SEARCH · Engineering Papers

Results for “computational framework”

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.

SourceResearch connectionFrom yearThrough year

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

At least 253 records · Page 14

All sources NASA NTRS DOE OSTI

DOE OSTI2026

Regio- and Stereoselective Lactone Polymerization: Divergent Effect of Catalyst Modification and Monomer Structure

Selective ring-opening polymerization (ROP) of chiral lactones enables access to biodegradable polyesters with precisely controlled microstructures. Here, combined DFT modeling and experimental validation elucidate how fine-tuning of enantiopure SalBinam aluminum catalysts (through introduction of bromine atoms and tert-butyl groups, respectively, in ortho- and para-positions) modulates regio- and stereocontrol in the ROP of methyl glycolide (MeG) and lactide (LA). Computations reveal that regioselectivity in MeG polymerization arises mainly from steric repulsion, with a small contribution from weak stabilizing C-H···Br interactions that favor ring-opening at the glycolic site, consistent with the experimentally enhanced regioselectivity for (R)-MeG. In contrast, the same steric congestion at the ligand’s ortho positions destabilizes the key transition states in rac-LA polymerization, reducing the calculated stereoselectivity. Experiments confirm the predicted loss of stereocontrol, yielding nearly atactic PLA under standard conditions. Extension of the computational framework to rac-MeG polymerization promoted by racemic catalyst identified a low-barrier, stepwise polymer chain exchange pathway that rationalizes the experimentally observed syndiotacticity of poly(lactic-co-glycolic acid).

DFT calculations↗

DOE OSTI2020

Finite-Temperature Structures of Supported Subnanometer Catalysts Inferred via Statistical Learning and Genetic Algorithm-Based Optimization

Single-atom catalysts (SACs) minimize noble metal utilization and can alter the activity and selectivity of supported metal nanoparticles. However, the morphology of active centers, including single atoms and subnanometer clusters of a few atoms, remains elusive due to experimental challenges. The computational cost to describe numerous cluster shapes and sizes makes direct first-principles calculations impractical. We present a computational framework to enable structure determination for single-atom and subnanometer cluster catalysts. As a case study, we obtained the low energy structures of Pd n (n = 1-21) clusters supported on CeO 2 (111), which are critical components of automobile three-way catalysts. Trained on density functional theory data, a three-dimensional cluster expansion is established using statistical learning to describe the Hamiltonian and predict energies of supported Pdn clusters of any structure. Low energy stable and metastable structures are identified using a Metropolis Monte Carlo-based genetic algorithm in the canonical ensemble at 300 K. We observe that supported single atoms sinter to form bilayer clusters and large cluster isomers share similarities in both shape and energy, and elucidate the significance of the support and microstructure on cluster stability. We discovered a simple surrogate structure-energy model, where the energy per atom scales with the square root of the average first coordination number, which can be used to estimate energies and compare the stability of clusters. Our framework, applicable to any metal/support system, fills an important methodological gap to predict the stability of supported metal catalysts in the subnanometer regime.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Topological Data Analysis for Particulate Gels

Soft gels, formed via the self-assembly of particulate materials, exhibit intricate multiscale structures that provide them with flexibility and resilience when subjected to external stresses. Here, this work combines particle simulations and topological data analysis (TDA) to characterize the complex multiscale structure of soft gels. Our TDA analysis focuses on the use of the Euler characteristic, which is an interpretable and computationally scalable topological descriptor that is combined with filtration operations to obtain information on the geometric (local) and topological (global) structure of soft gels. We reduce the topological information obtained with TDA using principal component analysis (PCA) and show that this provides an informative low-dimensional representation of the gel structure. We use the proposed computational framework to investigate the influence of gel preparation (e.g., quench rate, volume fraction) on soft gel structure and to explore dynamic deformations that emerge under oscillatory shear in various response regimes (linear, nonlinear, and flow). Our analysis provides evidence of the existence of hierarchical structures in soft gels, which are not easily identifiable otherwise. Moreover, our analysis reveals direct correlations between topological changes of the gel structure under deformation and mechanical phenomena distinctive of gel materials, such as stiffening and yielding. In summary, we show that TDA facilitates the mathematical representation, quantification, and analysis of soft gel structures, extending traditional network analysis methods to capture both local and global organization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

The Role of Defect Geometry in Localized Emission from Monolayer Tungsten Dichalcogenides

