Search NASA⌕ Search

SEARCH · Search NASA

Results for “Structural performance prediction”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Thermal-healing of lattice defects for high-energy single-crystalline battery cathodes

Abstract Single-crystalline nickel-rich cathodes are a rising candidate with great potential for high-energy lithium-ion batteries due to their superior structural and chemical robustness in comparison with polycrystalline counterparts. Within the single-crystalline cathode materials, the lattice strain and defects have significant impacts on the intercalation chemistry and, therefore, play a key role in determining the macroscopic electrochemical performance. Guided by our predictive theoretical model, we have systematically evaluated the effectiveness of regaining lost capacity by modulating the lattice deformation via an energy-efficient thermal treatment at different chemical states. We demonstrate that the lattice structure recoverability is highly dependent on both the cathode composition and the state of charge, providing clues to relieving the fatigued cathode crystal for sustainable lithium-ion batteries.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Exploring the Effects of Node Topology, Connectivity, and Metal Identity on the Binding of Nerve Agents and Their Hydrolysis Products in Metal–Organic Frameworks

Recent studies have shown that metal–organic frameworks (MOFs) built from hexanuclear M(IV) oxide cluster nodes are effective catalysts for nerve agent hydrolysis, where the properties of the active sites on the nodes can strongly influence the reaction energetics. The connectivity and metal identity of these M6 nodes can be easily tuned, offering extensive opportunities for computational screening to predict promising new materials. Thus, we used density functional theory (DFT) to examine the effects of node topology, connectivity, and metal identity on the binding energies of multiple nerve agents and their corresponding hydrolysis products. By computing an optimization metric based on the relative binding strengths of key hydrolysis reaction species (water, agent, and bidentate-bound products), we predicted optimal M6 nodes for hydrolyzing specific nerve agent and simulant molecules, where our results are in qualitative agreement with observed experimental trends. This analysis highlighted the notion that no single metal or node topology is optimal for all possible organophosphates, suggesting that MOFs should be selected based on the agent of interest. Using the large amount of data generated from our DFT calculations, we then derived quantitative structure–activity relationship (QSAR) models to help explain the complex trends observed in the binding energies. Through linear regression, we identified the most important descriptors for describing the binding of nerve agents and their hydrolysis products to M6 nodes. These results suggested that both molecular and node properties, including both structural and chemical features, collectively contribute to the binding energetics. By performing a thorough statistical analysis, we showed that our QSAR models are capable of making quantitatively accurate binding energy predictions for nerve agents and their hydrolysis products in a wide variety of M(IV)-MOFs. In conclusion, the insights gained herein can be used to guide future experiments for the synthesis of MOFs with enhanced catalytic activity for organophosphate hydrolysis.

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

Predicting Saturated Hydraulic Conductivity over Time for Degrading Saltstone Vault Concrete - 20376

