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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A Bayesian framework for adsorption energy prediction on bimetallic alloy catalysts

Abstract For high-throughput screening of materials for heterogeneous catalysis, scaling relations provides an efficient scheme to estimate the chemisorption energies of hydrogenated species. However, conditioning on a single descriptor ignores the model uncertainty and leads to suboptimal prediction of the chemisorption energy. In this article, we extend the single descriptor linear scaling relation to a multi-descriptor linear regression models to leverage the correlation between adsorption energy of any two pair of adsorbates. With a large dataset, we use Bayesian Information Criteria (BIC) as the model evidence to select the best linear regression model. Furthermore, Gaussian Process Regression (GPR) based on the meaningful convolution of physical properties of the metal-adsorbate complex can be used to predict the baseline residual of the selected model. This integrated Bayesian model selection and Gaussian process regression, dubbed as residual learning, can achieve performance comparable to standard DFT error (0.1 eV) for most adsorbate system. For sparse and small datasets, we propose an ad hoc Bayesian Model Averaging (BMA) approach to make a robust prediction. With this Bayesian framework, we significantly reduce the model uncertainty and improve the prediction accuracy. The possibilities of the framework for high-throughput catalytic materials exploration in a realistic setting is illustrated using large and small sets of both dense and sparse simulated dataset generated from a public database of bimetallic alloys available in Catalysis-Hub.org.

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

Trends in Formic Acid Electro-Oxidation on Transition Metals Alloyed with Platinum and Palladium

Direct formic acid fuel cells (DFAFCs) have emerged as a promising power source to meet increased demands for alternative energy sources in the transportation and portable energy storage sectors. Furthermore, these fuel cells utilize formic acid (FA), a nontoxic and carbon-neutral fuel when produced from biomass or via CO 2 reduction. Despite the promise of DFAFCs, the best monometallic catalysts, platinum and palladium, are poisoned by CO through the indirect oxidation pathway and require large overpotentials. By alloying Pt and Pd with other metals, we aim to improve both the activity and selectivity of these catalysts. Here, we present a systematic density functional theory (GGA-PW91) study of FA electro-oxidation (FAO) on the (111) facet of bimetallic Pt (Pt 3 M) or Pd (Pd 3 M) catalysts (M = Au, Ag, Cu, Pt, Pd, Ir, Rh, Ru, or Re) to evaluate the catalytic performance of these surfaces. For each surface, we calculate free energy diagrams and onset potentials of three key reaction mechanisms: direct oxidation of FA via carboxyl (COOH), direct oxidation of FA via formate (HCOO), and the indirect oxidation of FA that first forms CO en route to full oxidation to CO 2 . We then display the trends in the form of phase diagrams that compare the activity of the calculated surfaces against regions of high activity using the free energies of adsorbed CO and OH as descriptors, enabling high-throughput screening and design of improved catalysts, particularly those alloying Pt or Pd with Ir, Ru, or Re.

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

Sustainable Oxide Electrocatalyst for Hydrogen- and Oxygen-Evolution Reactions

In this work, an electrocatalyst for water splitting based on earth-abundant metals is reported. This perovskite-oxide catalyst, CaSrFe 0.75 Co 0.75 Mn 0.5 O 6-δ (CSFCM), is examined using both experimental and computational methods. It demonstrates a combination of properties, which include (a) very high activity for the oxygen-evolution reaction with an overpotential of eta = 0.19 V at 10 mA/cm 2 , (b) high stability over 1000 cycles of catalysis, (c) the ability to catalyze the hydrogen-evolution reaction effectively in both acidic and basic conditions, and (d) catalytic activity as a single-phase bulk material without the need for any additional processing, multicomponent composite preparation, or nanofabrication. Therefore, the catalytic activity of CSFCM is intrinsic, making it a good benchmark compound for future studies of electrocatalytic parameters. This work also highlights the impact of systematic structural design on electrocatalytic activity. Results from density functional theory calculations indicate that in addition to an optimal e g occupancy of similar to 1, an additional descriptor, i.e., maximizing the number of free e g carriers, correlates with the electrocatalytic activity.

