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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Using Machine-Learning Methods and Expert Prediction Probabilities to Forecast Solar Flares

It has long been known that studying connection between solar flares and properties of magnetic field in active regions is very important for understanding the flare physics and developing space weather forecasts. The Helioseismic and Magnetic Imager onboard the Solar Dynamics Observatory (SDO/HMI) obtains tremendous amounts of magnetic field data products. However the operational NOAA Space Weather Prediction Center (SWPC) forecasts of solar flares still represent prediction probabilities issued by the experts. In this research we investigate the possibilities to enhance the daily operational flare forecasts performed at the SWPC by developing a synergy of the expert predictions and physics-based criteria, and by employing machine-learning methods. Among the physics-based criteria we consider the descriptors of the Polarity Inversion Line (PIL) and Space weather HMI Active Region Patches (SHARP), and derive from them daily characteristics of the entire Sun. We also consider the daily descriptors of the GOES Soft X-Ray (SXR) 1-8 Angstroms flux such as the flare history of the previous days and averaged X-Ray flux. We estimate the effectiveness in separation of flaring and non-flaring cases for each characteristic, as well as for the expert prediction probabilities, and find that some PIL, SHARP and SXR descriptors are as effective as the expert prediction probabilities and should be considered to issue the flare forecast. Finally, we train and test several Machine-Learning classification algorithms (Support Vector Classifiers with various kernel functions, k-Nearest Neighbor Classifier, Random Forest Classifier, and Neural Networks) using the most effective descriptors and expert prediction probabilities, and compare the obtained predictions with the current SWPC forecasts.

Machine-Learning↗
DOE OSTI2025

Reward Driven Workflows for Unsupervised Explainable Analysis of Phases and Ferroic Variants From Atomically Resolved Imaging Data

Rapid progress in aberration corrected electron microscopy necessitates development of robust methods for the identification of phases, ferroic variants, and other pertinent aspects of materials structure from imaging data. While unsupervised methods for clustering and classification are widely used for these tasks, their performance can be sensitive to hyperparameter selection in the analysis workflow. In this study, the effects of descriptors and hyperparameters are explored on the capability of unsupervised ML methods to distill local structural information, exemplified by the discovery of polarization and lattice distortion in Sm − dopped BiFeO 3 (BFO) thin films. It is demonstrated that a reward-driven approach can be used to optimize these key hyperparameters across the full workflow, where rewards are designed to reflect domain wall continuity and straightness, ensuring that the analysis aligns with the material's physical behavior. This approach allows the discovery of local descriptors that are best aligned with the specific physical behavior, providing insight into the fundamental physics of materials. The reward driven workflow is further extended to disentangle structural factors of variation via an optimized variational autoencoder (VAE). Lastly, the importance of well-defined rewards is explored as a quantifiable measure of the success of the workflow.

Barakati, Kamyar [University of Tennessee, Knoxvil↗
DOE OSTI2023

Automated descriptor selection, volcano curve generation, and active site determination using the DescMAP software

The material space for catalyst discovery is expansive. Volcano curves are traditionally employed to provide physical insights into optimal catalyst characteristics for new material selection. Their generation lies on a single descriptor picked using expert knowledge. Here we present DescMAP, a Python-based software, to automate the selection of descriptors, the generation of volcano maps, and the identification of active sites for structure-sensitive reactions. Here, we consider traditional energy-based and geometric descriptors for structure-sensitive reactions. DescMAP is integrated with the Virtual Kinetic Laboratory (VLab) to provide multiple functionalities. It inputs spreadsheets or template files for flexibility and outputs interactive graphs for post-processing. We demonstrate its features using the non-oxidative dehydrogenation of ethane to ethylene over (111) closed-packed surfaces and the methane total oxidation over various Pt facets. It can be easily applied to other complex chemistries and achieves quick screening of potential catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Open-source generation of sigma profiles: impact of quantum chemistry and solvation treatment on machine learning performance

