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DOE OSTI2025

Neural Active Manifolds: Nonlinear Dimensionality Reduction for Uncertainty Quantification

We present a new approach for nonlinear dimensionality reduction, specifically designed for computationally expensive mathematical models. We leverage autoencoders to discover a one-dimensional neural active manifold (NeurAM) capturing the model output variability, through the aid of a simultaneously learnt surrogate model with inputs on this manifold. Our method only relies on model evaluations and does not require the knowledge of gradients. The proposed dimensionality reduction framework can then be applied to assist outer loop many-query tasks in scientific computing, like sensitivity analysis and multifidelity uncertainty propagation. In particular, we prove, both theoretically under idealized conditions, and numerically in challenging test cases, how NeurAM can be used to obtain multifidelity sampling estimators with reduced variance by sampling the models on the discovered low-dimensional and shared manifold among models. Several numerical examples illustrate the main features of the proposed dimensionality reduction strategy and highlight its advantages with respect to existing approaches in the literature.

Autoencoders↗

DOE OSTI2022

Cylindrical C 96 Fullertubes: A Highly Active Metal-Free O 2 -Reduction Electrocatalyst

A new isolation protocol was recently reported for highly purified metallic Fullertubes D 5h -C 90 , D 3d -C 96 , and D 5d -C 100 , which exhibit unique electronic features. Here, we report the oxygen reduction electrocatalytic behavior of C 60 , C 70 (spheroidal fullerenes), and C 90 , C 96 , and C 100 (tubular fullerenes) using a combination of experimental and theoretical approaches. C 96 (a metal-free catalyst) displayed remarkable oxygen reduction reaction (ORR) activity, with an onset potential of 0.85 V and a halfway potential of 0.75 V, which are close to the state-of-the-art Pt/C benchmark catalyst values. Here we achieved an excellent power density of 0.75 W cm −2 using C 96 as a modified cathode in a proton-exchange membrane fuel cell, comparable to other recently reported efficient metal-free catalysts. Combined band structure (experimentally calculated) and free-energy (DFT) investigations show that both favorable energy-level alignment active catalytic sites on the carbon cage are responsible for the superior activity of C 96 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Cylindrical C 96 Fullertubes: A Highly Active Metal‐Free O 2 ‐Reduction Electrocatalyst

Abstract A new isolation protocol was recently reported for highly purified metallic Fullertubes D 5h ‐C 90 , D 3d ‐C 96 , and D 5d ‐C 100, which exhibit unique electronic features. Here, we report the oxygen reduction electrocatalytic behavior of C 60 , C 70 (spheroidal fullerenes), and C 90 , C 96 , and C 100 (tubular fullerenes) using a combination of experimental and theoretical approaches. C 96 (a metal‐free catalyst) displayed remarkable oxygen reduction reaction (ORR) activity, with an onset potential of 0.85 V and a halfway potential of 0.75 V, which are close to the state‐of‐the‐art Pt/C benchmark catalyst values. We achieved an excellent power density of 0.75 W cm −2 using C 96 as a modified cathode in a proton‐exchange membrane fuel cell, comparable to other recently reported efficient metal‐free catalysts. Combined band structure (experimentally calculated) and free‐energy (DFT) investigations show that both favorable energy‐level alignment active catalytic sites on the carbon cage are responsible for the superior activity of C 96 .

Sanad, Mohamed Fathi↗

DOE OSTI2020

Large Scale Tensor Factorization via Parallel Sketches

Tensor factorization methods have recently gained increased popularity. A key feature that renders tensors attractive is the ability to directly model multi-relational data. In this work, we propose ParaSketch, a parallel tensor factorization algorithm that enables massive parallelism, to deal with large tensors. The idea is to compress the large tensor into multiple small tensors, decompose each small tensor in parallel, and combine the results to reconstruct the desired latent factors. Prior art in this direction entails potentially very high complexity in the (Gaussian) compression and final combining stages. Adopting sketching matrices for compression, the proposed method enjoys a dramatic reduction in compression complexity, and features a much lighter combining step. Moreover, theoretical analysis shows that the compressed tensors inherit latent identifiability under mild conditions, hence establishing correctness of the overall approach. Numerical experiments corroborate the theory and demonstrate the effectiveness of the proposed algorithm.