At the Savannah River Site (SRS), low-level liquid tank waste is mixed with cementitious materials to produce a solid, monolithic waste form referred to as saltstone. The liquid saltstone mixture is pumped into massive concrete structures referred to as saltstone disposal units (SDUs) for curing and long-term storage. SDUs are vaults made of high performance concrete forming a barrier between saltstone and the surrounding environment. Predicting the long-term performance of SDU vault concrete, as well as the saltstone material itself, is critical to understanding the potential releases of radionuclides to the surrounding environment as described in the Saltstone Disposal Facility (SDF) performance assessment (PA). The hydraulic conductivity profile for SDU vault concrete was conservatively assumed to degrade linearly from the initial saturated hydraulic conductivity of the intact concrete over a long period of time. The initial hydraulic properties of saturated SDU vault concrete have been measured, and the final hydraulic properties of degraded vault concrete are assumed to be the same as the porous backfill material that will surround the vault after closure. The assumed final condition of the vault concrete is also conservative in that it provides a greater estimate of water penetration through the vault than would generally be expected. Therefore, insights that provide more realistic but conservative estimates as to how degradation processes may change the hydraulic properties of the SDU vault concrete, the time dependent relationship of the vault concrete hydraulic properties to degradation (and thus time), and the final hydraulic properties of the vault concrete provides useful additions to the SDF PA. This paper focuses on the assumption that the saltstone vault concrete saturated hydraulic conductivity would decrease linearly as a function of degradation time; this assumption resulted from a worst-case, weighted (by relative layer thickness), arithmetic average of intact and backfill material hydraulic conductivities representing flow parallel to a layered system. This research indicates that the effective saturated hydraulic conductivity of the degraded SDU vault concrete should be based on the relative thickness-weighted geometric mean of the intact and degraded layers hydraulic conductivities. This alternative, which is supported extensively in the relevant literature and evaluated in this paper, leads to a more realistic and defensible estimate of the effective saturated hydraulic conductivity in degraded vault concrete while remaining conservative. The impact of using the more realistic estimate of the effective saturated hydraulic conductivity for the degraded concrete decreases the effective saturated hydraulic conductivity by up to several orders of magnitude for early times (i.e., when the SDU vault concrete is assumed degraded via sulfate attack and carbonation). This revised estimate of the effective saturated hydraulic conductivity has been implemented in the recently revised SDF PA. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Interpretable, extensible linear and symbolic regression models for charge density prediction using a hierarchy of many-body correlation descriptors

Here, density functional theory (DFT) is routinely used to make electronic structure predictions for high-throughput screening of materials and molecules for technologically relevant areas, like the identification of better catalysts, electronic materials, and drug discovery. However, the DFT formalism is limited by (a) its poor (quadratic-to-quartic) scaling, and (b) the need to perform repeated eigenvalue computations of the electronic Hamiltonian as part of its self-consistent field (SCF) iteration procedure to obtain the converged ground state electron density, ρ (r). Approaches that directly predict ρ (r) of a structure with high accuracy can accelerate conventional SCF calculations and can also be used in linearly scaling methods such as orbital-free DFT. To this end, we present a procedure to predict the ground state electron density of molecular and periodic three-dimensional systems directly from the atomic structure with a particular emphasis on physical interpretability. In our framework, ρ (r) is modeled using many-body correlation descriptors that accurately capture the effects of local atomic arrangements in the neighborhood of a grid point. Our use of a linear regression scheme to fit to charge density data enables transparent analysis of the relative contributions of various types of local atomic correlations. By systematically including increasingly complex correlations, our model is shown to accurately predict ρ (r) for a variety of chemically and electronically diverse systems — amorphous Ge, Al(001) slab, crystalline Ga 2 O 3 , molecular benzene, and polyethylene. We then demonstrate a symbolic regression-based protocol to construct easily computable, interpretable features from lower-order correlations that significantly improves our electron density predictions with effectively no increase in the computational cost.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Utilizing CellBox to Describe Varied Dynamical Systems

CellBox is a hybrid modeling framework that integrates machine-learning approaches with explicit mathematical modeling to determine the behavior of dynamical systems, intended for determining the complex interactions of molecule and phenotype within a cellular environment using perturbation/response-based data. The original work utilized analysis of a melanoma cell line, SK-Mel-133, which we sought to expand to and evaluate effectiveness in describing other nonlinear systems both within and outside of biology. We evaluated with two models. One model is based upon the free-fall of an object under a velocity-dependent drag force; this produced inherent perturbation/response data. The other model is based upon a form of mass-action molecular kinetics; for this, we ran the simulation to steady state in each species for each perturbation and select the steady state as input for CellBox. We determined that CellBox, outside of its original use case, can still strongly predict even non-biological dynamical systems in the small-perturbation regime, with r = 0.99977 to 0.85123, p < 0.05 for freefall. However, CellBox cannot accurately predict behavior within large-perturbation regimes, p >> 0.05 for freefall. In addition, we performed a naïve comparison of CellBox network structure to known network structure in the mass-action model to assess accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Thermoelectric Behavior of Structurally Disordered Lanthanide and Actinide Silicides