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

Actinides in complex reactive media: A combined ab initio molecular dynamics and machine learning analytics study of transuranic ions in molten salts

The predominant ionic chemistry and the similarity in ionic radius of actinides make it very difficult to structurally distinguish them in liquids. In this work, while ab initio molecular dynamics shows that the f-states clearly affects the electronic properties, their impact on structural properties is not obvious. For the series of trivalent actinides U 3+ , Pu 3+ , Cm 3+ , Cf 3+ , Fm 3+ in molten NaCl and FLiBe, actinide ligand bonds have a higher degree of covalency in NaCl (than in FLiBe), and a higher degree of ionicity in FLiBe. Furthermore, a machine learned classification model can distinguish atomic environments of chemically similar actinides, as long as atoms beyond the first solvation shells are considered. Our work shows that only two types of descriptors are necessary to account for all the fluctuations in heavy metal/molten salt mixtures: The first descriptor represents the electronic state of the heavy metal, while the second encompasses the local coordination environment.

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

Revisiting Competitive Adsorption of Small Molecules in the Metal–Organic Framework Ni-MOF-74

To precisely evaluate the potential of metal-organic frameworks (MOFs) for gas separation and purification applications, it is crucial to understand how various molecules competitively adsorb inside MOFs. In this paper, we combine in situ infrared spectroscopy with ab initio calculations to investigate the mechanisms associated with co-adsorption of a number of small molecules including CO, NO, and CO 2 inside the prototypical framework Ni-MOF-74. Surprisingly, we find that the displacement of CO bound inside Ni-MOF-74 (binding energy of 53 kJ/mol) is readily driven by CO 2 exposure, even though CO 2 has a noticeably weaker binding energy of only 41 kJ/mol; meanwhile, the significantly more strongly binding NO molecule (90 kJ/mol) is not able to easily displace bound CO inside Ni-MOF74. These results show that single-phase binding energies of a molecule inside the MOF cannot completely describe their interaction with the MOF in the presence of other guest molecules. Here, we unveil a number of crucial factors such as the kinetic barrier, partial pressure, secondary binding sites, and guest-host/lateral interactions that control the co-adsorption process and combined with the binding energy are better descriptors of the behavior and adsorption of gas mixtures inside MOFs.

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

Moltensaltpropnet

MoltenSaltPropnet is a physics-informed machine learning framework that aims to predict the thermophysical properties of molten fluoride and chloride salt mixtures, which are crucial for the design and safety of Generation IV molten salt reactors. The code processes data from the Molten-Salt Thermal Properties Database (MSTDB-TP) and the Janz compendium, converting critically evaluated correlations into fast, differentiable surrogate models for density, viscosity, thermal conductivity, and heat capacity across 448 distinct salt systems. The implementation consists of several key components: 1. Data Curation: The code parses and cleans the raw data, normalizing elemental mole fractions and extracting relevant regression coefficients for various thermophysical properties. 2. Feature Engineering: It generates fixed-length numerical descriptors that encapsulate the composition and temperature, incorporating polynomial interaction terms and dimensionality-reduction techniques to optimize model performance. 3. Coefficient Learning: Four different machine learning architectures are employed: a deep residual network (ResNet), a Kolmogorov–Arnold network (KAN), a sparsity-inducing neural network (SNN), and classical regression models. Each model learns to predict coefficients that define the temperature-dependent correlations for the thermophysical properties. 4. Property Reconstruction: The predicted coefficients are used to compute temperature-dependent property values, ensuring positivity and monotonic trends through a composite loss function that enforces physical constraints. 5. User Interface: An open-source web application enables users to filter the database, train task-specific models, and visualize the results, allowing for rapid exploration of candidate salt mixtures. MoltenSaltPropnet bridges the gap between limited experimental data and high-fidelity reactor simulations, providing a powerful tool for researchers in the field of molten salt reactors and advanced nuclear energy systems.

