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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Operando XAS and DFT Uncover Structure-Performance Relationships in Re/TiO 2 for Selective CO 2 Hydrogenation to Methanol

The conversion of CO 2 into value-added chemicals, such as methanol, offers a promising pathway toward a renewable energy future. However, a precise kinetic control and a highly selective catalyst are necessary to overcome the thermodynamic preference for CO 2 hydrogenation to methane. Rhenium-based catalysts, particularly Re/TiO 2 , demonstrate high activity and selectivity for methanol under high-pressure conditions. For example, at 100 bar and 200 °C, a methanol selectivity of 97−99% was obtained. Catalysts with 1 wt % Re and 5 wt % Re/ TiO 2 were used to study the effect of cluster sizes. At 250 °C, the 1 wt % catalyst achieves 97% selectivity at 23% conversion, whereas 5 wt % Re/TiO 2 achieves 74% selectivity at 40% conversion, corresponding to a drop in space-time yield from 65 to 16 g CH 3 OH ·g Re −1 ·h −1 , respectively. X-ray absorption spectroscopy provided insights into the structure of the active sites, while density functional theory calculations revealed the effects of cluster size on the energy barriers for H 2 activation, CH 3 OH dissociation, and CH 3 OH desorption, all of which directly influence conversion and selectivity. These results underscore the importance of balancing cluster size for optimal catalyst performance and provide insights into the design of efficient and selective catalysts for renewable methanol production.

XAS↗
DOE OSTI2021

Range-separated hybrid functionals for mixed dimensional heterojunctions: Application to phthalocyanines/MoS 2

We analyze the electronic structure and level alignment of transition-metal phthalocyanine (MPc) molecules adsorbed on two-dimensional MoS 2 employing density functional theory (DFT) calculations. We develop a procedure for multi-objective optimal tuning of parameters of range-separated hybrid functionals in these mixed-dimensional systems. Using this procedure, which leads to the asymptotically-correct exchange-correlation potential between molecule and two-dimensional material, we obtain electronic structures consistent with experimental photoemission results for both energy level alignment and electronic bandgaps, representing a significant advance compared to standard DFT methods. We elucidate the MoS 2 valence resonance with the transition-metal phthalocyanine non-frontier 3d orbitals and its dependence on the transition metal atomic number. Based on our calculations, we derive parameter-free, model self-energy corrections that quantitatively accounts for the effects of the heterogeneous dielectric environment on the electronic structure of these mixed-dimensional heterojunctions.

2D materials↗
DOE OSTI2020

Evaluation of Amorphous Oxide Coatings for High-Voltage Li-Ion Battery Applications Using a First-Principles Framework

Cathode surface coatings are widely used industrially as a means to suppress degradation and improve electrochemical performance of lithium-ion batteries. However, developing an optimal coating is challenging, as different coating materials may enhance one aspect of performance while hindering another. Furthermore, to elucidate the fundamental thermodynamic and transport properties of amorphous cathode coating materials, here, we present a framework for calculating and analyzing the Li + and O 2- transport and the stability against delithiation in such materials. Our framework includes systematic workflows of ab-initio molecular dynamics calculations to obtain amorphous structures and diffusion trajectories coupled with an analysis of critical changes of the active-ion local environment during diffusion. Based on these data, we provide an estimate of room-temperature diffusivities, including statistical error bars, and the evaluation of the coating suitability in terms of its ability to facilitate Li + transport while blocking O 2- transport. Finally, we add the thermodynamic stability analysis of the coating chemistry within the operating voltage of common Li-ion cathodes. We apply this framework to two commonly used amorphous coating materials, Al 2 O 3 and ZnO. We find that (1) in general, a higher Li + content increases both Li + and O 2- diffusivities in both Al 2 O 3 and ZnO. Also, Li + and O 2- diffuse much faster in ZnO than in Al 2 O 3 . (2) However, neither Al 2 O 3 nor ZnO is expected to retain a significant concentration of Li + at high charge. (3) ZnO performs much more poorly in terms of O 2- blocking, and hence, Al 2 O 3 is preferred for high-voltage cathode applications. These results will help to quantitatively evaluate amorphous materials, such as metal oxides and fluorides, for different performance metrics and facilitate the development of optimal cathode coatings.

