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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
DOE OSTI2020

Anion Association Strength as a Unifying Descriptor for the Reversibility of Divalent Metal Deposition in Nonaqueous Electrolytes

Developing next-generation battery chemistries that move beyond traditional Li-ion systems is critical to enabling transformative advances in electrified transportation and grid-level energy storage. Here, we provide the first evidence for common descriptors for improved reversibility of metal plating/stripping in nonaqueous electrolytes for multivalent ion batteries. Focusing first on the specific role of chloride (Cl – ) in promoting electrochemical reversibility in multivalent systems, rotating disk (RDE) and ring-disk electrode (RRDE) investigations were performed utilizing a variety of divalent cations (Mg 2+ , Zn 2+ , and Cu 2+ ) and the bis-(trifluoromethane sulfonyl) imide (TFSI – ) anion. By introducing varying concentrations of Cl – , a cooperative effect is observed between TFSI – and Cl – that yields the more reversible behavior of mixed electrolytes relative to electrolytes containing only TFSI – . This effect is shown to be general for Mg, Zn, and Cu electrodeposition, and mechanistic understanding of the role of Cl – in improving reversibility of TFSI-based electrolytes is obtained through the combination of R(R)DE experimental results and density functional theory (DFT) evaluation of the redox activity and thermodynamic stability of various TFSI- and Cl-based solution complexes of metal ions. The cooperative anion effect is further generalized to other mixed-anion systems, where systematic variations in anion association strength predicted from DFT (i.e., Cl – > OTf – ≈ TFSI – > BF 4 – > PF 6 – ) yield corresponding trends in redox potentials and improvements of ≥200 mV in the reversibility of metal deposition/dissolution. These results identify anion association strength as a common descriptor for the reversibility of divalent metal anodes and suggest a set of general design principles for developing new electrolytes with improved activity and stability.

25 ENERGY STORAGE↗
DOE OSTI2024

Materials descriptors for advanced water dissociation catalysts in bipolar membranes

The voltage penalty driving water dissociation (WD) at high current density is a major obstacle in the commercialization of bipolar membrane (BPM) technology for energy devices. Here, in this study, we show that three materials descriptors, that is, electrical conductivity, microscopic surface area and (nominal) surface-hydroxyl coverage, effectively control the kinetics of WD in BPMs. Using these descriptors and optimizing mass loading, we design new earth-abundant WD catalysts based on nanoparticle SnO 2 synthesized at low temperature with high conductivity and hydroxyl coverage. These catalysts exhibit exceptional performance in a BPM electrolyser with low WD overvoltage (η wd ) of 100 ± 20 mV at 1.0 A cm -2 . The new catalyst works equivalently well with hydrocarbon proton-exchange layers as it does with fluorocarbon-based Nafion, thus providing pathways to commercializing advanced BPMs for a broad array of electrolysis, fuel-cell and electrodialys is applications.

Sasmal, Sayantan↗
DOE OSTI2023

Machine learning predictions of diffusion in bulk and confined ionic liquids using simple descriptors

Ionic liquids have many intriguing properties and widespread applications such as separations and energy storage. However, ionic liquids are complex fluids and predicting their behavior is difficult, particularly in confined environments. We introduce fast and computationally efficient machine learning (ML) models that can predict diffusion coefficients and ionic conductivity of bulk and nanoconfined ionic liquids over a wide temperature range (350–500 K). The ML models are trained on molecular dynamics simulation data for 29 unique ionic liquids as bulk fluids and confined in graphite slit pores. This model is based on simple physical descriptors of the cations and anions such as molecular weight and surface area. Here, we also demonstrate that accurate results can be obtained using only descriptors derived from SMILES (simplified molecular-input line-entry system) codes for the ions with minimal computational effort. This offers a fast and efficient method for estimating diffusion and conductivity of nanoconfined ionic liquids at various temperatures without the need for expensive molecular dynamics simulations.