Lanthanide and actinide intermetallic materials exhibit intriguing electronic, structural, and magnetic properties arising from their partially filled f orbitals. Strong f-electron correlations can significantly modify the electronic density of states, carrier scattering, and transport behavior, making these systems promising candidates for unconventional thermoelectric materials. In previous studies, single crystals of A1.33T4Al8Si2 (A = Ce, Th, U, Np; T = Co, Ni) were synthesized. This structure consists of ordered TAl2 corrugated double layers separated by disordered An–Si monolayers. The inherent disorder within the An–Si layers has been linked to unusual magnetic behavior, such as spin-glass phenomena, and is expected to strongly influence phonon scattering and charge-carrier transport relevant to thermoelectric performance. However, the thermoelectric properties of this material family have not previously been explored. In this work, DFT calculations were performed to evaluate the electronic density of states, band structures, and thermoelectric properties, including the effects of doping. To validate these predictions, Ce1.33Co4Al8Si2 single crystals were grown and experimentally characterized. Additionally, a new Yb1.33Co4Al8Si2 phase was synthesized and characterized using single-crystal XRD, SEM-EDS, magnetization measurements, and thermoelectric measurements. These results provide insight into structure–property relationships governing thermoelectric behavior in f-electron intermetallics and offer guidance for future thermoelectric materials.

37 - INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL C↗
DOE OSTI2020

Atomic-Scale Structural Mapping of Active Sites in Monolayer PGM-Free Catalysts by Low-Voltage 4D-STEM

Two-dimensional (2D) materials have attracted a large amount of attention in both basic and applied fields, and scanning transmission electron microscopy (STEM) is often uniquely well-suited for characterizing the atomic-scale structure of these materials [1-4]. As a result, STEM is poised to significantly impact progress on platinum group metal (PGM)-free catalysts, which are currently under intense development to enable low-cost, commercially viable hydrogen fuel cells [5]. While recent advancements have resulted in fuel cell performance comparable to Pt catalysts by some measures [6], cell durability remains a significant challenge, limiting practical applications [7]. Catalytically active sites in PGM-free materials are proposed to be FeN4 complexes embedded in a graphene lattice (Fig. 1b) within layered or other larger materials, but this is still under debate largely due to the range of potential actives sites predicted by computational methods and lack of methods for directly validating these models [5]. Fundamental insights into the atomic structure and resulting degradation pathways of proposed active sites are therefore needed to fully understand and control cell performance and durability [6].2D materials typically make ideal samples for STEM, but those within PGM-free catalysts present additional challenges since these materials are often defect-rich, with a high density of edges, dopant atoms, etc., which significantly increase susceptibility to beam damage at standard operating voltages. This makes analysis of potential FeN4 active sites particularly challenging, since a large proportion of Fe exists at edge sites where beam-induced atomic displacements can prohibit high-resolution structural characterization [6]. Conventional dark-field imaging compounds this problem by producing less signal for a given dose and being less sensitive to light elements than dose-efficient phase contrast imaging techniques such as those enabled by four-dimensional (4D)-STEM [8-10] (Fig. 1a). Consequently, active site structural analysis is often left to methods such as low-resolution imaging combined with quantum chemical calculations [6], which hinders accurate determination of reaction and degradation mechanisms.Here, we demonstrate direct atomic-scale structural mapping of FeN4 sites by performing low-voltage 4D-STEM on a model PGM-free catalyst system with many exposed monolayer regions. To accomplish this, we pair a 30 keV aberration-corrected probe with a fast pixelated detector that has optimal performance at low beam voltages [11]. This enables us to simultaneously image light and heavy elements with high signal-to-noise by center-of-mass analysis (Fig. 1c) while minimizing beam-induced atomic displacements at sensitive sites. The monolayer nature of these materials additionally allows for experimental validation by direct comparison with multislice simulations [12] of model structures (Fig. 1d-e). This work demonstrates how low-voltage 4D-STEM will provide new insights into the atomic-scale structure and degradation mechanisms of active sites in PGM-free catalysts, facilitating the development of low-cost hydrogen fuel cells and other energy conversion technologies in the future [13].