The combination of machine learning (ML) models with chemistry-related tasks requires the description of molecular structures in a machine-readable way. The nature of these so-called molecular descriptors has a direct and major impact on the performance of ML models and remains an open problem in the field. Structural descriptors like SMILES strings or molecular graphs lack size-independence and can be memory intensive. Machine-learned descriptors can be of low dimensionality and constant size but lack physical significance and human interpretability. Sigma profiles, which are unnormalized histograms of the surface charge distributions of solvated molecules, combine physical significance with low dimensionality and size-independence, making them a suitable candidate for a universal molecular descriptor. However, their widespread adoption in ML applications requires open access to sigma profile generation, which is currently not available. This work details the development of OpenSPGen – an open-source tool for generating sigma profiles. Also presented are studies on the effect of different settings on the efficacy of the generated sigma profiles at predicting thermophysical material properties when used as inputs to a Gaussian process as a simple surrogate ML model. We find that a higher level of theory does not translate to more accurate results. We also provide further recommendations for sigma profile calculation and use in ML models.

Salih, Fathya Y. M. [University of Notre Dame, IN ↗
NASA NTRS1998

Track Structure Model for Radial Distributions of Electron Spectra and Event Spectra from High-Energy Ions

An analytic method is described for evaluating the average radial electron spectrum and the radial and total frequency-event spectrum for high-energy ions. For high-energy ions, indirect events make important contributions to frequency-event spectra. The method used for evaluating indirect events is to fold the radial electron spectrum with measured frequency-event spectrum for photons or electrons. The contribution from direct events is treated using a spatially restricted linear energy transfer (LET). We find that high-energy heavy ions have a significantly reduced frequency-averaged final energy (yF) compared to LET, while relativistic protons have a significantly increased yF and dose-averaged lineal energy (yD) for typical site sizes used in tissue equivalent proportional counters. Such differences represent important factors in evaluating event spectra with laboratory beams, in space- flight, or in atmospheric radiation studies and in validation of radiation transport codes. The inadequacy of LET as descriptor because of deviations in values of physical quantities, such as track width, secondary electron spectrum, and yD for ions of identical LET is also discussed.

Cucinotta, F. A.↗
DOE OSTI2023

Atomic-scale modeling of C/N kinetic stability descriptors for PGM-free electrocatalysts at finite temperatures

The durability of platinum group metal-free (PGM-free) electrocatalysts is a major barrier to their usage in polymer electrolyte fuel cell cathodes. C and N removal from active sites may play an important role in the catalyst’s ability to maintain high activity. While C degradation mechanisms are kinetically controlled, previous studies have focused on thermodynamic descriptors. In this work, we develop a temperature-dependent kinetic descriptor of C and N stability using an electron beam-damage model. Our approach considers the electron beam energy threshold (EBET) describing the knock-on displacement of C and N atoms as a stability descriptor for atomic structures. The stability of different sites is calculated to be different showing this approach can discriminate between similar sites with varied configurations. Additionally, we provide important insight regarding TEM beam damage of proposed active sites. We calculate 60 keV electrons can damage some proposed active site structures even at room temperature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Cu Based Dilute Alloys for Tuning the C 2+ Selectivity of Electrochemical CO 2 Reduction

Electrochemical CO 2 reduction is a promising technology for replacing fossil fuel feedstocks in the chemical industry but further improvements in catalyst selectivity need to be made. So far, only copper-based catalysts have shown efficient conversion of CO 2 into the desired multi-carbon (C 2+ ) products. This work explores Cu-based dilute alloys to systematically tune the energy landscape of CO 2 electrolysis toward C 2+ products. Selection of the dilute alloy components is guided by grand canonical density functional theory simulations using the calculated binding energies of the reaction intermediates CO*, CHO*, and OCCO* dimer as descriptors for the selectivity toward C 2+ products. Here, a physical vapor deposition catalyst testing platform is employed to isolate the effect of alloy composition on the C 2+ /C 1 product branching ratio without interference from catalyst morphology or catalyst integration. Six dilute alloy catalysts are prepared and tested with respect to their C 2+ /C 1 product ratio using different electrolyzer environments including selected tests in a 100-cm 2 electrolyzer. Consistent with theory, CuAl, CuB, CuGa and especially CuSc show increased selectivity toward C 2+ products by making CO dimerization energetically more favorable on the dominant Cu facets, demonstrating the power of using the dilute alloy approach to tune the selectivity of CO 2 electrolysis.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Non-bonding interaction of dual atom catalysts for enhanced oxygen reduction reaction