In two-dimensional transition metal dichalcogenides such as tungsten diselenide (WSe 2 ), single photon emission has been broadly attributed to exciton localization from atomic point defects, yet the precise microscopic origins are unclear. This work introduces an empirically grounded computational framework that explains the origins of facile single photon emission in WSe 2 . High-resolution microscopy identifies native defect geometries in monolayer WSe 2 lattices from which the model is built. Here, the qualitative effects of chalcogen type, defect geometry, and mechanical strain on the electronic structure are individually assessed using density functional theory, and a specific divacancy configuration emerges as the candidate for localized single-electron transitions that match observed spectral energies. Spectroscopy and photon correlation measurements further validate this model, establishing a self-consistent link between defect geometry, electronic structure, and quantum emission.

defect emission↗

DOE OSTI2026

Computational Discovery of Ultralow Thermal Conductivity in the Energy-Degenerate Polymorphic Crystal Family A 2 M 2 M’Q 4

Crystalline materials, characterized by their well-defined lattices, typically exhibit a unique global thermodynamic minimum for a specific composition. However, in this study, we discover a quaternary chalcogenide family, A 2 M 2 M’Q 4 (A: alkali metals; M: coinage metal; M’: transition or group-IVA metals; Q: chalcogens), that exhibits pervasive energy (near-)degeneracy. For a given composition, multiple structurally distinct polymorphs exist within a formation enthalpy window of only a few milli-electron volts per atom. We quantify this inherent structural flexibility using a dedicated descriptor, σ f : the standard deviation of formation enthalpies among degenerate (meta)stable polymorphs. The consistently low σf observed across the A 2 M 2 M’Q 4 family signifies a characteristically shallow and frustrated potential energy landscape, which drives pronounced lattice anharmonicity, marking these materials as prime candidates for ultralow lattice thermal conductivity (κ L ). Employing an advanced high-throughput computational framework that integrates thermodynamics, lattice dynamics, and thermal conductivity calculations, we screen 1215 A 2 M 2 M’Q 4 compounds, identifying 30 stable candidates with κ L < 0.5 W m –1 K –1 at 300 K. Among them, Rb 2 Ag 2 SnTe 4 and Rb 2 Au 2 HfTe 4 , two representatives from the IVA and TM subgroups, are predicted to show ultralow room-temperature κ L of 0.174 W m –1 K –1 and 0.295 W m –1 K –1 , respectively. A systematic analysis suggests that the nonbonding and antibonding states induced by “dual rattlers” are the origin of low thermal conductivity in these compounds. Our results position the A 2 M 2 M’Q 4 family as a rich source of intrinsic thermal insulators and suggest that polymorphic energy degeneracy may serve as a valuable signpost for identifying crystalline families with potential anharmonicity.

cations↗

DOE OSTI2022

Spanning the Gap From Bulk to Bin: A Novel Spectral Microphysics Method

Microphysics methods for climate models and numerical weather prediction typically track one, two, or three moments of a droplet size distribution for various categories of liquid, ice, and aerosol. Such methods rely on conversion parameters between these categories, which introduces uncertainty into predictions. While higher-fidelity options such as bin and Lagrangian schemes exist, they require too many degrees of freedom for climate modeling applications and introduce numerical challenges. Here we introduce a flexible spectral microphysics method based on collocation of basis functions. This method generalizes to a linear bulk scheme when using few basis functions and to a smoothed bin scheme with more degrees of freedom. Tested in an idealized box setting, the method improves spectral accuracy for droplet collision-coalescence and may eliminate the need for precipitation autoconversion rates required by bulk methods; furthermore, it generalizes well to multimodal distributions with less complexity than a bin method. The potential to extend this collocation representation to multiple hydrometeor classes suggests a path forward to unify liquid, ice, and aerosol microphysics in a single, flexible, computational framework for climate modeling.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Effect of Nucleation Heterogeneity on Mineral Precipitation in Confined Environments

Abstract The formation of new mineral phases in confined environments, especially in porous media, is crucial for various geological processes like mineralization and diagenesis. The nucleation and precipitation of minerals are initiated at the microscale through fluid‐rock interaction, where dissolution of primary phases leads to supersaturated conditions and nucleation and growth of secondary ones. Previous research has focused primarily on either precipitation or nucleation, without fully exploring their combined impact. Our study introduces a computational framework that integrates classical nucleation theory with the micro‐continuum method. We validated our model by comparing with experiments, and discovered that different surface nucleation rate changes the mode of precipitation from a preferential to uniform precipitate textures. Furthermore, our study uncovered that the conventional deterministic precipitation method tends to underestimate the permeability of the porous matrix. In contrast, the new framework significantly improves model accuracy by incorporating preferential precipitation and heterogeneous nucleation.

