion resistance, and radiation tolerance. In this study, we focus on the Cr–Mn–V alloy system, developing and employing machine-learning interatomic potentials (MLIPs) to investigate the formation of an ordered phase and its influence on defect properties. Using hybrid Monte Carlo-Molecular Dynamics simulations, we observe the formation of a B2-ordered phase at lower temperatures, consistent with Density Functional Theory (DFT) predictions. Ordered structures display a bimodal distribution of migration energies and reduced mean square displacement values, indicating suppressed vacancy diffusion. Our results also show that the migration energy barrier varies based on the atomic species, with Mn and V exhibiting the highest and lowest average barriers, respectively. These findings suggest that atomic ordering inhibits defect mobility, potentially enhancing the radiation resistance of CrMnV alloys. The validated MLIP provides a reliable framework for simulations that are faster than traditional DFT while maintaining the accuracy required to study defect and ordering properties.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Accurate equation of state of H 2 – He binary mixtures up to 5.4 GPa

Brillouin scattering spectroscopy has been used to obtain an accurate (<1%) ρ-P equation of state (EOS) of 1:1 and 9:1 H 2 -He molar mixtures from 0.5 to 5.4 GPa at 296 K. Our calculated equations of state indicate close agreement with the experimental data right to the freezing pressure of hydrogen at 5.4 GPa. The measured velocities agree on average, within 0.5%, of an ideal mixing model. The ρ-P EOSs presented have a standard deviation of under 0.3% from the measured densities and under 1% deviation from ideal mixing. Furthermore, a detailed discussion of the accuracy, precision, and sources of error in the measurement and analyses of our equations of state is presented.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

A Novel Framework to Project the Permafrost Fate With Explicit Quantification of Soil Property and Future Climate Uncertainties

This study develops a novel general framework to project the permafrost fate with rigorous uncertainty quantification to assess dominant sources. Borehole temperature records from three sites in the Russian western Arctic are used to constrain the uncertainty of a high‐fidelity freeze‐thaw model. Projections from 9 Global Climate Models (GCM) are stochastically downscaled to generate future trajectories of surface ground heat flux. Under the two emission scenarios SSP2‐4.5 and SSP5‐8.5, the projected average thawing depths by 2100 vary from 0.4 to 14.4 m or 2.1 to 17.7 m, and the increase in the top 10 m average temperature from 2015 to 2100 is 1.2–2.7°C or 1.9–3.0°C. The results show that the freeze‐thaw model uncertainty can sometimes dominate over that of GCM outputs, calling for site‐specific information to improve model accuracy. The framework is applicable for understanding permafrost degradation and related uncertainties at larger scales.

Bayesian downscaling↗
DOE OSTI2023

Generating high-resolution total canopy SIF emission from TROPOMI data: Algorithm and application

Solar-induced chlorophyll fluorescence (SIF) is a rapidly advancing front in modeling global terrestrial gross primary production (GPP). Canopy total SIF emissions (SIF total ) are mechanistically linked to the plant photosynthesis, and can be estimated from satellite observed SIF (SIF obs ) through radiative transfer modeling. However, the current satellite SIF obs and thus SIF total are available only at coarse spatial resolutions from several kilometers to tens of kilometers, inhibiting the application at fine spatial scales. Here, in this work, we proposed an algorithm to generate both global high-resolution SIF total (HSIF total ) and high-resolution SIF obs (HSIF obs ) at 1 km from low-resolution SIF obs (LSIF obs ) from the TROPOspheric Monitoring Instrument (TROPOMI), which has a spatial resolution at nadir of 3.5 km by 5.6–7 km. Our statistical method is based on the law of energy conservation and uses satellite derived fraction of absorbed photosynthetically active radiation, fluorescence efficiency, and the escape probability of fluorescence. We evaluated the accuracy of our HSIF total using the Orbiting Carbon Observatory-2 SIF (R 2 = 0.78). We found that the spatial resolution had clear effects on the relationship between HSIF total and GPP. We also compared HSIF total to 8-day averaged tower GPP from 135 flux sites and found that they were better correlated when HSIF total was averaged over a 1-km radius around the tower than when averaged over a larger radius. Our study provided a unique high-resolution HSIF total product, which will advance the estimation of GPP by extrapolating site-level relationships to the global scale.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Multifidelity Approach to Sensitivity Estimation in Large-Eddy Simulation