Here, we demonstrate the design of graphene-supported dual atom catalysts (DACs) for the four-electron oxygen reduction reaction (ORR), by utilizing the non-bonding interaction of counterpart metals (M) that synergistically tune the electronic properties and catalytic activity of the Fe active site in FeMN 6 -DAC and FeMN 8 -DAC systems, where M stands for Fe, Co, Ni, Cu, and Zn. More specifically, for Fe-M distances below 15Å, the non-bonding interaction is significant, making the system act as the DAC. We predicted that FeNiN 6 -DAC and FeNiN 8 -DAC exhibit a low ORR overpotential (η ORR ) of 0.28 V and 0.47 V, respectively, which are at the summits of volcano plots. This low η ORR originates from the high Bader charge transfer coupled with high spin density at the Fe site in both the FeNiN 6 -DAC and FeNiN 8 -DAC systems, which weakens the adsorption of OH* intermediate while enhancing its desorption to H 2 O. Guided by these density functional theory (DFT) computational results, we synthesized FeCoN 8 -DAC and FeNiN 8 -DAC along with N-doped graphene and confirmed their structures with scanning transmission electron microscopy (STEM), X-ray photoelectron spectroscopy (XPS), X-ray absorption near-edge structure (XANES), extended X-ray absorption fine structure (EXAFS), and electron spin resonance (ESR). We verify experimentally the catalytic activities and find that FeNiN 8 -DAC has the low experimental overpotential of 0.39 V with a Tafel slope of 47 mVdec -1 . Based on these results, we propose a DFT-guided strategy to tune the charge transfer and spin population of the active site toward designing DACs for electrochemical ORR.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Atomic-scale identification of active sites of oxygen reduction nanocatalysts

Heterogeneous nanocatalysts play a crucial role in both the chemical and energy industries. Despite substantial advancements in theoretical, computational and experimental studies, identifying their active sites remains a major challenge. Here we utilize atomic electron tomography to determine the three-dimensional atomic structure of PtNi and Mo-doped PtNi nanocatalysts for the electrochemical oxygen reduction reaction. We then employ the experimental atomic structures as input to first-principles-trained machine learning to identify the active sites of the nanocatalysts. Through the analysis of the structure–activity relationships, we formulate an equation termed the local environment descriptor, which balances the strain and ligand effects to provide physical and chemical insights into active sites in the oxygen reduction reaction. The ability to determine the three-dimensional atomic structure and chemical composition of realistic nanoparticles, combined with machine learning, could transform our fundamental understanding of the active sites of catalysts and guide the rational design of optimal nanocatalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Automated Experiment in 4D-STEM: Exploring Emergent Physics and Structural Behaviors

Automated experiments in 4D scanning transmission electron microscopy (STEM) are implemented for rapid discovery of local structures, symmetry-breaking distortions, and internal electric and magnetic fields in complex materials. Deep kernel learning enables active learning of the relationship between local structure and 4D-STEM-based descriptors. With this, efficient and “intelligent” probing of dissimilar structural elements to discover desired physical functionality is made possible. This approach allows effective navigation of the sample in an automated fashion guided by either a predetermined physical phenomenon, such as strongest electric field magnitude, or in an exploratory fashion. Therefore, we verify the approach first on preacquired 4D-STEM data and further implement it experimentally on an operational STEM. The experimental discovery workflow is demonstrated using graphene and subsequently extended toward a lesser-known layered 2D van der Waals material, MnPS 3 . This approach establishes a pathway for physics-driven automated 4D-STEM experiments that enable probing the physics of strongly correlated systems and quantum materials and devices, as well as exploration of beam-sensitive materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Prediction of O and OH Adsorption on Transition Metal Oxide Surfaces from Bulk Descriptors