Zachman, Michael↗
DOE OSTI2021

Budget constrained machine learning for early prediction of adverse outcomes for COVID-19 patients

Abstract The combination of machine learning (ML) and electronic health records (EHR) data may be able to improve outcomes of hospitalized COVID-19 patients through improved risk stratification and patient outcome prediction. However, in resource constrained environments the clinical utility of such data-driven predictive tools may be limited by the cost or unavailability of certain laboratory tests. We leveraged EHR data to develop an ML-based tool for predicting adverse outcomes that optimizes clinical utility under a given cost structure. We further gained insights into the decision-making process of the ML models through an explainable AI tool. This cohort study was performed using deidentified EHR data from COVID-19 patients from ProMedica Health System in northwest Ohio and southeastern Michigan. We tested the performance of various ML approaches for predicting either increasing ventilatory support or mortality. We performed post hoc analysis to obtain optimal feature sets under various budget constraints. We demonstrate that it is possible to achieve a significant reduction in cost at the expense of a small reduction in predictive performance. For example, when predicting ventilation, it is possible to achieve a 43% reduction in cost with only a 3% reduction in performance. Similarly, when predicting mortality, it is possible to achieve a 50% reduction in cost with only a 1% reduction in performance. This study presents a quick, accurate, and cost-effective method to evaluate risk of deterioration for patients with SARS-CoV-2 infection at the time of clinical evaluation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Beyond interpolation: Physics-inspired gating transformers for extrapolating irradiation conditions to novel nuclear fuels

The qualification of advanced nuclear fuels relies on irradiation experiments in test reactors that emulate commercial conditions. Designing these tests requires accurate prediction of key irradiation quantities, particularly heat generation rate and burnup, yet obtaining them typically involves computationally expensive multi-step simulation workflows. We propose a physics-inspired gating transformer (PIGT) that integrates an inverse-square, distance-based attenuation into the encoder representation to bias attention toward physically relevant spatial relationships while retaining data-driven flexibility. Using MiniFuel irradiation data from the High Flux Isotope Reactor at Oak Ridge National Laboratory, we benchmark against ensemble methods, feedforward and recurrent networks, convolutional models, and standard transformers. While baseline models perform well under interpolation, they exhibit a pronounced generalization gap when evaluated on fuels not included in the training set. The proposed model consistently improves extrapolative accuracy and stability, yielding the strongest performance on unseen fuel configurations. These results indicate that a lightweight physics structure embedded within attention mechanisms can substantially improve robustness, enabling more reliable surrogate predictions to accelerate the design of nuclear fuel irradiation experiments.

Fuel qualification↗
DOE OSTI2021

Impacts of Anisotropic Porosity on Heat Transfer and Off-Gassing during Biomass Pyrolysis

The pore structure of biogenic materials imbues the ability to deliver water and nutrients through a plant from root to leaf. This anisotropic pore granularity can also play a significant role in processes such as biomass pyrolysis that are used to convert these materials into useful products like heat, fuel, and chemicals. Evolutions in modeling of biomass pyrolysis as well as imaging of pore structures allow for further insights into the concerted physics of phase change-induced off-gassing, heat transfer, and chemical reactions. In this work, we report a biomass single particle model which incorporates these physics to explore the impact of implementing anisotropic permeability and diffusivity on the conversion time and yields predicted for pyrolysis of oak and pine particles. Simulation results showed that anisotropic permeability impacts predicted conversion time more than 2 times when the Biot number is above 0.1 and pyrolysis numbers (Py 1 , Py 2 ) are less than 20. Pore structure significantly impacts predicted pyrolytic conversion time (>8 times) when the Biot number is above 1 and the pyrolysis number is below 1, i.e., the “conduction controlled” regime. Therefore, these nondimensional numbers reflect that when internal heat conduction limits pyrolysis performance, internal pyrolysis off-gassing further retards effective heat transfer rates as a closely coupled phenomenon. Overall, this study highlights physically meaningful opportunities to improve particle-scale pyrolysis modeling and experimental validation relevant to a variety of feedstock identities and preparations, guiding the future design of pyrolyzers for efficient biomass conversion.