Retamales, Mauricio Eduardo Tano [Idaho National L↗
DOE OSTI2020

w19_OMEC Scientific Highlight [Slides]

Scientific Achievement: Revealed the effect of graphite substrate on H binding / hydrogen evolution reaction (HER) activity of organic molecular electrocatalysts (OMECs); and demonstrated the effect of molecular modification on calculated HER activity descriptor. Significance and Impact: Understanding of HER processes is important for discovering low-cost OMECs for hydrogen production and energy application; design of OMEC structures remains challenging due to the unexplored redox properties of organic molecules; and density functional theory (DFT) has been proposed to provide needed insights into HER activity for improved materials design. Research Details: DFT calculations on the binding energy of HER intermediates are used for predicting reduction potentials and and calculated H adsorption energy as activity descriptor; and LANL Institutional Computing (IC) resources are essential for successful project execution given the size of structures involved in modeling molecule-support interaction.

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

Quantum Simulations of Radiation Damage in a Molecular Polyethylene Analog

Abstract An atomic‐level understanding of radiation‐induced damage in simple polymers like polyethylene is essential for determining how these chemical changes can alter the physical and mechanical properties of important technological materials such as plastics. Ensembles of quantum simulations of radiation damage in a polyethylene analog are performed using the Density Functional Tight Binding method to help bind its radiolysis and subsequent degradation as a function of radiation dose. Chemical degradation products are categorized with a graph theory approach, and occurrence rates of unsaturated carbon bond formation, crosslinking, cycle formation, chain scission reactions, and out‐gassing products are computed. Statistical correlations between product pairs show significant correlations between chain scission reactions, unsaturated carbon bond formation, and out‐gassing products, though these correlations decrease with increasing atom recoil energy. The results present relatively simple chemical descriptors as possible indications of network rearrangements in the middle range of excitation energies. Ultimately, the work provides a computational framework for determining the coupling between nonequilibrium chemistry in polymers and potential changes to macro‐scale properties that can aid in the interpretation of future radiation damage experiments on plastic materials.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2024

AIACHNE's contribution for Nuclear Energy Agency Working Party on International Nuclear Data Evaluation Co-operation Subgroup 50

The AIACHNE (AI/ML Informed cAlifornium CHi Nuclear data Experiment) project aims at designing an experiment for the 252 Cf Prompt Fission Neutron Spectrum (PFNS) that explores systematic biases in an experimental database retrieved from the EXFOR databases. To that end, machine learning (ML) methods were applied to pint-point measurement features likely related to bia. From that information, we selected a feature that should be explored by the AIACHNE experiment. Measurement features are metadata encapsulating all pertinent information about the physical measurement and analysis techniques. Examples are, for instance, what neutron and fission detectors were used for the physical metadata, and what background reduction techniques were employed for analysis techniques. Such metadata were retrieved both from EXFOR entries as well as the literature of data sets described in detail in Ref. [2]. The prerequisite for applying machine learning techniques is casting the metadata into a format that can be parsed by the algorithm. This step might seem trivial but requires to find a unique language where metadata that carry the same physics meaning across several experiments must have the same identifier. One example is, for instance, the neutron detector. As seen in Figure 1, the machine learning code identified the use of 6 Li detectors as being related to bias in some datasets of the AIACHNE 252 Cf PFNS experimental database. In fact, here are several experiments that used neutron detectors containing 6Li in the database, for instance for the example below. EXFOR format has a unique keywords describing detectors such as “SCIN” or “GLASD”. One may think that these keywords are already sufficient descriptors for ML to uniquely find an issue. However, “SCIN” (used for [3, 4]) and “GLASD” (used for [5]) fail to inform the algorithm what is the active material in the detector. And, the key common issue leading to bias in 252 Cf related to neutron detectors is not whether it is a glass detector or a scintillator. No, the issue is that 6 Li was within both detector types and that even small mistakes in the detector response functions around approximately 200 keV are amplified by the 6 Li(n,α) resonance there leading to bias in data as highlighted in Fig. 1 and Ref. [1]. Hence, the features describing the neutron detector must call out the active material in the detector, rather than the existing EXFOR detector keyword, that the ML algorithm can find physically meaningful features related to bias. The AIACHNE team used a precursor of the WPEC (Working Party on International Nuclear Data Evaluation Co-operation) SG(Subgroup)-50 format to store the metadata for the ML analysis.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Gaussian approximation of dispersion potentials for efficient featurization and machine-learning predictions of metal–organic frameworks