25 ENERGY STORAGE↗
DOE OSTI2021

Deep Learning Coordinate-Free Quantum Chemistry

Computing quantum chemical properties of small molecules and polymers can provide insights valuable to physicists, chemists and biologists when designing new materials, catalysts, biological probes and drugs. Deep learning can compute quantum chemical properties accurately in a fraction of the time required by commonly used methods such as density functional theory (DFT). However, many of these deep learning architectures require energy minimized molecular geometries as input, which is also computationally expensive, and decreasing the reproducibility and throughput of these methods. In this study, we demonstrate that accurate quantum chemical computations can be performed without optimized geometries by operating in the coordinate-free domain using deep learning on graph encodings. Furthermore, we also find that the choice of graph-encoding architecture substantially affects the performance of these methods. The Wave architecture outperforms graph convolution architectures, particularly on complex molecules. Furthermore, the structures of these graph encoding architectures provide an opportunity to probe an important, outstanding question in quantum mechanics: What types of quantum chemical properties can be represented by local-variable models? We find that Wave, a local-variable model, is more accurately calculates quantum chemical properties. Graph convolutional architectures require global variables, and are not as effective as as Wave. We anticipate that coordinate-free, deep-learning models of quantum chemistry will become valuable tools in chemistry and biology, enabling researchers to rapidly screen chemical databases or identify new molecules using automated, de-novo design algorithms.

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

Screening binary alloys for electrochemical CO 2 reduction towards multi-carbon products

Electrochemical reduction of CO 2 (eCO 2 R) to high-value chemicals presents an attractive approach for utilizing CO 2 . Copper (Cu) is presently the only electrocatalyst that fulfills this purpose with notable activity, but selectivity remains a problem. To identify catalysts for eCO 2 R with high selectivity towards multicarbon (C 2(+) ) products, we explore binary systems composed of strongly and weakly CO binding metals alloyed with Cu, Fe, Co, Ni, and Pd. A total number of 142 alloys with two commonly studied configurations, L1 2 and L1 0 , are simulated with density functional theory (DFT). We leverage recent progress in the atomistic understanding of the eCO 2 R mechanism and use the binding energies of CO* and C* as descriptors when screening for C 2(+) selectivity. We evaluate the stability of the binary alloys by analyzing the formation energy of the clean alloy surfaces. Our theoretical screening identifies about 16 Cu-based alloys and 18 non-Cu based alloys with optimal C 2(+) selective properties for eCO 2 R. For the non-Cu based binary alloys, the p-block elements play an important role in tuning the C* and CO* adsorption energies. In terms of stability, most of the Cu-based systems alloyed with metals that exhibit strong CO* binding are unstable. Ni-based alloys are more stable than the Co-based alloys followed by the Fe-based alloys, and all the Pd-based alloys are stable. In general, the L1 0 structural Fe, Co, Ni, and Pd-based alloys are more stable than the corresponding L1 2 alloys. Our approach identifies materials known to have good C 2(+) selectivity, but it also proposes several other promising materials that have not previously been tested for eCO 2 R.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Analysis and prediction of reaction kinetics using the degree of rate control

“Degree of rate control” (DRC) analysis provides a quantitative approach for analysing the kinetics of multi-step reaction mechanisms that has been widely applied to both heterogeneous and homogeneous catalysis research, as well as electrocatalysis. The DRC of any given transition state or intermediate is defined as a partial derivative such that it approximately equals the fractional increase in net rate to the product of interest per differential decrease in its standard-state free energy for that species (÷RT), holding constant the standard-state free energies of all other transition states and intermediates. Even very complex mechanisms usually have only a few species with non-zero DRCs and are thus the species whose interactions with the catalyst most strongly affect the net rate. These key DRC values thus offer a simple and intuitive route to optimize catalyst materials, especially with the assistance of computational methods like density functional theory (DFT). These high-DRC species are also the species whose energetics must be most accurately measured or calculated to achieve an accurate kinetic model for any reaction mechanism. In simple cases with a single “rate-determining step”, the DRC for its transition state (TS) is + 1. Catalyst-bound intermediates, on the other hand, often have negative DRCs equal to a small integer times their fractional occupancy of catalyst sites. The apparent activation energy equals a weighted average of the standard-state enthalpies (relative to reactants) of all the species (intermediates, transition states and products) in the reaction mechanism, each weighted by its DRC (+RT). It has been shown that the apparent transfer coefficient in electrocatalysis, an inverted form of the Tafel slope, is a weighted average of the number of electrons transferred to generate each intermediate or product species in the mechanism, weighted again by the DRC. Quantitative analysis of kinetic isotope effects (KIEs, or the ratio of net rates for different reactant isotopes) in complex mechanisms has shown that the logarithm of the KIE equals the weighted average over all species in the mechanism of the difference between the two isotopes in their standard-state free energies (÷RT), again weighted by the DRC. The reaction orders with respect to fluid-phase concentrations of reactants, products and intermediates have also been proven to be directly related to DRCs. Thus, there are numerous experimental observables which equate to short linear combinations of DRCs, so that combinations of experimental measurements might provide access to DRC values. Since its invention for transition states in 1994 and its generalization to include intermediates in 2009, the DRC has thus far mainly been calculated for microkinetic models based either entirely on DFT or on DFT with the key energies (i.e., those for high-DRC species) being fine-tuned to match experiments. The relationships summarized in this work provide new opportunities for using experiments earlier in the development and optimization of microkinetic models that require input from computational catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Thermodynamic evaluation and modeling of the CeF 3 -LaF 3 -LiF-NaF system