A novel approach to compute affordable approximate sensitivities in a large-eddy simulation (LES) is proposed and assessed. The approach is based on solving a Reynolds-averaged Navier–Stokes (RANS) problem that has been linearized around the mean LES solution, with closure modeling required for the linearized changes in turbulent Reynolds stresses. In the present study, the closure modeling is based on a linearized algebraic turbulence model. The method is assessed for the flow over a NACA0012 airfoil at a fixed angle of attack, with the Reynolds number as the variable parameter. The results show that, provided an accurate linearized closure model, the method predicts the correct sensitivity of the skin friction coefficient and the mean velocity field at the cost of a linearized RANS, which provides an important proof-of-concept for this approach. Here, the linearized algebraic turbulence model with standard model coefficients produces a reasonably accurate sensitivity, but the results also suggest that accuracy could be gained from recalibrating the model coefficients for this new use of the model.

42 ENGINEERING↗
DOE OSTI2025

A novel approach to increase accuracy in remotely sensed evapotranspiration through basin water balance and flux tower constraints

Remote sensing-derived evapotranspiration (RSET) products capture the spatiotemporal variations of evapotranspiration (ET) from field to basin scales with unprecedented details. However, their accuracy varies across RSET estimation methods and diverse hydroclimate regions. While ET modeling efforts to account for biophysical processes and controlling parameters have made good progress in recent years, a parallel approach of integrating in-situ ET with RSET could reduce biases in RSET products. Basin water balance ET (WBET) and flux tower ET are widely applied to evaluate RSET accuracy, yet such ET measurements are rarely used for RSET bias corrections, especially for large area applications. To address this issue, we propose a novel approach: the water balance equivalence (WABE) method, which generates spatially continuous WBET for correcting biases in RSET products. The WABE method computes synthetic WBET by integrating observed WBET and flux tower-derived FLUXCOM ET, which fills the spatial gaps of observed WBET and generates a spatially continuous WBET dataset. Synthetic WBET (2002–2015 annual average) of eight-digit hydrologic unit code (HUC8) basins across the conterminous United States (CONUS), constituting 44 % (887 out of 2035 basins) of CONUS basins, was determined within 2.0 % (RMSE = 12 %) of observed WBET at CONUS and between 1–12 % (RMSE = 3–33 %) across 18 regions in CONUS. With WABE-based bias corrections, the overall annual bias of RSET decreased from 10 % (RMSE = 34 %) to 6 % (RMSE = 26 %) across 37 flux tower sites. The WABE method offers a new approach for RSET accuracy improvement and shows great promise for large area implementations with a potential to yield substantial benefits for building accurate basin water budgets and water management decisions.

Khand, Kul↗
DOE OSTI2022

A deep learning approach for semantic segmentation of unbalanced data in electron tomography of catalytic materials

In computed TEM tomography, image segmentation represents one of the most basic tasks with implications not only for 3D volume visualization, but more importantly for quantitative 3D analysis. In case of large and complex 3D data sets, segmentation can be an extremely difficult and laborious task, and thus has been one of the biggest hurdles for comprehensive 3D analysis. Heterogeneous catalysts have complex surface and bulk structures, and often sparse distribution of catalytic particles with relatively poor intrinsic contrast, which possess a unique challenge for image segmentation, including the current state-of-the-art deep learning methods. To tackle this problem, we apply a deep learning-based approach for the multi-class semantic segmentation of a γ-Alumina/Pt catalytic material in a class imbalance situation. Specifically, we used the weighted focal loss as a loss function and attached it to the U-Net’s fully convolutional network architecture. We assessed the accuracy of our results using Dice similarity coefficient (DSC), recall, precision, and Hausdorff distance (HD) metrics on the overlap between the ground-truth and predicted segmentations. Our adopted U-Net model with the weighted focal loss function achieved an average DSC score of 0.96 ± 0.003 in the γ-Alumina support material and 0.84 ± 0.03 in the Pt NPs segmentation tasks. We report an average boundary-overlap error of less than 2 nm at the 90th percentile of HD for γ-Alumina and Pt NPs segmentations. The complex surface morphology of γ-Alumina and its relation to the Pt NPs were visualized in 3D by the deep learning-assisted automatic segmentation of a large data set of high-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) tomography reconstructions.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Measuring Transverse Displacements Using Unmanned Aerial Systems Laser Doppler Vibrometer (UAS-LDV): Development and Field Validation