block term decomposition↗

DOE OSTI2020

Characterization and identification of HPC applications at leadership computing facility

High Performance Computing (HPC) is an important method for scientific discovery via large-scale simulation, data analysis, or artificial intelligence. Leadership-class supercomputers are expensive, but essential to run large HPC applications. The Petascale era of supercomputers began in 2008, with the first machines achieving performance in excess of one petaflops, and with the advent of new supercomputers in 2021 (e.g., Aurora, Frontier), the Exascale era will soon begin. However, the high theoretical computing capability (i.e., peak FLOPS) of a machine is not the only meaningful target when designing a supercomputer, as the resources demand of applications varies. A deep understanding of the characterization of applications that run on a leadership supercomputer is one of the most important ways for planning its design, development and operation. In order to improve our understanding of HPC applications, user demands and resource usage characteristics, we perform correlative analysis of various logs for different subsystems of a leadership supercomputer. This analysis reveals surprising, sometimes counter-intuitive patterns, which, in some cases, conflicts with existing assumptions, and have important implications for future system designs as well as supercomputer operations. For example, our analysis shows that while the applications spend significant time on MPI, most applications spend very little time on file I/O. Combined analysis of hardware event logs and task failure logs show that the probability of a hardware FATAL event causing task failure is low. Combined analysis of control system logs and file I/O logs reveals that pure POSIX I/O is used more widely than higher level parallel I/O. Based on holistic insights of the application gained through combined and co-analysis of multiple logs from different perspectives and general intuition, we engineer features to "fingerprint" HPC applications. We use t-SNE (a machine learning technique for dimensionality reduction) to validate the explainability of our features and finally train machine learning models to identify HPC applications or group those with similar characteristic. To the best of our knowledge, this is the first work that combines logs on file I/O, computing, and inter-node communication for insightful analysis of HPC applications in production.

Liu, Zhengchun↗

DOE OSTI2026

Systematic Benchmarking of Climate Models: Methodologies, Applications, and New Directions

As climate models become increasingly complex, there is a growing need to comprehensively and systematically assess model performance with respect to observations. Given the increasing number and diversity of climate model simulations in use, the community has moved beyond simple model intercomparison and toward developing methods capable of benchmarking a large number of simulations against a suite of climate metrics. Here, we present a detailed review of evaluation and benchmarking methods and approaches developed in the last decade, focusing primarily on scientific implications for Coupled Model Intercomparison Project (CMIP) simulations and CMIP6 results that contributed to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Based on this review, we explain the resulting contemporary philosophy of model benchmarking, and provide clear distinctions and definitions of the terms model verification, process validation, evaluation, and benchmarking. While significant progress has been made in model development based on systematic evaluation and benchmarking efforts, some climate system biases still remain. The development of open‐source community software packages has played a fundamental role in identifying areas of significant model improvement and bias reduction. We review the key features of several software packages that have been commonly used over the past decade to evaluate and benchmark global and regional climate models. Additionally, we discuss best practices for the selection of evaluation and benchmarking metrics and for interpreting the obtained results, the importance of selecting suitable sources of reference data and accurate uncertainty quantification.

Environmental sciences↗

DOE OSTI2021

Alloying–realloying enabled high durability for Pt–Pd-3d-transition metal nanoparticle fuel cell catalysts

Abstract Alloying noble metals with non-noble metals enables high activity while reducing the cost of electrocatalysts in fuel cells. However, under fuel cell operating conditions, state-of-the-art oxygen reduction reaction alloy catalysts either feature high atomic percentages of noble metals (>70%) with limited durability or show poor durability when lower percentages of noble metals (<50%) are used. Here, we demonstrate a highly-durable alloy catalyst derived by alloying PtPd (<50%) with 3d-transition metals (Cu, Ni or Co) in ternary compositions. The origin of the high durability is probed by in-situ/operando high-energy synchrotron X-ray diffraction coupled with pair distribution function analysis of atomic phase structures and strains, revealing an important role of realloying in the compressively-strained single-phase alloy state despite the occurrence of dealloying. The implication of the finding, a striking departure from previous perceptions of phase-segregated noble metal skin or complete dealloying of non-noble metals, is the fulfilling of the promise of alloy catalysts for mass commercialization of fuel cells.