In the search for stable and active catalysts, density functional theory and machine learning (ML) based models can accelerate the screening of materials. While stability is conveniently addressed on the bulk level of computation, the modelling of catalytic activity requires expensive surface simulations. Here, in this work, we develop models for the surface adsorption energy of O and OH intermediates across a consistent and extensive dataset of pure transition metal oxide surfaces. We show that adsorption energies across metal oxidation states of +2 to +6 are well captured from the metal-oxygen bond strength extracted from the bulk level calculation. Specifically, we calculate the integrated crystal orbital Hamiltonian population (ICOHP) of the metal-oxygen bond in the bulk oxide and employ a simple normalization scheme to obtain a strong correlation with adsorption energetics. By combining our ICOHP descriptor with non DFT features in a Gaussian Process regression (GPR) model, we achieve high model accuracy with mean absolute errors of 0.166 and 0.219 eV for OH and O adsorption, respectively. By targeting the O-OH adsorption energy difference with our GPR model, we predict the the oxygen evolution reaction (OER) activity from bulk descriptors only. Furthermore, we utilize the strong correlation between the COHP and metal oxygen bond lengths to rapidly predict adsorption energetics and catalytic activity from the optimized bulk geometry. Our approach can enable an efficient search for active catalysts by eliminating the need for surface calculations in the initial screening phase.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Modern chemical graph theory

Abstract Graph theory has a long history in chemistry. Yet as the breadth and variety of chemical data is rapidly changing, so too do graph encoding methods and analyses that yield qualitative and quantitative insights. Using illustrative cases within a basic mathematical framework, we showcase modern chemical graph theory's utility in Chemists' analysis and model development toolkit. The encoding of both experimental and simulation data is discussed at various levels of granularity of information. This is followed by a discussion of the two major classes of graph theoretical analyses: identifying connectivity patterns and partitioning methods. Measures, metrics, descriptors, and topological indices are then introduced with an emphasis upon enhancing interpretability and incorporation into physical models. Challenging data cases are described that include strategies for studying time dependence. Throughout, we incorporate recent advancements in computer science and applied mathematics that are propelling chemical graph theory into new domains of chemical study. This article is categorized under: Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods Structure and Mechanism > Computational Materials Science Structure and Mechanism > Molecular Structures

Leite, Leonardo S. G.↗
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 OSTI2025

Deciphering coulombic loss in lithium-ion batteries and beyond

Lithium-ion batteries are pivotal for modern energy storage, yet accurately predicting their lifespan remains a critical challenge. While descriptors like coulombic efficiency are widely used to assess battery longevity, the unclear physical origins of coulombic losses cause semi-quantitative correlation with capacity, complicating battery development. Here, we combine high-precision leakage current and open-circuit-voltage measurements with charge conservation principles to explore microscopic charge consumptions at electrode-electrolyte interfaces across diverse chemistries. We demonstrate that coulombic loss arises from a synergy between local charge neutrality and global charge compensation, reconciling its quantitative correlation to capacity. Contrary to conventional assumptions equating coulombic loss with irreversible capacity loss, this framework resolves systematic overestimations and paradoxical phenomena in existing chemistries. Our findings establish physics-informed criteria for accelerated lifespan evaluation and guide rational design of long-life lithium-ion batteries and beyond.

Batteries↗
DOE OSTI2024

Data Science Enabled Enabled Discovery of Superconductors (Final Progress Report)