74 ATOMIC AND MOLECULAR PHYSICS↗
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 OSTI2022

Descriptor Aided Bayesian Optimization for Many-Level Qualitative Variables With Materials Design Applications

Abstract Engineering design often involves qualitative and quantitative design variables, which requires systematic methods for the exploration of these mixed-variable design spaces. Expensive simulation techniques, such as those required to evaluate optimization objectives in materials design applications, constitute the main portion of the cost of the design process and underline the need for efficient search strategies—Bayesian optimization (BO) being one of the most widely adopted. Although recent developments in mixed-variable Bayesian optimization have shown promise, the effects of dimensionality of qualitative variables have not been well studied. High-dimensional qualitative variables, i.e., with many levels, impose a large design cost as they typically require a larger dataset to quantify the effect of each level on the optimization objective. We address this challenge by leveraging domain knowledge about underlying physical descriptors, which embody the physics of the underlying physical phenomena, to infer the effect of unobserved levels that have not been sampled yet. We show that physical descriptors can be intuitively embedded into the latent variable Gaussian process approach—a mixed-variable GP modeling technique—and used to selectively explore levels of qualitative variables in the Bayesian optimization framework. This physics-informed approach is particularly useful when one or more qualitative variables are high dimensional (many-level) and the modeling dataset is small, containing observations for only a subset of levels. Through a combination of mathematical test functions and materials design applications, our method is shown to be robust to certain types of incomplete domain knowledge and significantly reduces the design cost for problems with high-dimensional qualitative variables.

Engineering↗
DOE OSTI2023

Machine Learning for Prediction of Thermodynamic Descriptors

Our objective is to apply machine learning (ML) algorithms for the prediction of molecular catalysis descriptors from geometric properties derived from experimental crystallographic databases. Catalysis is often considered a “low-data” discipline that is poorly suited for ML methods. An exception is the extensive structural information that is available for molecular catalysts through the Cambridge Structural Database (CSD), which contains atomically precise molecular structures from X-ray diffraction analysis for >600K metal complexes. As a proof-of-principle, we targeted the prediction of hydricity, a thermodynamic property that provides understanding and control of catalytic hydride transfer. We built a training set composed of ~100 molecular complexes with a known hydricity and structural information from the CSD. This data set was converted into a machine-readable format using the smooth overlap of atomic positions (SOAP) representation and further labeled with simple electronic descriptors for the metal centers. Multiple different neural networks were trained on this data set, and the accuracy of the hydricity predictions ranged from < 2 kcal/mol to 20 kcal/mol. The accuracy of each model was highly sensitive to which compounds were in the train versus test set, underscoring the challenges associated with small and chemically diverse data sets. Finally, to further augment the data set, we attempted to experimentally measure several new hydricity values, however these experiments were unsuccessful due to undesired chemical reactivity of the selected complexes.

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

High entropy oxides prediction and discovery by the Mixed Enthalpy-Entropy Descriptor

The vast, high-dimensional composition space of high-entropy oxides (HEOs) offers exceptional opportunities for functional materials discovery, yet it also poses a fundamental challenge: the rational and efficient prediction of stable, synthesizable compositions and the corresponding structure–property relationships. Despite growing interest, the field still lacks broadly applicable, physically grounded descriptors capable of navigating various large chemical spaces. Here, we introduce a Mixed Enthalpy–Entropy Descriptor (MEED) that enables rapid, first-principles–based prediction of HEOs synthesizability across diverse chemistries. Using MEED, we perform high-throughput screening of two distinct HEO families: rocksalt oxides and perovskite oxides. The predicted top candidates in each family were experimentally validated. MEED reveals unifying thermodynamic and structural principles governing stability across both chemical compositions and polymorphs, providing mechanistic insight into the formation of high-entropy phases. This work significantly broadens the accessible chemical design space for HEOs and establishes a data-efficient framework for accelerating the discovery of next-generation functional materials.

Yu, Liping [University of Central Florida]↗
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↗
DOE OSTI2024

Critical Assessment of Electronic Structure Descriptors for Predicting Perovskite Catalytic Properties

The discovery and design of materials which can efficiently catalyze the oxygen reduction and evolution reactions at reduced temperatures is important for facilitating the widespread adoption of fuel cell and electrolyzer technologies. Numerous studies have produced correlations between catalytic properties, such as oxygen surface exchange or electrode area specific resistance (ASR), and properties of the catalyst material. However, correlations have historically been limited in scope (e.g., using only a few materials or at a single temperature) and it has been difficult to provide detailed assessments of their robustness. Here, in this study, we assess the ability of the O p-band center electronic structure descriptor, obtained from density functional theory (DFT) calculations, to correlate with oxygen surface exchange rates, diffusivities, and area specific resistances for a large database of perovskite oxide catalytic properties. By data mining the literature, we obtain 747 catalytic property value data points spanning 299 unique perovskite compositions from 313 studies. We assess linear correlations of each property with the O p-band center and find generally modest correlations that are qualitatively useful (prediction mean absolute errors of about 0.5 log units are typical), where the correlations are improved at higher temperatures (e.g., 800 °C vs. 500 °C) and significantly improve when considering fits to the subset of materials which have multiple independent measurements. These findings suggest that the spread of property data is significantly influenced by experimental uncertainty, and subsequent measurements of additional materials will likely improve the O p-band center correlations.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