25 ENERGY STORAGE↗

DOE OSTI2023

Decay Dose Shielding Analysis with Hybrid Unstructured Mesh/Constructive Solid Geometry Monte Carlo Calculation and ADVANTG Acceleration

Here, the target segments of the Oak Ridge National Laboratory Second Target Station (STS) neutron production facility become highly activated due to spallation reactions or nuclei transmutation by primary protons and emitted neutrons. Once the target segments are removed from their location within the core vessel, decay dose rates must be accurately quantified to determine the shielding configurations of remote-handling tools and transport casks and to aid in planning maintenance activities. For this analysis, we utilized a hybrid unstructured mesh (UM)/constructive solid geometry approach for calculating spallation products and neutron fluxes, activation calculations using the AARE package that includes the CINDER2008 activation code to calculate the decay photon source at different cooling times, and the ADVANTG code to accelerate the final decay photon transport calculation. Both Type 316 stainless steel (SS-316) and lead were investigated as candidates for shielding materials. The decay photon transport calculation through the thick SS-316 or lead shields exhibited between 25 and 30 orders-of-magnitude attenuations in the radial direction, depending on the shield. Such a difficult shielding calculation required advanced variance reduction. ADVANTG has some missing features, which limits its usability in spallation neutron source applications. It does not support volumetric sources created for MCNP6.2 UM capability. An approximate source was created for this problem. Not only was this approximate source needed for running the ADVANTG calculation to generate the weight windows, but also it was essential to develop source biasing (SB) parameters that were crucial for dramatically accelerating the decay photon transport in this problem. With this approximate source, the analysis was completed in a very reasonable computational time, and the design of the STS remote-handling equipment was finalized. This paper compares the efficiency of Monte Carlo simulations with different weight window and SB parameters calculated using different approximate ADVANTG calculations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Assessment of a Grade 91 Steel Forging and Seamless Pipe Section After 141,000-Hours of Operation in a Superheat Outlet Header

In this work, two unique heats of 9Cr creep strength enhanced ferritic (CSEF) steels extracted from a retired superheat outlet header after 141,000 hours of service were evaluated. These two CSEF steels were a forging manufactured to SA-182 F91 (F91) reducer and a seamless pipe produced to SA-335 P91 (P91) pipe. Their creep deformation and fracture behavior were assessed using a lever arm creep frame integrated with in-situ high-temperature digital image correlation (DIC) system. Critical metallurgical and microstructure factors, including composition, service damage, grain matrix degradation, precipitates, and inclusions were quantitatively characterized to link the performance of the two service aged F91 and P91 CSEF steels. The creep test results show the F91 and P91 steels exhibit a large variation in creep strength and creep ductility. The F91 steel fractured at 572 hours while P91 steel fractured at 1,901 hours when subjected to a test condition of 650 °C and 100 MPa. The nominal creep strains at fracture were 12.5% (F91) and 14.5% (P91), respectively. The high-resolution DIC strain measurements reveal the local creep strain in F91 was about 50% while the local creep strain in P91 was >80%. The characterization results show that the F91 steel possessed pre-existing creep damage from its time in service, a higher fraction of inclusions, and a faster matrix grain coarsening rate. These features contribute to the observed reduction in performance for the F91 steel. The context for these findings, and the importance of metallurgical risk in an integrated life management approach will be emphasized.

Wang, Yiyu↗

DOE OSTI2023

Model Energy Codes End-Use Opportunity Analysis

Model energy codes (ASHRAE Standard 90.1 and IECC) has significantly impacted programs and policies aimed at improving energy efficiency both in United States and across the globe. Standard 90.1, first published in 1975 (originally referred to as Standard 90) provides minimum energy efficiency guidelines for designing, constructing, operating, and maintaining new construction and renovated buildings. It is updated continuously, with new editions published every 3 years. Pacific Northwest National Laboratory (PNNL) conducts simulated energy and cost savings analysis of model energy codes to determine their expected impact and to track progress toward net-zero goals and efficiency targets. Additionally, the simulation outputs are examined to assess how energy is used across primary systems within prominent U.S. commercial building types to understand how energy is used in each building type at the end-use level and to identify areas for improvements in future code cycles. This end-use opportunity analysis of model energy codes provides ASHRAE technical committees and other interested parties a better understanding of how the Standard affects various building systems and end uses, specifically, those in the Standard that most prominently influence energy efficiency. Additionally, findings provide industry stakeholders guidance in identifying building types and end-uses with the most potential for energy efficiency improvements through energy codes and those that may require beyond code measures to meet energy use reduction targets. This paper summarizes features and functionalities of a webtool created by PNNL to explore and visualize the End-Use opportunity analysis.