58 GEOSCIENCES↗

DOE OSTI2026

Emergence of Diverse Failure Patterns in Weathering‐Induced Landslides: Insights From Particle Finite Element Simulations

Weathering is a fundamental driver of landslide evolution over geological timescales. Despite its ubiquity and importance, quantifying how weathering drives the progressive destabilization of rock slopes remains challenging. In this work, we develop a unified computational framework based on the particle finite element method to investigate the evolution of weathering‐induced landslides, from long‐term weathering to short‐term slope failure and runout dynamics. The framework integrates key processes, including weathering front propagation, time‐dependent strength degradation, rupture surface development, and post‐failure runout dynamics. Through numerical simulation experiments, we elucidate how interactions among weathering characteristics (type, intensity, and rate law), bedrock strength, fracture distribution, and slope geometry govern the failure modes and kinematics of weathering‐induced landslides. Simulations show that matrix‐dominated weathering leads to shallow translational failures, whereas fracture‐dominated weathering produces deep‐seated rotational and compound landslides. Pre‐existing fractures and slope morphology also strongly influence the movement of destabilized landmasses, affecting the failure pattern (e.g., kinematic mode and rupture surface geometry) and post‐failure behavior (e.g., runout velocity). We further demonstrate that the failure time and volume of weathered slopes are governed by the competition between gravitational driving forces and cohesive resisting forces during progressive destabilization. These findings provide new insights into the fundamental mechanisms that drive the emergence of diverse failure patterns of weathering‐induced landslides with important implications for landslide hazard assessment.

Wang, Liang [Eidgenoessische Technische Hochschule↗

DOE OSTI2024

Neutron scattering and neural-network quantum molecular dynamics investigation of the vibrations of ammonia along the solid-to-liquid transition

Abstract Vibrational spectroscopy allows us to understand complex physical and chemical interactions of molecular crystals and liquids such as ammonia, which has recently emerged as a strong hydrogen fuel candidate to support a sustainable society. We report inelastic neutron scattering measurement of vibrational properties of ammonia along the solid-to-liquid phase transition with high enough resolution for direct comparisons to ab-initio simulations. Theoretical analysis reveals the essential role of nuclear quantum effects (NQEs) for correctly describing the intermolecular spectrum as well as high energy intramolecular N-H stretching modes. This is achieved by training neural network models using ab-initio path-integral molecular dynamics (PIMD) simulations, thereby encompassing large spatiotemporal trajectories required to resolve low energy dynamics while retaining NQEs. Our results not only establish the role of NQEs in ammonia but also provide general computational frameworks to study complex molecular systems with NQEs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Atomic-scale understanding of oxide growth and dissolution kinetics of Ni-Cr alloys

Aqueous corrosion of metals is governed by formation and dissolution of a passivating, multi-component surface oxide. Unfortunately, a detailed atomistic description is challenging due to the compositional complexity and the need to consider multiple kinetic factors simultaneously. To this end, we combine experiments with a first-principles-derived, multiscale computational framework that transcends thermodynamic descriptions to explicitly simulate the kinetic evolution of surface oxides of Ni-Cr alloys as a function of composition, temperature, pH, and applied voltage. In the absence of pitting, we identify three distinct voltage regimes, which are kinetically dominated by oxide growth, dissolution, and competitive dissolution and reprecipitation. Evolving compositional gradients and oxide thickness are revealed, including a transition between a metastable Ni-Cr mixed oxide and a thick, porous Ni-dominated oxide. Beyond elucidating the underlying physics, we highlight the need for competing kinetics in models to properly predict the transition from passivation to corrosion. Our results provide a key step towards co-design of alloy composition alongside environmental conditions for sustainable use across a variety of critical energy and infrastructure applications.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Integration of theory and experiment in the modelling of heterogeneous electrocatalysis.