O 2 dosage as a descriptor of TWC performance under lean/rich dithering in stoichiometric natural gas engines

The effects of oscillations in gas composition, known as lean/rich dithering, on the performance of a commercial Pd-based three-way catalyst (TWC) for stoichiometric natural gas (NG) engines were evaluated using synthetic exhaust flow reactor experiments. Under simulated NG exhaust conditions, NO conversion was intimately correlated to CH 4 conversion at slightly fuel-rich operating conditions. CH 4 conversion significantly varied with lambda (or O 2 concentration) and depended on the direction of the lambda change. The dynamic CH 4 conversion is likely related to the change of catalyst oxidation state and structures of Pd active sites. The CH 4 -NO cross-over point was found at a rich-biased lambda rather than stoichiometry. Compared to static operation, catalyst performance was much higher under a realistic dithering condition. The impacts of dithering parameters including amplitude and frequency on CH 4 and NO conversions were explored. O 2 dosage is proposed as an effective descriptor of the TWC dithering performance. In the O 2 -dosage space, the performance of distinct dithering parameters collapse, helping to elucidate the fundamental influence of dithering parameters on TWC performance. With the help of O2 dosage, an optimal operating window leading to high CH 4 and NO conversions was identified. A higher dithering amplitude is required to keep the catalyst in a reduced state for higher CH 4 and NO conversions, while an appropriate O 2 dosage that does not exceed the breakthrough OSC is necessary to avoid excess O 2 that results in NO slip. The dithering amplitude and O 2 -dosage-metric maps provide a new method for studying dynamic TWC performance and identifying optimum operation strategies.

03 NATURAL GAS↗
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 OSTI2021

Initial Fermi orbital descriptors for FLOSIC calculations: The quick-FOD method

Fermi orbital descriptors (FODs) play a key role in Fermi-Löwdin orbital self-interaction correction (FLOSIC) calculations used to remove self-interaction from approximate density functionals. Optimal FODs are obtained by minimizing the self-interaction-corrected total energy, and, in this process, identifying initial sets of FODs becomes crucial. Here we propose, implement and test a novel method for automatically initializing FODs, quick-FOD, based on the minimization of an empirical energy expression that involves a Coulomb-like FOD-electron density attraction, an FOD-FOD short-range repulsion, and an exchange-like FOD-density repulsion. Quick-FOD successfully reproduces FOD arrangements in qualitatively good agreement with Lewis theory and with full-fledged FLOSIC calculations.

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

Permutation-adapted complete and independent basis for atomic cluster expansion descriptors

Atomic cluster expansion (ACE) methods provide a systematic way to describe particle local environments of arbitrary body order. For practical applications it is often required that the basis of cluster functions be symmetrized with respect to rotations and permutations. Existing methodologies yield sets of symmetrized functions that are over-complete. These methodologies thus require an additional numerical procedure, such as singular value decomposition (SVD), to eliminate redundant functions. In this work, it is shown that analytical linear relationships for subsets of cluster functions may be derived using recursion and permutation properties of generalized Wigner symbols. From these relationships, subsets (blocks) of cluster functions can be selected such that, within each block, functions are guaranteed to be linearly independent. It is conjectured that this block-wise independent set of permutation-adapted rotation and permutation invariant (PA-RPI) functions forms a complete, independent basis for ACE. Along with the first analytical proofs of block-wise linear dependence of ACE cluster functions and other theoretical arguments, numerical results are offered to demonstrate this. The utility of the method is demonstrated in the development of an ACE interatomic potential for tantalum. Using the new basis functions in combination with Bayesian compressive sensing sparse regression, some high degree descriptors are observed to persist and help achieve high-accuracy models.