The thermodynamic properties of the LiF-NaF-CeF 3 -LaF 3 pseudo-quaternary system have been evaluated, and models for the relevant phases have been revised or newly developed. The solid solubility of NaF in LaF 3 and CeF 3 was described in the model based on reported phase-equilibria observations and results from density functional theory calculations. The Gibbs energy functions for the NaCeF 4 and NaLaF 4 phases have been updated with recently reported thermochemical measurements. The Gibbs energy functions and model relations for the LiF-NaF-LaF 3 and LiF-NaF-CeF 3 pseudo-ternary systems were successfully optimized based on thermochemical properties and phase equilibria observations.

CALPHAD↗
DOE OSTI2022

Universal machine learning framework for defect predictions in zinc blende semiconductors

Our article introduces a universal predictive framework for point defect formation energies and charge transition levels in a wide chemical space of zinc blende semiconductors and possible impurity atoms selected from across the periodic table. This framework was developed by leveraging high-throughput quantum mechanical simulations benchmarked using some experimental data from the literature, as well as machine learning (ML)-based regressions techniques that map unique materials descriptors to computed defect properties and yield optimized and generalizable models. Furthermore, the power and utility of these models is revealed through quick predictions for thousands of new defects and screening of low-energy impurities, which may tune the equilibrium conductivity in the semiconductor. This work presents, to our knowledge, the largest density functional theory (DFT) dataset of defect properties in semiconductors and the largest DFT+ML-based screening of point defects in semiconductors to date.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Phase Diagrams of Alloys and Their Hydrides via On-Lattice Graph Neural Networks and Limited Training Data

Efficient prediction of sampling-intensive thermodynamic properties is needed to evaluate material performance and permit high-throughput materials modeling for a diverse array of technology applications. To alleviate the prohibitive computational expense of high-throughput configurational sampling with density functional theory (DFT), surrogate modeling strategies like cluster expansion are many orders of magnitude more efficient but can be difficult to construct in systems with high compositional complexity. We therefore employ minimal-complexity graph neural network models that accurately predict and can even extrapolate to out-of-train distribution formation energies of DFT-relaxed structures from an ideal (unrelaxed) crystallographic representation. This enables the large-scale sampling necessary for various thermodynamic property predictions that may otherwise be intractable and can be achieved with small training data sets. Two exemplars, optimizing the thermodynamic stability of low-density high-entropy alloys and modulating the plateau pressure of hydrogen in metal alloys, demonstrate the power of this approach, which can be extended to a variety of materials discovery and modeling problems.

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

SOMAS: a platform for data-driven material discovery in redox flow battery development

Abstract Aqueous organic redox flow batteries offer an environmentally benign, tunable, and safe route to large-scale energy storage. The energy density is one of the key performance parameters of organic redox flow batteries, which critically depends on the solubility of the redox-active molecule in water. Prediction of aqueous solubility remains a challenge in chemistry. Recently, machine learning models have been developed for molecular properties prediction in chemistry and material science. The fidelity of a machine learning model critically depends on the diversity, accuracy, and abundancy of the training datasets. We build a comprehensive open access organic molecular database “Solubility of Organic Molecules in Aqueous Solution” (SOMAS) containing about 12,000 molecules that covers wider chemical and solubility regimes suitable for aqueous organic redox flow battery development efforts. In addition to experimental solubility, we also provide eight distinctive quantum descriptors including optimized geometry derived from high-throughput density functional theory calculations along with six molecular descriptors for each molecule. SOMAS builds a critical foundation for future efforts in artificial intelligence-based solubility prediction models.