ASHRAE 90.1, Energy Codes, Tableau↗

DOE OSTI2021

Probing polarization dynamics at specific domain configurations: Computer-vision based automated experiment in piezoresponse force microscopy

Topological defects in ferroelectric materials have attracted much attention due to the emergence of conductive, ferroic, and magnetic functionalities. However, many topological configurations dynamically evolve during the switching processes, making them a challenge to characterize via traditional techniques. Here, we implement an automated experimentation approach for the exploration of functional properties in BiFeO 3 thin films. Specifically, we visualize the ferroelectric domain structures via single frequency piezoresponse force microscopy and implement a computer vision-based algorithm to discover features of interest at which spectroscopic measurements are taken. Subsequently, we employ dimensionality reduction techniques to reveal characteristic polarization behaviors at these features. This approach can be extended to other spectroscopies and modalities to probe only specific features of interest, ultimately enabling dynamical processes in ferroelectrics to be studied.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Interpretable and flexible non-intrusive reduced-order models using reproducing kernel Hilbert spaces

This paper develops an interpretable, non-intrusive reduced-order modeling technique using regularized kernel interpolation. Existing non-intrusive approaches approximate the dynamics of a reduced-order model (ROM) by solving a data-driven least-squares regression problem for low-dimensional matrix operators. Our approach instead leverages regularized kernel interpolation, which yields an optimal approximation of the ROM dynamics from a user-defined reproducing kernel Hilbert space. We show that our kernel-based approach can produce interpretable ROMs whose structure mirrors full-order model structure by embedding judiciously chosen feature maps into the kernel. The approach is flexible and allows a combination of informed structure through feature maps and closure terms via more general nonlinear terms in the kernel. We also derive a computable a posteriori error bound that combines standard error estimates for intrusive projection-based ROMs and kernel interpolants. In conclusion, the approach is demonstrated in several numerical experiments that include comparisons to operator inference using both proper orthogonal decomposition and quadratic manifold dimension reduction.

Data-driven model reduction↗

DOE OSTI2023

The Reductive Leaching of Chalcopyrite by Chromium(II) Chloride for the Rapid and Complete Extraction of Copper

A hydrometallurgical process is developed to lower the costs of copper production and thereby sustain the use of copper throughout the global transition to renewable energy technologies. The unique feature of the hydrometallurgical process is the reductive treatment of chalcopyrite, which is in contrast to the oxidative treatment more commonly pursued in the literature. Chalcopyrite reduction by chromium(II) ion is described for the first time and superior kinetics are shown. At high concentrate loadings of 39, 78, and 117 gL –1 , chalcopyrite reacted completely within minutes at room temperature and pressure. The XRD, SEM-EDS, and XPS measurements indicate that chalcopyrite reacts to form copper(I) chloride (CuCl). After the reductive treatment, the mineral products are leached by iron(III) sulfate to demonstrate the complete extraction of copper. The chromium(II) ion may be regenerated by an electrolysis unit inspired by an iron chromium flow battery in a practical industrial process.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Probabilistic Data-Driven Sampling via Multi-Criteria Importance Analysis

Although supercomputers are becoming increasingly powerful, their components have thus far not scaled proportionately. Compute power is growing enormously and is enabling finely resolved simulations that produce never-before-seen features. However, I/O capabilities lag by orders of magnitude, which means only a fraction of the simulation data can be stored for post hoc analysis. Prespecified plans for saving features and quantities of interest do not work for features that have not been seen before. Data-driven intelligent sampling schemes are needed to detect and save important parts of the simulation while it is running. Here, we propose a novel sampling scheme that reduces the size of the data by orders-of-magnitude while still preserving important regions. The approach we develop selects points with unusual data values and high gradients. Finally, we demonstrate that our approach outperforms traditional sampling schemes on a number of tasks.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Facile Synthesis of Micrometer-sized Hierarchical Porous Si@C Anodes for High-Performance Lithium-Ion Batteries