Angular momentum↗
DOE OSTI2025

Electronic density of states as the descriptor of elastic bond strength, ductility, and local lattice distortion in BCC refractory alloys

Although electronic density of states (DOS) is fundamental to materials properties, its general relationship to mechanical properties of alloys is not well established. In this paper, using density functional theory (DFT) calculations, we show that the electronic occupancy at the Fermi level, N(E f ), obtained from DOS is a key descriptor of alloy strength and ductility. Our comprehensive analysis of numerous body centered cubic (BCC) refractory high entropy alloys (RHEAs) shows an overwhelming correlation that low N(E f ) indicates strong bonds that have high stiffness resulting in high elastic constants. High bond stiffness indicates presence of covalent nature of bonds that are directional in nature resulting in resistance to deformation leading to high bulk (B) and shear (G) moduli. Consequently, N(E f ) provides a direct correlation to the tendency of alloy ductility evidenced in the Pugh ratio (G/B). As stiffer bonds result in lower local lattice distortion (LLD), N(E f ) are LLD are also found to be corelated which opens up a correlation to solid solution strengthening and yield strength. Thus, this work unveils fundamental correlations between N(E f ) and (1) elastic bond strength, (2) ductility, and (3) LLD. These correlations open opportunities for the design of high strength high ductile RHEAs.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Geometric compatibility measure m' for twin transmission: A predictor or descriptor?

In this work, the geometric compatibility factor m' is critically analyzed to assess whether it can be used to interpret/predict twin transmission (TT) across grain boundaries (GBs). This geometric measure is widely used to relate the likelihood of TT to the misalignment of both the shear and plane-normal directions within a twin set (i.e., incoming and outgoing twin). Here, using a large set of electron back scattering diffraction (EBSD) data, a detailed statistical analysis of twin-GB interactions is performed for {${1\bar{01}}2$} tensile twins in hexagonal close-packed (HCP) metals Mg, Zr, and Ti at different strain levels. In addition, a full-field crystal plasticity model is employed to quantify the role of local stresses and the applicability of m' as a criterion for the TT process. This combined study addresses the following three main questions: (i) What is the fidelity of m' in describing experimentally observed TTs? (ii) Can m' be used as a metric to predict/anticipate TT? (iii) Does m' naturally capture local stress effects? As a descriptor, m' cannot rationalize ~25% of TT events observed in Mg or more than 50% of TT events in Zr and Ti. As a predictor, the m'-measure does not predict TT events in over ~50% of twin-GB interactions analyzed. Further, the applicability of m' to describe and predict TT events decreases with an increase in elastic anisotropy, plastic anisotropy, and macroscopic strain levels. Finally, the twinning simulations reveal that m' does not capture the key effects of local stresses on variant selection upon twin transmission. The local stress induced by the twinning shear transformation plays a dominant role in driving the TT process compared to the geometric alignment of the constituting twins, i.e., m'.

36 MATERIALS SCIENCE↗
DOE OSTI2024

E min : A First-Principles Thermochemical Descriptor for Predicting Molecular Synthesizability

Predicting the synthesizability of a new molecule remains an unsolved challenge that chemists have long tackled with heuristic approaches. Here, in this study, we report a new method for predicting synthesizability using a simple yet accurate thermochemical descriptor. We introduce E min , the energy difference between a molecule and its lowest energy constitutional isomer, as a synthesizability predictor that is accurate, physically meaningful, and first-principles based. We apply E min to 134,000 molecules in the QM9 data set and find that E min is accurate when used alone and reduces incorrect predictions of "synthesizable" by up to 52% when used to augment commonly used prediction methods. Our work illustrates how first-principles thermochemistry and heuristic approximations for molecular stability are complementary, opening a new direction for synthesizability prediction methods.

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

Predicting a Key Catalyst-Performance Descriptor for Supported Metal Nanoparticles: Metal Chemical Potential

The chemical potential of metal atoms in supported nanoparticles provides a convenient descriptor of their performance as heterogeneous catalysts that captures the effects of particle size, support and alloying. Based on microcalorimetric measurements, the chemical potential is shown in this work to be predictable as a function of monometallic particle diameter and the adhesion energy of the particle to the support, and, for oxide supports, this adhesion energy correlates predictably with metal oxophilicity. These correlations provide predictions of metal chemical potential that can enable catalyst design. They also suggest an improvement in the Gibbs-Thomson relation for free-standing nanoparticles, whether metals or molecular solids.

metal/oxide interface↗