SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Computational Data for: "Introducing Metal-Sulfur Active Sites in Metal-Organic Frameworks via Post-Synthetic Modification for Hydrogenation Catalysis"
Thermophysical properties of adsorbates and gas-phase species define the free energy landscape of heterogeneously catalyzed processes and are pivotal for an atomistic understanding of the catalyst performance. These thermophysical properties, such as the free energy or the enthalpy, are typically derived from density functional theory (DFT) calculations. Enthalpies are species-interdependent properties that are only meaningful when referenced to other species. The widespread use of DFT has led to a proliferation of new energetic data in the literature and databases. However, there is a lack of consistency in how DFT data is referenced and how the associated enthalpies or free energies are stored and reported, leading to challenges in reproducing or utilizing the results of prior work. Additionally, DFT suffers from exchange–correlation errors that often require corrections to align the data with other global thermochemical networks, which are not always clearly documented or explained. In this review, we introduce a set of consistent terminology and definitions, review existing approaches, and unify the techniques using the framework of linear algebra. This set of terminology and tools facilitates the correction and alignment of energies between different data formats and sources, promoting the sharing and reuse of ab initio data. Standardization of thermochemistry concepts in computational heterogeneous catalysis reduces computational cost and enhances fundamental understanding of catalytic processes, which will accelerate the computational design of optimally performing catalysts.
Catalytic CO 2 reduction reactions featuring high selectivity toward formate are relatively rare. In some homogeneous molecular CO 2 -reducing electrocatalysis, using triethylamine (TEA) and isopropanol (IPA) as additives improves catalytic performance in producing formate. In this paper, we investigate whether the rhenium(I) bis-diimine dicarbonyl complexes, cis-[Re(N^N) 2 (CO) 2 ] + , where N^N is 2,2’-bipyridine ([1] + ) or 3,4,7,8-tetramethyl-1,10-phenanthroline ([2] + ), are capable of electrocatalytically reducing CO 2 to formate in acetonitrile containing TEA and IPA. Catalyst [1] + was ineffective at CO 2 reduction, yielding formate quantities comparable to those produced in experiments without the catalyst. Catalyst [2] + , however, is a promising electrocatalyst for the CO 2 reduction reaction in the presence of TEA and IPA, with formate being produced in millimolar concentrations (10.5 mM), as detected by 1 H NMR spectroscopy after 6 h electrolysis (formate Faradaic efficiency = 11%, with the major balance going to H 2 ). Upon more detailed examination, [2] + exhibited a turnover frequency (TOF) of 12 s –1 for formate, comparable to other leading molecular catalysts that competently execute this reduction. Combinations of spectroscopy, electrochemistry, and theory were used to better understand the mechanism of CO 2 reduction by [2] + . Fourier transform infrared spectroelectrochemical (FTIR-SEC) data provided no evidence for CO ligand dissociation or substitution upon one- and two-electron reduction of [2] + , suggesting that a mechanism distinct from one that is metal-hydride-based is operative in catalysis. Computational studies guide mechanistic investigations toward the proposed formation of a hydrophenanthroline-based intermediate responsible for hydride transfer to CO 2 and electrocatalytic formate production from [2] + .
Not Available
Machine learning techniques have emerged as a useful tool for identifying complex patterns and correlations in large datasets, such as associating catalyst performance to its physicochemical properties. In the heterogeneous catalysis communities, machine learning models have mostly been developed using high-throughput quantum chemistry calculations, with only a few case studies resulting in experimentally validated catalyst improvements. This limited success may be due to the use of simplified catalyst structures in computational studies and the lack of comprehensive experimental datasets. In this Review, we bring together studies integrating high-throughput approaches and machine learning for the advancement of solid heterogeneous catalysis, leveraging both experimental and computational data. We systematically analyze trends in the field, based on the descriptors used as model input and output; the materials, devices, or reactions investigated; the dataset size; and the overall achievements. Furthermore, for models reporting unitless R 2 values, we compare the performances based on these mentioned trends.
A multiscale computational approach to examine direct DME synthesis in a packed bed reactor composed of Cu/ZnO/Al 2 O 3 and γ-Al 2 O 3 catalysts.
This invited presentation encapsulates computational investigation of nanostructured Ag electrocatalyst for CO2 to CO conversion to complement the surface science/electrochemical experiments.