Theoretical modelling is essential to deepen our understanding of heterogeneous electrocatalytic energy conversion processes, such as water splitting. Here, Sharon Hammes-Schiffer and Giulia Galli offer their perspectives on the best strategies for successfully studying such systems. Heterogeneous electrocatalysis is critical to many energy conversion processes. Theoretical and computational approaches are essential to interpret experimental data and provide the mechanistic understanding necessary to design more effective catalysts. However, automated general procedures to build predictive theoretical and computational frameworks are not readily available; specific choices must be made in terms of the atomistic structural model and the level of theory, as well as the experimental data used to inform and validate these choices. Here we outline some best practices for modelling heterogeneous systems and present examples in the context of catalysis at metal electrodes and oxides. The level of theory should be chosen for the specific system and properties of interest, and experimental validation is essential from the beginning to the end of the study. Continuous feedback and ultimate integration between experiment and theory enhances the power of calculations to elucidate mechanisms, identify effective descriptors and clarify design principles.

Hammes-Schiffer, Sharon↗

DOE OSTI2026

Adaptive optical correction for in vivo two-photon fluorescence microscopy with neural fields

Adaptive optics restore ideal imaging performance in complex samples by measuring and correcting optical aberrations but often require custom-built microscopes with carefully aligned wavefront sensing/shaping devices and can be susceptible to sample motion. Here we describe NeAT, a computational framework using neural fields for adaptive optics two-photon fluorescence microscopy. NeAT estimates wavefront aberration and recovers sample structure from a 3D image stack without requiring external datasets for training. Incorporating motion correction in learning and correcting conjugation errors commonly found in commercial microscopes, NeAT is designed for deployment in biological laboratories for in vivo imaging. We validate NeAT’s performance using a custom-built microscope with a wavefront sensor under varying signal-to-noise ratios, aberration and motion conditions. With a commercial microscope, we demonstrate real-time aberration correction for in vivo morphological and functional imaging in the living mouse brain, with NeAT improving the signal and accuracy of glutamate and calcium imaging of synapses and neurons.

Kang, Iksung↗

DOE OSTI2021

Subnetwork representation learning for discovering network biomarkers in predicting lymph node metastasis in early oral cancer

Cervical lymph node metastasis is the leading cause of poor prognosis in oral tongue squamous cell carcinoma and also occurs in the early stages. The current clinical diagnosis depends on a physical examination that is not enough to determine whether micrometastasis remains. The transcriptome profiling technique has shown great potential for predicting micrometastasis by capturing the dynamic activation state of genes. However, there are several technical challenges in using transcriptome data to model patient conditions: (1) An Insufficient number of samples compared to the number of genes, (2) Complex dependence between genes that govern the cancer phenotype, and (3) Heterogeneity between patients between cohorts that differ geographically and racially. We developed a computational framework to learn the subnetwork representation of the transcriptome to discover network biomarkers and determine the potential of metastasis in early oral tongue squamous cell carcinoma. Our method achieved high accuracy in predicting the potential of metastasis in two geographically and racially different groups of patients. The robustness of the model and the reproducibility of the discovered network biomarkers show great potential as a tool to diagnose lymph node metastasis in early oral cancer.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

acCRISPR: an activity-correction method for improving the accuracy of CRISPR screens

Abstract High throughput CRISPR screens are revolutionizing the way scientists unravel the genetic underpinnings of engineered and evolved phenotypes. One of the critical challenges in accurately assessing screening outcomes is accounting for the variability in sgRNA cutting efficiency. Poorly active guides targeting genes essential to screening conditions obscure the growth defects that are expected from disrupting them. Here, we develop acCRISPR, an end-to-end pipeline that identifies essential genes in pooled CRISPR screens using sgRNA read counts obtained from next-generation sequencing. acCRISPR uses experimentally determined cutting efficiencies for each guide in the library to provide an activity correction to the screening outcomes via calculation of an optimization metric, thus determining the fitness effect of disrupted genes. CRISPR-Cas9 and -Cas12a screens were carried out in the non-conventional oleaginous yeast Yarrowia lipolytica and acCRISPR was used to determine a high-confidence set of essential genes for growth under glucose, a common carbon source used for the industrial production of oleochemicals. acCRISPR was also used in screens quantifying relative cellular fitness under high salt conditions to identify genes that were related to salt tolerance. Collectively, this work presents an experimental-computational framework for CRISPR-based functional genomics studies that may be expanded to other non-conventional organisms of interest.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Regularization by denoising diffusion models for solving inverse PDE problems with application to full waveform inversion