Measurement of bridge displacements is important for ensuring the safe operation of railway bridges. Traditionally, contact sensors such as Linear Variable Displacement Transducers (LVDT) and accelerometers have been used to measure the displacement of the railway bridges. However, these sensors need significant effort in installation and maintenance. Therefore, railroad management agencies are interested in new means to measure bridge displacements. This research focuses on mounting Laser Doppler Vibrometer (LDV) on an Unmanned Aerial System (UAS) to enable contact-free transverse dynamic displacement of railroad bridges. Researchers conducted three field tests by flying the Unmanned Aerial Systems Laser Doppler Vibrometer (UAS-LDV) 1.5 m away from the ground and measured the displacement of a moving target at various distances. The accuracy of the UAS-LDV measurements was compared to the Linear Variable Differential Transducer (LVDT) measurements. The results of the three field tests showed that the proposed system could measure non-contact, reference-free dynamic displacement with an average peak and root mean square (RMS) error for the three experiments of 10% and 8% compared to LVDT, respectively. Such errors are acceptable for field measurements in railroads, as the interest prior to bridge monitoring implementation of a new approach is to demonstrate similar success for different flights, as reported in the three results. This study also identified barriers for industrial adoption of this technology and proposed operational development practices for both technical and cost-effective implementation.

42 ENGINEERING↗
DOE OSTI2024

Reductive Analysis with Compiler-Guided Large Language Models for Input-Centric Code Optimizations

Input-centric program optimization aims to optimize code by considering the relations between program inputs and program behaviors. Despite its promise, a long-standing barrier for its adoption is the difficulty of automatically identifying critical features of complex inputs. This paper introduces a novel technique, reductive analysis through compiler-guided Large Language Models (LLMs), to solve the problem through a synergy between compilers and LLMs. It uses a reductive approach to overcome the scalability and other limitations of LLMs in program code analysis. The solution, for the first time, automates the identification of critical input features without heavy instrumentation or profiling, cutting the time needed for input identification by 44× (or 450× for local LLMs), reduced from 9.6 hours to 13 minutes (with remote LLMs) or 77 seconds (with local LLMs) on average, making input characterization possible to be integrated into the workflow of program compilations. Optimizations on those identified input features show similar or even better results than those identified by previous profiling-based methods, leading to optimizations that yield 92.6% accuracy in selecting the appropriate adaptive OpenMP parallelization decisions, and 20-30% performance improvement of serverless computing while reducing resource usage by 50-60%.

Input-Centric Optimization↗
DOE OSTI2025

Accelerating GNNs on GPU Sparse Tensor Cores through N:M Sparsity-Oriented Graph Reordering

Recent advancements in GPU hardware support have introduced the capability to leverage N:M sparse patterns for substantial performance gains. Graphs in Graph Neural Networks (GNNs) are typically sparse, but the sparsity is often irregular, not conforming to such sparse patterns. In this paper, we propose a novel graph reordering algorithm, the first of its kind, to reshape irregular graph data into the N:M structured sparse pattern at the tile level, allowing linear-algebra-based graph operations in GNNs to benefit from the N:M sparse hardware. The optimization is lossless, maintaining the accuracy of GNN. It can remove 98-100\% violations of the N:M sparse patterns at the vector level, and increase the proportion of conforming graphs in SuiteSparse collection from 5-9\% to 88.7-93.5\%. On A100 GPUs, the optimization accelerates Sparse Matrix Matrix (SpMM) by up to 43X (2.3X -- 7.5X on average) and speeds up the key graph operations in GNNs on real graphs by as much as 8.6X (3.5X on average).