This Final Technical Report describes efforts by 4 PIs at the University of Florida (Peter Hirschfeld, Richard Hennig, Greg Stewart and James Hamlin), over the period September 2019-August 2023, to use data science and machine learning techniques to discover new conventional superconductors. The PIs constructed a discovery loop with two theorists and two experimentalists to: develop algorithms to machine learn descriptors correlating strongly with the critical temperature Tc (PI's Peter Hirschfeld, UF Physics and Richard Hennig, UF Materials Science and En), synthesize and measure properties of promising materials, and feed back the knowledge gained into the prediction algorithm. This work was motivated by the theoretical prediction and experimental discovery of high-pressure, high-pressure hydride superconductors, and to find ways to recreate the high critical temperatures in these systems at ambient pressure. Highlights from the grant include: 1) a new equation for Tc in terms of moments of the electron-phonon spectral function, improving on the so-called Allen-Dynes equation (1975); 2) study of the metastable A15 superconductor Nb3Si, formed under explosive compression at ~1000GPa to determine the kinetic barrier to the ground state structure; 3) the development of ultra-fast machine-learned atomic potentials for molecular dynamics, and 4) the discovery of superconductivity at 19K in WB2 arising from metastable defect structures in the crystal.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Interpretable Deep Learning for Advancing Field-Enhanced Catalysis

This DOE Early Career project developed a physics-informed, interpretable AI-and-modeling framework to understand and exploit electric-field effects in heterogeneous catalysis, with ammonia cracking and synthesis as a representative pathway. The team built and validated methods to map local electric fields on metal surfaces and nanoparticles, showing that low-coordination features (tips/edges/corners) can concentrate fields by several-fold relative to flat facets. Using DFT-generated datasets, the project created physics-guided machine learning models that rapidly predict local electric fields and field-dependent adsorption energetics with near-DFT accuracy while reducing computational cost by orders of magnitude. These predictions were integrated with microkinetic modeling to quantify how field-dipole interactions reshape reaction energetics and mechanisms, enabling large increases in predicted catalytic rates and substantial reductions in operating temperature under favorable field conditions. To accelerate discovery of earth-abundant catalysts, the project combined interpretable ML screening (with electronic-structure descriptors identified as key drivers) with a generative inverse-design workflow based on diffusion models and physics constraints. The resulting closed-loop approach, linking simulation, mechanistic modeling, and AI, provides reusable tools and datasets for designing catalysts and operating conditions in field-enhanced catalysis, with broad relevance to electrostatic catalysis, plasma catalysis, electrocatalysis, and other energy-related chemical transformations.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

ppdx : Automated modeling of protein–protein interaction descriptors for use with machine learning

This paper describes ppdx, a python workflow tool that combines protein sequence alignment, homology modeling, and structural refinement, to compute a broad array of descriptors for characterizing protein–protein interactions. The descriptors can be used to predict various properties of interest, such as protein–protein binding affinities, or inhibitory concentrations (IC 50 ), using approaches that range from simple regression to more complex machine learning models. The software is highly modular. It supports different protocols for generating structures, and 95 descriptors can be currently computed. More protocols and descriptors can be easily added. The implementation is highly parallel and can fully exploit the available cores in a single workstation, or multiple nodes on a supercomputer, allowing many systems to be analyzed simultaneously. As an illustrative application, ppdx is used to parametrize a model that predicts the IC 50 of a set of antigens and a class of antibodies directed to the influenza hemagglutinin stalk.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Computational descriptor for electrochemical currents of carbon dioxide reduction on Cu facets

Computation screening is crucial for designing efficient electrochemical catalysts for carbon dioxide (CO 2 R) reduction that produce valuable hydrocarbons and oxygenates. In this work, leveraging density functional theory calculations for the CO adsorption energy ΔE CO on seventeen Cu terminations, we discover a strong linear correlation between ΔE CO and the experimentally measured CO 2 R electrochemical currents (ACS Catal. 2022, 12, 11, 6578–6588). Examining ab initio thermodynamics of early critical intermediates CO*, COH*, and CHO*, we find that CO* → CHO* is the thermodynamically controlling step. Beyond the general CO adsorption energy that only shows a linear trend with CO 2 R activity, we show that the reaction free energy of CO* → CHO* is the descriptor for the overall CO 2 R activity for Cu facets, as it displays a volcano relationship with the experimental current. Importantly, we show that high step and kink density of the Cu terminations not only enhances CO adsorption strength but also modulates the CO* → CHO* pathway, as respectively exemplified in the (941) and (741) facets. In addition, we explain that the high activity of (741) is due to its relatively low hydrogen evolution reaction activity compared with the other Cu surfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