Energy-related descriptors in machine learning are a promising strategy to predict adsorption properties of metal–organic frameworks (MOFs) in the low-pressure regime. Interactions between hosts and guests in these systems are typically expressed as a sum of dispersion and electrostatic potentials. The energy landscape of dispersion potentials plays a crucial role in defining Henry’s constants for simple probe molecules in MOFs. To incorporate more information about this energy landscape, we introduce the Gaussian-approximated Lennard-Jones (GALJ) potential, which fits pairwise Lennard-Jones potentials with multiple Gaussians by varying their heights and widths. The GALJ approach is capable of replicating information that can be obtained from the original LJ potentials and enables efficient development of Gaussian integral (GI) descriptors that account for spatial correlations in the dispersion energy environment. GI descriptors would be computationally inconvenient to compute using the usual direct evaluation of the dispersion potential energy surface. Here, we show that these new GI descriptors lead to improvement in ML predictions of Henry’s constants for a diverse set of adsorbates in MOFs compared to previous approaches to this task.

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

AIMD simulations of water over transition metal surfaces

AIMD simulations of water molecules over (111) surfaces of Ag, Au, Cu, Pt, Pd, and Rh, (211) and (100) surfaces of Ag, Au, Cu, and Pt, and (0001) surface of Ru. Data used for analysis in "OH binding energy as a universal descriptor of the potential of zero charge on transition metal surfaces," which can be found on ChemRxiv with DOI 10.26434/chemrxiv-2021-dkb6l.

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

Decoding the Mechanisms of Phase Transitions from In Situ Microscopy Observations

Abstract Analysis of the temperature‐ and stimulus‐dependent imaging data toward elucidation of the physical transformations is an ubiquitous problem in multiple fields. Here, temperature‐induced phase transition in BaTiO 3 is explored using the machine learning analysis of domain morphologies visualized via variable‐temperature scanning transmission electron microscopy (STEM) imaging data. This approach is based on the multivariate statistical analysis of the time or temperature dependence of the statistical descriptors of the system, derived in turn from the categorical classification of observed domain structures or projection on the continuous parameter space of the feature extraction‐dimensionality reduction transform. The proposed workflow offers a powerful tool for the exploration of the dynamic data based on the statistics of image representation as a function of the external control variable to visualize the transformation pathways during phase transitions and chemical reactions. This can include the mesoscopic STEM data as demonstrated here, but also optical, chemical imaging, etc., data. It can further be extended to the higher dimensional spaces, for example, analysis of the combinatorial libraries of materials compositions.

Valleti, Sai Mani Prudhvi↗
DOE OSTI2021

Assessment of DFT methods for the prediction of detachment energies and electronic structures of complex and multiply charged anions

Here, in this work, we assess the ability of different density functional theory methods to reproduce the vertical/adiabatic detachment energies (VDEs/ADEs) of a series of 40 anions, including monoanion versus multiply charged anions (MCAs) and covalent versus noncovalent interaction. The statistical analysis of errors is firstly performed by comparing the theoretical values with the experimental benchmark data obtained from the negative ion photoelectron spectroscopy (NIPES). The proposed optimally tuned range-separated (OTRS) functionals are proved to not only well reproduce the experimental VDEs/ADEs, but also simulate experimental NIPES. The result implies a more serious issue of delocalization error for MCAs and a balanced description of electron-rich/- deficient region of anions is necessary. The radius of the spherically symmetric average electron localization function (ELF) region is demonstrated as a useful descriptor for the characterization of electronic structure of anionic systems and a quasilinear fitting model is proposed to efficiently obtain their OTRS parameters.

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

Can a simple topological-constraints-based model predict the initial dissolution rate of borosilicate and aluminosilicate glasses?