With the growing demand for realistic representations of chemical structures and the advent of exascale computing, the intelligent sampling of potential energy surfaces and efficient identification of global minima have become more essential but also more feasible. Building on prior studies demonstrating the efficiency of the Artificial Bee Colony (ABC) swarm intelligence algorithm, we report a hybrid metaheuristic framework that integrates the adaptive exploration capabilities of ABC coupled with the exploitation strengths of genetic algorithms (GA) in a scalable, Python-based implementation. The resulting tool, RANGE (Robust Adaptive Nature-inspired Global Explorer), provides seamless interfaces to multiple potential energy evaluators, either directly or via widely used Python libraries, and is designed for high-performance computing environments. We describe the implementation details of RANGE and evaluate its performance, relative to ABC- or GA-alone based algorithms, on a variety of chemical systems, including molecular clusters and heterogeneous surfaces. In conclusion, our results demonstrate RANGE’s efficiency, robustness, and broad applicability in addressing challenging global optimization problems in computational chemistry and materials science.
Metal-organic frameworks have emerged as promising materials in the field of catalysis. They offer an optimal ground for screening catalysts and tailoring their catalytic properties. Here, in this work, via density functional theory (DFT) calculations, we investigated the catalytic activity of the trimetallic MOF nodes, M' 2 M for propylene oligomerization, by varying the active metal M from Sc to Cu with M' being Fe, aiming to grasp the impact of altering the active atoms on the catalyst's activity. Additionally, we examined how substituting the spectator atom, M', with other transition metals, i.e., from Sc to Cu, affects these energy barriers, keeping Ni as the active metal. We proposed several cases with lower or comparable energy barriers to the experimentally reported Fe 2 Ni trimetallic MOF node. In addition, we found a correlative relationship between spin-density from natural population analysis and energy barriers in the realm of C-C bond formation, whereby an elevation in spin-density is found to be inversely proportional to the magnitude of the energy barriers. Moreover, we calculated the energy barriers for C-C coupling and beta-hydride elimination using multireference NEVPT2 calculations on top of the CASSCF wave function to validate the rate-determining step that is predicted by DFT.
Computational investigations of heterogeneously catalyzed reactions using density functional theory (DFT) are often inaccurate, largely due to uncertainties in the choice of DFT functional (enthalpic uncertainty) and approximations for modeling adsorbate movement along the catalyst surface (entropic uncertainty). This work illustrates that both uncertainties are significant in the investigation of ethane dehydrogenation (EDH) and hydrogenolysis on Pt catalysts by considering the complete deconstruction of ethane on Pt(111), Pt(211), and Pt(100) using microkinetic modeling (MKM). Hence, this work uses both noncalibrated and Bayesian-calibrated MKMs to quantify and correct inaccuracies in macroscopic properties due to both uncertainties. A Bayesian approach to the correction of entropic errors was introduced using a “Modified Fermi Function (MFF)” to calibrate between the two bounds of entropy represented by the harmonic oscillator (HO) and free translator (FT) approximations. Regardless of enthalpic and entropic uncertainties, all three surfaces are capable of ethane activation; however, Pt(211) was found to be the most active and is largely responsible for methane production. Next, Pt(111) is largely responsible for acetylene production, and Pt(100) has the highest ethylene selectivity but is most susceptible to coking. By comparison of different calibrated models, the FT entropy approximation was found to better describe EDH under typical experimental conditions. Statistical evidence was found to support Pt(111) as the active site for EDH, assuming that one single site is responsible for the chemistry. On the three surfaces, competing second dehydrogenations to CH 2 CH 2 and CH 3 CH were observed as well as isomerization of CH 3 CH back to CH 2 CH 2 and deeper dehydrogenation of CH 3 CH. In conclusion, C–C cleavage was found to largely proceed via the CH 3 C intermediate on Pt(100) and Pt(111), while on Pt(211), it was via both CHC and CH 3 C.
The development of functional nanomaterials with controlled morphologies is essential for advancements in medicine, electronics and computing, energy, catalysis, and environmental applications. However, conventional synthesis methods often demand high energy input and pose significant environmental challenges. Urease-based biomineralization presents an efficient, eco-friendly alternative for nanomaterial production under mild conditions. In this study, we engineered Escherichia coli ( E. coli ) to express a urease gene cluster from Sporosarcina pasteurii using CRAGE-Duet technology. The engineered strain successfully synthesized calcium carbonate and calcium phosphate crystals. Expanding the approach, we synthesized metal oxide nanoparticles, including hematite (Fe 2 O 3 ), and nanocrystalline anatase titanium dioxide (TiO 2 ). These nanomaterials were characterized by electron microscopy, demonstrating the potential of E. coli as a sustainable and versatile platform for green nanomaterial synthesis.