09 BIOMASS FUELS↗
DOE OSTI2023

Face‐on Orientation Matches Vertical Organic Electrochemical Transistors for High Transconductance and Superior Non‐Volatility

Abstract The recently developed vertical structure of organic electrochemical transistors (OECTs) can integrate volatile and non‐volatile functions into one reconfigurable device, making it highly promising. However, comparing with the conventional planar OECT (c‐OECT), the understanding of vertical OECT (v‐OECT) working principles and device engineering strategies is still lacking, impeding rational optimization. Since a major difference between c‐ and v‐OECTs is their charge transport directionality, which is highly influenced by crystallite orientations, the orientation–device structure match thus becomes an important yet outstanding topic for OECTs. Herein, using ann‐type small molecule IDIC‐MEG, investigate how much impact such match can have on OECT performance. The IDIC‐MEG c‐OECT fails to work due to the seriously hindered in‐plane electron transport by face‐on orientation. Surprisingly, simply changing the device structure from planar to vertical allows the resultant v‐OECT to exhibit the highest reported transconductance (46.3 mS) among all small‐molecule OECTs, thanks to the match between face‐on orientation and the vertical structure. Such match also leads to excellent non‐volatility, including highly predictable programmability and good operational stability. This work, for the first time, explicitly demonstrates the significance of orientation–device structure match for OECT optimization, establishing new guidelines for achieving high‐performance volatile and non‐volatile OECTs.

Chemistry↗
DOE OSTI2021

Predicting Fluid Flow Regime, Permeability, and Diffusivity in Mudrocks from Multiscale Pore Characterisation

In geoenergy applications, mudrocks prevent fluids to leak from temporary (H 2 , CH 4 ) or permanent (CO 2 , radioactive waste) storage/disposal sites and serve as a source and reservoir for unconventional oil and gas. Understanding transport properties integrated with dominant fluid flow mechanisms in mudrocks is essential to better predict the performance of mudrocks within these applications. In this study, small-angle neutron scattering (SANS) experiments were conducted on 71 samples from 13 different sets of mudrocks across the globe to capture the pore structure of nearly the full pore size spectrum (2 nm–5 μm). We develop fractal models to predict transport properties (permeability and diffusivity) based on the SANS-derived pore size distributions. The results indicate that transport phenomena in mudrocks are intrinsically pore size-dependent. Depending on hydrostatic pore pressures, transition flow develops in micropores, slip flow in meso- and macropores, and continuum flow in larger macropores. Fluid flow regimes progress towards larger pore sizes during reservoir depletion or smaller pore sizes during fluid storage, so when pressure is decreased or increased, respectively. Capturing the heterogeneity of mudrocks by considering fractal dimension and tortuosity fractal dimension for defined pore size ranges, fractal models integrate apparent permeability with slip flow, Darcy permeability with continuum flow, and gas diffusivity with diffusion flow in the matrix. This new model of pore size-dependent transport and integrated transport properties using fractal models yields a systematic approach that can also inform multiscale multi-physics models to better understand fluid flow and transport phenomena in mudrocks on the reservoir and basin scale.