Partial differential equation (PDE)-governed inverse problems are fundamental across various scientific and engineering applications; yet they face significant challenges due to nonlinearity, ill-posedness, and sensitivity to noise. Here, we introduce a computational framework, regularization by denoising using diffusion models for partial differential equations (RED-DiffEq), by integrating physics-driven inversion and data-driven learning. RED-DiffEq leverages pretrained diffusion models as a regularization mechanism for PDE-governed inverse problems. We apply RED-DiffEq to solve the full waveform inversion problem in geophysics, a challenging seismic imaging technique that seeks to reconstruct high-resolution subsurface velocity models from seismic measurement data. Our method shows enhanced accuracy and robustness compared to benchmark methods. Additionally, it exhibits strong generalization and domain decomposition capacity, enabling the inversion of more complex velocity models with larger domains than those used in training the diffusion model. Our framework can also be directly applied to diverse PDE-governed inverse problems.

Shan, Siming [Yale University, New Haven, CT (Unit↗

DOE OSTI2022

Scientific machine learning benchmarks

Deep learning has transformed the use of machine learning technologies for the analysis of large experimental datasets. In science, such datasets are typically generated by large-scale experimental facilities, and machine learning focuses on the identification of patterns, trends and anomalies to extract meaningful scientific insights from the data. In upcoming experimental facilities, such as the Extreme Photonics Application Centre (EPAC) in the UK or the international Square Kilometre Array (SKA), the rate of data generation and the scale of data volumes will increasingly require the use of more automated data analysis. Furthermore, at present, identifying the most appropriate machine learning algorithm for the analysis of any given scientific dataset is a challenge due to the potential applicability of many different machine learning frameworks, computer architectures and machine learning models. Historically, for modelling and simulation on high-performance computing systems, these issues have been addressed through benchmarking computer applications, algorithms and architectures. Extending such a benchmarking approach and identifying metrics for the application of machine learning methods to open, curated scientific datasets is a new challenge for both scientists and computer scientists. Here, we introduce the concept of machine learning benchmarks for science and review existing approaches. As an example, we describe the SciMLBench suite of scientific machine learning benchmarks.

42 ENGINEERING↗

DOE OSTI2020

Predicting metal–metal interactions. II. Accelerating generalized schemes through physical insights

Operando-computational frameworks that integrate descriptors for catalyst stability within catalyst screening paradigms enable predictions of rates and selectivity on chemically faithful representations of nanoparticles under reaction conditions. These catalyst stability descriptors can be efficiently predicted by density functional theory (DFT)-based models. The alloy stability model, for example, predicts the stability of metal atoms in nanoparticles with site-by-site resolution. In this work, we use physical insights to present accelerated approaches of parameterizing this recently introduced alloy-stability model. These accelerated approaches meld quadratic functions for the energy of metal atoms in terms of the coordination number with linear correlations between model parameters and the cohesive energies of bulk metals. By interpolating across both the coordination number and chemical space, these accelerated approaches shrink the training set size for 12 fcc p- and d-block metals from 204 to as few as 24 DFT calculated total energies without sacrificing the accuracy of our model. We validate the accelerated approaches by predicting adsorption energies of metal atoms on extended surfaces and 147 atom cuboctahedral nanoparticles with mean absolute errors of 0.10 eV and 0.24 eV, respectively. This efficiency boost will enable a rapid and exhaustive exploration of the vast material space of transition metal alloys for catalytic applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Less can be more: Insights on the role of electrode microstructure in redox flow batteries from two-dimensional direct numerical simulations

Understanding how to structure a porous electrode to facilitate fluid, mass, and charge transport is key to enhancing the performance of electrochemical devices, such as fuel cells, electrolyzers, and redox flow batteries (RFBs). Here, using a parallel computational framework, direct numerical simulations are carried out on idealized porous electrode microstructures for RFBs. Strategies to improve an electrode design starting from a regular lattice are explored. By introducing vacancies in the ordered arrangement, it is possible to achieve higher voltage efficiency at a given current density, thanks to improved mixing of reactive species, despite reducing the total reactive surface. Careful engineering of the location of vacancies, resulting in a density gradient, outperforms disordered configurations. Our simulation framework is a new tool to explore transport phenomena in RFBs, and our findings suggest new ways to design performant electrodes.

25 ENERGY STORAGE↗