Extensive studies have been conducted to determine if replacing graphite anodes with silicon is feasible since silicon possesses a high capacity and is an abundant resource; however, Si anodes suffer significant volume changes during cycling, which pulverizes its structure and subsequently causes rapid capacity fading. We used SiO2 nanoparticles (nSiO2) and low-cost coal-derived humic acid as a feedstock to synthesize high performance Si-C anodes for Li-ion Batteries through spray drying, calcination, and magnesiothermic reductions. Our Si-C anodes features micrometer-sized porous Si coated with a graphitized carbon shell (mpSi@C). The hierarchical graphitized carbon shell and porous silicon structure relieve the mechanical stress of the Si phase upon cycling, which stabilizes the structure. This mpSi@C composite design allows for a high initial discharge capacity of 2199.9 mAh g-1 at 0.1 A g-1 and a cycling performance of 68% capacity retention after 100 cycles at 1.0 A g-1. The multipoint contact between the Si anode and C structure allows for a remarkable performance rate of 566.3 mAh g-1 at 5.0 A g-1.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2024

Aspatial Oxyanion Reduction Catalysis

The scientifically challenging problem of catalytically reducing oxyanions was explored and presented great opportunities in terms of the environment, economics and energy self-sufficiency. Oxyanions are pervasive as they are found in many areas of technology, all forms of life, in minerals, and as synthetic materials. Given their ubiquity, the contamination of inorganic oxyanions in drinking water is a national problem as 26 states and Puerto Rico have reported high concentrations of these harmful pollutants. The goals achieved by this research was to catalytically investigate the reduction of new oxyanions utilizing sustainable earth abundant catalysts featuring bio-inspired ligands. Further, ligand modifications will be explored to understand the impact on oxyanion reduction, mechanistic studies, and for electrocatalytic oxyanion reduction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Traps and transport resistance are the next frontiers for stable non-fullerene acceptor solar cells

Stability is one of the most important challenges facing material research for organic solar cells (OSC) on their path to further commercialization. In the high-performance material system PM6:Y6 studied here, we investigate degradation mechanisms of inverted photovoltaic devices. We have identified two distinct degradation pathways: one requires the presence of both illumination and oxygen and features a short-circuit current reduction, the other one is induced thermally and marked by severe losses of open-circuit voltage and fill factor. We focus our investigation on the thermally accelerated degradation. Our findings show that bulk material properties and interfaces remain remarkably stable, however, aging-induced defect state formation in the active layer remains the primary cause of thermal degradation. The increased trap density leads to higher non-radiative recombination, which limits the open-circuit voltage and lowers the charge carrier mobility in the photoactive layer. Furthermore, we find the trap-induced transport resistance to be the major reason for the drop in fill factor. Our results suggest that device lifetimes could be significantly increased by marginally suppressing trap formation, leading to a bright future for OSC.

14 SOLAR ENERGY↗

DOE OSTI2023

Dual-site catalysts featuring platinum-group-metal atoms on copper shapes boost hydrocarbon formations in electrocatalytic CO2 reduction

Abstract Copper-based catalyst is uniquely positioned to catalyze the hydrocarbon formations through electrochemical CO 2 reduction. The catalyst design freedom is limited for alloying copper with H-affinitive elements represented by platinum group metals because the latter would easily drive the hydrogen evolution reaction to override CO 2 reduction. We report an adept design of anchoring atomically dispersed platinum group metal species on both polycrystalline and shape-controlled Cu catalysts, which now promote targeted CO 2 reduction reaction while frustrating the undesired hydrogen evolution reaction. Notably, alloys with similar metal formulations but comprising small platinum or palladium clusters would fail this objective. With an appreciable amount of CO-Pd 1 moieties on copper surfaces, facile CO * hydrogenation to CHO * or CO-CHO * coupling is now viable as one of the main pathways on Cu(111) or Cu(100) to selectively produce CH 4 or C 2 H 4 through Pd-Cu dual-site pathways. The work broadens copper alloying choices for CO 2 reduction in aqueous phases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