Tuning glass composition to obtain targeted properties generally relies on empirical approaches. However, a deep understanding of the physical and chemical mechanisms linking glass composition to its structure and properties would enable developing reliable predictive models. Indeed, although empirical models are usually able to interpolate composition–property relationships within a given compositional envelope, they often fail at extrapolating predictions far from their training domain. Here, as an alternative route to empirical models, we show that a structural descriptor based on the number of topological constraints per atom can be used to predict the initial dissolution rate of aluminosilicate and borosilicate glasses after being parameterized on different families of glasses (specific series of borosilicate glasses). Sixteen glasses belonging to these families were studied and their initial dissolution rates were determined at 90 °C and pH 90 °C = 9, covering rates spanning over 5 orders of magnitude. The model based on topological constraints was trained based on seven select borosilicate glasses (R 2 = 0.997) and used to predict the dissolution rate of nine additional borosilicate and aluminosilicate glasses. We show that, provided that corrections are made for high alkali content glasses that dissolve incongruently (preferential release of Na), the model gives reasonable predictions, even far from its training domain.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Time for a drought experiment: Do you know your plants’ water status?

Abstract Drought stress is an increasing concern because of climate change and increasing demands on water for agriculture. There are still many unknowns about how plants sense and respond to water limitation, including which genes and cellular mechanisms are impactful for ecology and crop improvement in drought-prone environments. A better understanding of plant drought resistance will require integration of several research disciplines. A common set of parameters to describe plant water status and quantify drought severity can enhance data interpretation and research integration across the research disciplines involved in understanding drought resistance and would be especially useful in integrating the flood of genomic data being generated in drought studies. Water potential (ψw) is a physical measure of the free energy status of water that, along with related physiological measurements, allows unambiguous description of plant water status that can apply across various soil types and environmental conditions. ψw and related physiological parameters can be measured with relatively modest investment in equipment and effort. Thus, we propose that increased use of ψw as a fundamental descriptor of plant water status can enhance the insight gained from many drought-related experiments and facilitate data integration and sharing across laboratories and research disciplines.

Juenger, Thomas E. (ORCID:0000000195509288)↗
DOE OSTI2022

Machine Learning-driven Molecular Design for Therapeutic Discovery

The ongoing novel coronavirus pandemic (COVID-19) has highlighted the need for new therapeutics to counter the threat of emerging viral pathogens. The main proteases are a promising target for developing antiviral inhibitors. In this work, we utilized a novel combination of artificial intelligence-driven iterative design of covalent inhibitor candidates, physics-based computational modeling of protein-inhibitor interactions, and “All in One” Native MS biophysical assay screening and characterization of therapeutic candidates. With our existing expertise in hit generation using a particular scaffold as a starting point, we first generated tens of thousands of compounds that preserve the key scaffold. In order to optimize the candidates, we calculated about 136 descriptors consisting of 2D and 3D features for molecules targeting the SARS-CoV-2 Main protease (Mpro). These compounds were initially filtered according to properties and further sorted by predicted binding affinity using our automated docking modeling and machine learning methods. We tested a handful of candidates and identified two as inhibitors of Mpro with micromolar affinities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Corrosion and Enhanced Hydrogen Evolution in Electrochemical Reduction of Ammonium Carbamate on Transition Metal Surfaces

Experiments and theory are combined to search for catalyst activity and stability descriptors for the direct reactive capture and conversion (RCC) of CO 2 in ammonia capture solutions using Cu, Ag, Au, Sn, and Ti electrodes. Two major phenomena emerge in RCC that are not predominant in the electrochemical CO 2 reduction (CO 2 R) reaction, namely, the rapid corrosion and restructuring of the catalyst in the presence of the CO 2 -ammonia adducts and the promotion of the competing hydrogen evolution reaction (HER). The prevalence of HER in RCC is correlated to the electrostatic attraction of the protonated amine to the electrode and the repulsion of the captured CO 2 , using the potential of zero charge (PZC). The stability of catalysts under RCC conditions is a function of the applied potential and cannot be readily predicted using binding energy descriptors commonly used in the prediction of CO 2 R activity. Finally, a direct correlation between calculated binding energies of CO 2 R intermediates, atomic oxygen, hydrogen, and ammonia and the activity and stability of transition metals for RCC cannot be found, highlighting the need for descriptors beyond those known for CO 2 R.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