36 MATERIALS SCIENCE↗
DOE OSTI2020

The structural information filtered features (SIFF) potential: Maximizing information stored in machine-learning descriptors for materials prediction

Machine learning inspired potentials continue to improve the ability for predicting structures of materials. However, many challenges still exist, particularly when calculating structures of disordered systems. These challenges are primarily due to the rapidly increasing dimensionality of the feature-vector space which in most machine-learning algorithms is dependent on the size of the structure. In this article, we present a feature-engineered approach that establishes a set of principles for representing potentials of physical structures (crystals, molecules, and clusters) in a feature space rather than a physically motivated space. Our goal in this work is to define guiding principles that optimize information storage of the physical parameters within the feature representations. In this manner, we focus on keeping the dimensionality of the feature space independent of the number of atoms in the structure. Our Structural Information Filtered Features (SIFF) potential represents structures by utilizing a feature vector of low-correlated descriptors, which correspondingly maximizes information within the descriptor. We present results of our SIFF potential on datasets composed of disordered (carbon and carbon–oxygen) clusters, molecules with C 7 O 2 H 2 stoichiometry in the GDB9-14B dataset, and crystal structures of the form (Al x Ga y In z ) 2 O 3 as proposed in the NOMAD Kaggle competition. Our potential's performance is at least comparable, sometimes significantly more accurate, and often more efficient than other well-known machine-learning potentials for structure prediction. However, primarily, we offer a different perspective on how researchers should consider opportunities in maximizing information storage for features.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Carbon Fiber and Syntactic Foam Hybrid Materials via Core–Shell Material Extrusion Additive Manufacturing

Abstract Biological materials often employ hybrid architectures, such as the core–shell (C–S) motif present in porcupine quills and plant stems, to achieve unique specific properties and performance. Drawing inspiration from these natural materials, a new method to fabricate lightweight and stiff C–S architected filaments is reported. Specifically, a C–S printhead conducive to printing highly loaded fiber‐filled inks, as well as a new low‐density syntactic foam ink, are utilized to 3D‐print C–S architectures consisting of a syntactic epoxy foam core surrounded by a stiff carbon fiber‐reinforced epoxy composite shell. Effective printing of test specimens and structures with controlled geometry, composition, and architecture is demonstrated. The new foam ink exhibits density as low as 0.68 g cm −3 and C–S structures exhibit up to 25% higher specific flexural stiffness ( E 1/3 /ρ) than either constituent alone. Finally, a new mechanical model is presented to predict this performance improvement while accounting for potential eccentricity of the core.

Pack, Robert C.↗
DOE OSTI2025

Impact of the Atomic Structure at the BiVO 4 /TiO 2 Interface on the Electronic Properties and Performance of BiVO 4 /TiO 2 Photoanodes

In photoelectrochemical cells, semiconductor electrodes are usually interfaced with protection layers to extend their stability. Ideally, the protection layer should not decrease photocurrent generation. Hence, the conduction band minimum (CBM) and valence band maximum (VBM) of the protection layer should appropriately align with those of the underlying semiconductor electrode to facilitate the desired interfacial charge transfer with minimal interfacial recombination. However, predicting interfacial band alignment can be challenging, as it may vary depending on the detailed interfacial atomic structure. Investigating the effect of the atomic structure at the semiconductor/protection layer junction on the band alignment is also challenging as it requires samples with varied interfaces without altering the semiconductor and protection layers. Here, we considered TiO 2 , the most widely used material as a protection layer, interfaced with a BiVO 4 photoanode, and we fabricated two n-type BiVO 4 (010)/TiO 2 photoanodes where a thin (∼4 nm) amorphous TiO 2 layer was deposited by atomic layer deposition (ALD). While the individual BiVO 4 (010) and TiO 2 layers were identical in these two samples, we modified the interfacial atomic structure at the BiVO 4 /TiO 2 junction by changing which precursor, Ti or O, was introduced first upon deposition of TiO 2 . By experimentally and computationally investigating the differences in these two samples, we show that the band alignments between BiVO 4 and TiO 2 at the interface may not be straightforwardly predicted by the CBM and VBM of bulk BiVO 4 and TiO 2 and that interfacial atomic arrangements can have a marked impact on the electronic properties and photoelectrochemical performance of the BiVO 4 (010)/TiO 2 photoanode.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