25 ENERGY STORAGE↗
DOE OSTI2022

Near-Quantitative Predictions of the First-Shell Coordination Structure of Hydrated First-Row Transition Metal Ions Using K-Edge X-ray Absorption Near-Edge Spectroscopy

Understanding the solvation structure of transition metal ions is important due to its broad impact on the kinetics, stability, and reactivity in a variety of applications in geochemistry, biochemistry, energy storage, and environmental chemistry. Using water as the common ligand, we study the X-ray absorption pre-edge and near-edge spectra at the K-edge of a near-complete series of hydrated first-row transition metal ions with d-orbital occupancy ranging from d 2 to d 10 . Starting with optimized structures that were derived from an explicit solvation treatment at the density functional theory (DFT) level, we then compute the pre-edge X-ray absorption spectra at the metal ion K-edge at the time dependent density functional theory (TDDFT) and restricted active-space second-order perturbation theory (RASPT2) levels of theory. TDDFT calculations provide accurate results for spectra that are dominated by single excitations, while significant improvements were obtained with RASPT2 calculations that correctly distinguish between singly and doubly excited states, where relevant, with quantitative accuracy compared with experiment. We analyze and assign the dierent pre-edge features for each metal ion in order to reveal the impact of the variations in the d orbital occupancy on the rst-shell coordination environment. For completeness, we also report the lowest energy ligand-field d-d transitions for all the transition metal aqua ions considered in this study using complete active space second order perturbation theory (CASPT2).

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

Strain-Induced asymmetry and on-site dynamics of silicon defects in graphene

In the last decade, the atomically-focused electron beams utilized in scanning transmission electron microscopes (STEMs) have been shown to induce a broad set of local structural transformations in materials, opening pathways for directing material synthesis and modification atom-by-atom. The mechanisms underlying these transformations remain largely unknown, due to the intractability of modeling the myriad of reaction pathways that can be accessed through high-energy electron scattering. The information on materials’ structure and dynamics that can be extracted from STEM images is similarly left underexplored. Here, we report the observation of anomalous on-site dynamics of individual silicon impurity atoms in graphene during STEM imaging. Density functional theory-based structural optimizations of anisotropically-strained molecular nanographenes reveal two distinct (but nearly degenerate) stable structures for four-fold coordinated silicon impurities, where interconversion between the two structures manifests slight changes of the silicon position within the lattice site. Implications for defect-based strain engineering in graphene are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Exploring Structure-Sensitive Relations for Small Species Adsorption Using Machine Learning

Accurate prediction of adsorption energies on heterogeneous catalyst surfaces is crucial to predicting reactivity and screening materials. Adsorption linear scaling relations have been developed extensively but often lack accuracy and apply to one adsorbate and a single binding site type at a time. These facts undermine their ability to predict structure sensitivity and optimal catalyst structure. Using machine learning on nearly 300 density functional theory calculations, we demonstrate that generalized coordination number scaling relations hold well for oxygen- and high-valency carbon-binding species but fail for others. Here we reveal that the valency and the electronic coupling of a species with the surface, along with the site type and its coordination environment, are critical for small species adsorption. The model simultaneously predicts the adsorption energy and preferred site and significantly outperforms linear scalings in accuracy. It can expose the structure sensitivity of chemical reactions and enable enhanced catalyst activity via engineering particle shape and facet defects. The generality of our methodology is validated by training the model with transition metal data and transferring it to predict adsorption energies on single-atom alloys.