artificial intelligence, graph neural networks↗
DOE OSTI2025

Real-Time Ammonia and Humidity Monitoring with Ultra-Fast Conductometric Sensors Based on Porphyrin and Phthalocyanine Complexes

Organic semiconductors like porphyrins and phthalocyanines are attracting a wide range of researchers due to their versatile electrical properties and sensing performances in conductometric sensors. In this study, we investigate two types of π-extended porphyrins, which share the same macrocyclic structure but differ in their central metal. These porphyrins are employed as sublayers in bilayer heterojunction devices, with the lutetium bisphthalocyanine complex, LuPc 2 , serving as the common top layer. Remarkably, the central metal in the porphyrin macrocycle significantly influences the solubility of the materials and, consequently, the surface topography of the resulting bilayer heterojunction devices. This structural variation translates into distinct electrical and sensing performances. The device incorporating nickel as metal centre (AM2) demonstrates superior sensitivity towards NH3, with a relative response (RR) of ca. -7% at 90 ppm, an ultra-fast response time of about 9 s, and an impressive limit of detection (LOD) of 250 ppb, whereas, the device that has zinc as metal centre in sublayer (AM3) exhibits RR value of ca. -0.9% at 90 ppm with t 90 of ca. 120 s and LOD of 2 ppm. Both devices are evaluated under randomly varying NH 3 concentration and RH value. The results shows that the AM2-based sensor allows following NH 3 in real-time, while the AM3-based sensor delivers an average concentration over time. On the other hand, the AM2-based sensor exhibits slow kinetics under RH exposure, while the AM3-based sensor precisely mirrors the pattern of random RH changes generated by the software, demonstrating its exceptional responsiveness and accuracy in tracking humidity fluctuations. In conclusion, these findings underscore the critical role of the metal centre in tuning the electrical and sensing properties of the heterojunction devices.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Assessing cathode property prediction via exchange-correlation functionals with and without long-range dispersion corrections

In this work, we benchmark calculated interlayer spacings, average topotactic voltages, thermodynamic stabilities, and band gaps in layered lithium transition-metal oxides (TMOs) and their de-lithiated counterparts, which are used in lithium-ion batteries as positive electrode materials, against available experimental data. Specifically, we examine the accuracy of properties calculated within density functional theory (DFT) using eight different treatments of electron exchange-correlation: the strongly constrained and appropriately normed (SCAN) and Perdew–Burke–Ernzerhof (PBE) density functionals, Hubbard-U-corrected SCAN and PBE (i.e., SCAN+U and PBE+U), and SCAN(+U) and PBE(+U) with added long-range dispersion (D) interactions (i.e., DFT(+U)+D). van der Waals interactions are included respectively via the revised Vydrov-Van Voorhis (rVV10) for SCAN(+U) and the DFT-D3 for PBE(+U). We find that SCAN-based functionals predict larger voltages due to an underestimation of stability of the MO2 systems, while also predicting smaller interlayer spacings compared to their PBE-based counterparts. Furthermore, adding dispersion corrections to PBE has a greater effect on voltage predictions and interlayer spacings than with SCAN, indicating that DFT-SCAN – despite being a ground-state theory – fortuitously captures some short and medium-range dispersion interactions better than PBE. While SCAN-based and PBE-based functionals yield qualitatively similar band gap predictions, there is no significant quantitative improvement of SCAN-based functionals over the corresponding PBE-based versions. Finally, we expect SCAN-based functionals to yield more accurate property predictions than the respective PBE-based functionals for most TMOs, given SCAN's stronger theoretical underpinning and better predictions of systematic trends in interlayer spacings, intercalation voltages, and band gaps obtained in this work.

25 ENERGY STORAGE↗