Modular performance prediction for scientific workflows using Machine Learning

Scientific workflows provide an opportunity for declarative computational experiment design in an intuitive and efficient way. A distributed workflow is typically executed on a variety of resources, and it uses a variety of computational algorithms or tools to achieve the desired outcomes. Such a variety imposes additional complexity in scheduling these workflows on large scale computers. As computation becomes more distributed, insights into expected workload that a workflow presents become critical for effective resource allocation. In this paper, we present a modular framework that leverages Machine Learning for creating precise performance predictions of a workflow. The central idea is to partition a workflow in such a way that makes the task of forecasting each atomic unit manageable and gives us a way to combine the individual predictions efficiently. We recognize a combination of an executable and a specific physical resource as a single module. This gives us a handle to characterize workload and machine power as a single unit of prediction. Overall, our modular technique of creating atomic modules and deployment of longest-path approach to estimate workflow performance, allows the framework to adapt to highly complex nested directed acyclic workflows and scale to new scenarios, since it does not make assumptions of underlying workflow structure. We present performance estimation results of independent workflow modules executed on the XSEDE SDSC Comet cluster using various Machine Learning algorithms. The results provide insights into the behavior and effectiveness of different algorithms in the context of scientific workflow performance prediction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Development and assessment of hierarchical multi-reward reinforcement learning based potential for silicene with state-of-the-art models

We develop a new interatomic force field for Silicene, a 2D material with a buckled hexagonal lattice structure with high polymorphism. We introduce new parameterizations of a Tersoff model using a hierarchical multi-reward reinforcement learning (RL) methodology coupled with a continuous Monte Carlo Tree Search optimization. Our model significantly outperforms existing methods by enhancing the accuracy of predictions for the structural and thermodynamic properties of seven silicene polymorphs-including structure, energy, equation of state, elasticity, and phonon dispersion-when compared to established models. We further make a comprehensive comparison of the various models in predicting the mechanical and thermal properties of silicene. We trace the origin of the improved performance to the description of the angular dependence in the bond-order term, suggesting that modifying the angular terms in short-range models is essential to capture the structural diversity in low dimensional systems.

2D materials↗
DOE OSTI2022

Light Water Reactor Sustainability: Program Materials Research Pathway (FY22 Technical Program Plan)

The Materials Research (MR) Pathway within the Light Water Reactor Sustainability (LWRS) program is charged with performing R&D to develop the scientific basis for understanding and predicting the long-term environmental degradation behavior of materials in nuclear reactors. Furthermore, the mechanistic understanding of degradation phenomena in materials must be leveraged to develop mitigation and repair strategies and new material alternatives for existing components. This research will provide data and methods to assess the performance of systems, structures, and components essential to safe and sustained reactor operations. The R&D products developed from the LWRS program will be used by stakeholders—including utilities, industry groups, and regulators—to inform operational and regulatory requirements for materials in reactor systems, structures, and components subjected to long-term operation conditions, providing key inputs to regulators and industry. Therefore, the intent of this research is to provide options to reduce the operating costs, which may be in the form of offset maintenance costs due to better predictive models for component lifetimes, improved analyses of materials through nondestructive evaluation, reduced costs for repairs, or extended performance of plants through the selection of improved replacement materials. To provide the best options, industry experience and guidance are important because of their role in coordinated or collaborative research projects. The objectives of this report are to describe the motivation and organization of the MR Pathway within the LWRS program; provide details on the individual research tasks within the MR Pathway; describe the outcomes and deliverables of the MR Pathway, including recent technical highlights and progress; and describe the requirements for performing this critically important research.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