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

Site-specific surface reactivity on MgO for atomic layer deposition via selective hydration

Atomic layer deposition (ALD) is a powerful technique for thin film synthesis, offering atomic-scale precision and conformality. While ALD of MgO has been widely studied for applications in energy storage and microelectronics, its potential as surface on which deposition may be selective and defects repaired remains underexplored. Here, we present a combined theoretical and experimental investigation of MgO surface hydration and its implications for targeted ALD growth using water and dimethyl aluminum isopropoxide (DMAI) as reactants. We perform density functional theory (DFT) calculations to examine molecular and dissociative H 2 O adsorption on MgO (100) terraces and step-edge sites, including pristine surfaces and those with Mg/O vacancies. Reaction Gibbs free energies are calculated under various conditions to quantify surface reactivity. Our findings reveal facet- and defect-dependent hydration behaviors that align with experimental ALD growth trends on MgO (100). This study provides a molecular-level understanding of MgO surface chemistry critical for optimizing ALD processes for thin film growth and defect repair.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Mechanistic insight into the effects of A-site doping in perovskite nanorods on interfacial Li-ion and Na-ion transport in polymer–perovskite composite electrolytes

Composite polymer electrolytes (CPEs) offer a promising pathway to safer and higher energy density electrochemical energy storage yet optimizing interfacial ion transport remains a critical challenge due to high resistances and a lack of understanding of the complex interfacial electrostatic interactions. This work investigates the mechanistic origins of the interfacial ion transport enhancement in single-ion conducting (SIC) polymer electrolytes filled with A-site disordered perovskite LaxMyTiOz (LMTO) (M = Li, Na, K) nanorods. By integrating Density Functional Theory (DFT) with broadband dielectric spectroscopy (BDS) and pulsed-field-gradient NMR (PFG-NMR), we analyze the ion transport behavior in Li-based and Na-based SIC systems containing compositionally distinct LMTO fillers (LMTO800 and LMTO900). Specifically, we introduce the site-to-site energy difference (ΔE) descriptor, which extends beyond single-site descriptors such as adsorption energy and ion binding energies. We demonstrate that ΔE together with ion binding energies provide a more comprehensive picture of the energy landscape at the polymer–ceramic interface. The combined DFT and experimental results show that A-site doping in the ceramic filler can effectively modulate ΔE and ion binding energies, creating a favorable potential energy landscape that promotes enhanced ion transport along the interface. This comprehensive study illustrates the impact of ceramic compositional tuning on interfacial ion transport and provides materials design rules towards real-world applications.

B shepard, Lauren [Pennsylvania State University]↗
DOE OSTI2025

Learning error distribution kernel‐enhanced neural network methodology for multi‐intersection signal control optimization

Traffic congestion has substantially induced significant mobility and energy inefficiency. Many research challenges are identified in traffic signal control and management associated with artificial intelligence (AI)-based models. For example, developing AI-driven dynamic traffic system models that accurately capture high-resolution traffic attributes and formulate robust control algorithms for traffic signal optimization is difficult. Additionally, uncertainties in traffic system modeling and control processes can further complicate traffic signal system controllability. To partially address these challenges, this study presents a novel, hybrid neural network model enhanced with a probability density function kernel shaping technique to formulate traffic system dynamics better and improve comprehensive traffic network modeling and control. The numerical experimental tests were conducted, and the results demonstrate that the proposed control approach outperforms the baseline control strategies and reduces overall average delays by 11.64% on average. By leveraging the capabilities of this innovative model, this study aims to address major challenges related to traffic congestion and energy inefficiency toward more effective and adaptable AI-based traffic control systems.

Wang, Hong [Oak Ridge National Laboratory (ORNL), ↗
DOE OSTI2025

A Comparison of Electronic Structure Methods for Predicting the Hydrogenation Energies of Candidate Molecules for Hydrogen Storage

The development of novel energy materials and fuels is required to expand current available energy sources. Aiming to reach this goal, there is growing interest in using molecular hydrogen as an energy carrier due to its abundance and high energy density. Liquid organic hydrogen carriers (LOHCs) are a promising route to the large-scale storage and transport of hydrogen for use in the energy economy. The search for thermodynamically viable LOHC molecules for real world use has led to a set of constraints on the dehydrogenation enthalpy and the minimum gravimetric hydrogen capacity. These constraints allow one to formulate the search for an ideal LOHC candidate molecule as an optimization problem well suited to the strengths of machine learning and artificial intelligence computational approaches. A critical barrier to a large-scale, high-throughput screening of LOHC candidate molecules is the lack of reliable training data. Computational electronic structure methods including density functional theory, coupled cluster approximations, and diffusion Monte Carlo can be used to provide training data where experimental data are either unreliable or do not exist. In this work, we use these methods to calculate the dehydrogenation energies and enthalpies of candidate LOHC molecules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