The Haber–Bosch process for ammonia synthesis contributes up to ~3% of global greenhouse gas emissions. Plasmonic catalysts strongly concentrate light and can alter the reaction intermediates via out-of-equilibrium processes, providing the potential for an alternative, less-energy-intensive pathway to synthesize ammonia. Here, in this study, we show that gold-ruthenium (AuRu) bimetallic nanoparticles can synthesize ammonia at room temperature and pressure using visible light. We create AuRu alloys with varying compositions and achieve ammonia production rates of ~60 μmol per gram of catalyst bed per hour. In situ infrared spectroscopy reveals that light accelerates the hydrogenation of nitrogen intermediates compared to conventional thermal catalysis. Through computational modelling, we demonstrate that photo-excited electrons enable associative hydrogenation pathways for nitrogen activation rather than direct nitrogen–nitrogen bond breaking. This light-assisted mechanism requires both hydrogen and light working together to overcome the nitrogen activation barrier, mimicking how biological enzymes produce ammonia naturally and providing fundamental insights for developing sustainable, energy-efficient chemical synthesis.
This project focused on the development bimetallic metal-organic frameworks (MOFs) as gas phase heterogeneous catalysts for hydrogenation and hydroformylation reactions. MOFs are a new class of hybrid inorganic/organic materials that are highly crystalline, with structures consisting of metal nodes of specific geometries connected by organic linkers. Although there have been a number of studies of catalysis at MOF nodes in solution, there is little experimental data in the literature for gas phase reactions despite the fact that industrial heterogeneous catalysis on MOFs is more economically viable than homogeneous catalysis. The use of MOFs as heterogeneous catalysts presents the unique opportunity to carefully control the composition, geometry and ensemble sizes of the active sites, which are all critical factors for the rational design of new catalysts. Specific objectives of the project are as follows: (1) to tailor the geometry, composition and ensemble size of the active sites; (2) to understand how the adsorption of molecules at metal sites can be modified by interactions with a neighboring metal site; (3) to determine how oxidation states of the metal change during reaction and how these states can be modified by metal-metal electronic interactions; and (4) to elucidate reaction mechanisms and intermediates. For these studies, we have chosen to investigate selective hydrogenation of hydrocarbons and hydroformylation, which are industrially relevant reactions. Our interdisciplinary team of Chen, Shustova and Vogiatzis/Henkelman provides critical expertise in novel MOF synthesis (Shustova), atomic-scale surface science and catalysis (Chen) and computational studies of reaction mechanisms (Vogiatzis/Henkelman) that are necessary for the development of these catalysts. We believe that this work will lead directly to the rational design of new catalysts that are versatile, highly active/selective and suitable for industrial processes.
The precise understanding of adsorption energetics and molecular geometry at catalytic sites is fundamental for advancing catalysis, particularly under the constraints of resource efficiency and environmental sustainability. Here, this study benchmarks the performance of density functional theory (DFT) calculations against diffusion Monte Carlo (DMC) calculations for adsorption properties of small gas molecules relevant to CO oxidation—namely O 2 , CO, CO 2 , and atomic oxygen—on a single Pt atom supported by pristine graphene. Our findings reveal that DMC calculations provide a significantly different landscape of adsorption energetics compared to DFT results. Notably, DFT predicts different lowest-energy configurations and spin states, particularly for O 2 , which suggests potential discrepancies in predicting the catalytic behavior. Furthermore, this study identifies the critical issue of CO poisoning, highlighted by the large disparity between the DMC adsorption energies of O 2 (−1.23(2) eV) and CO (−3.37(1) eV), which can inhibit the catalytic process. These results emphasize the necessity for more sophisticated computational approaches in catalysis research, aiming to refine the prediction accuracy of reaction mechanisms and to enhance the design of more effective catalysts.
Divalent f-block chemistry has undergone rapid expansion in recent years, driven by advances in the stabilization of low-valent lanthanide and actinide complexes. Although f-elements were historically accessed in the +3 or higher oxidation states, the isolation of +2 species has revealed unusual spectroscopic signatures, distinct bonding motifs, and multiple accessible electronic configurations that influence their chemical and physical properties. These developments have generated opportunities in areas including quantum information science, molecular magnetism, catalysis, and luminescence. Computational chemistry has played a pivotal role in interpreting the electronic structure and reactivity of these systems. However, accurately modeling divalent f-block complexes remains challenging because of strong electronic correlation, multiconfigurational character, and the presence of close-in-energy competing electronic states. As experimental capabilities and theoretical methodologies continue to advance, a comprehensive assessment of divalent chemistry is both timely and needed. In this Review, we integrate experimental and theoretical perspectives to provide a systematic analysis of divalent lanthanide and actinide complexes across the f-block series. We critically assess the role of ligands in determining competing ground-state configurations (f n d 1 vs. f n+1 ) resulting in their distinct spectroscopic, magnetic, and bonding properties. Finally, we discuss emerging strategies for predictive modeling and rational design of low-valent f-block molecular systems, with potential applications ranging from dinitrogen reduction to quantum technologies.
Vanadium oxides are widely tunable materials, with many thermodynamically stable phases suitable for applications spanning catalysis to neuromorphic computing. The stability of vanadium in a range of oxidation states enables mixed-valence polymorphs of kinetically accessible metastable materials. Low-temperature synthetic routes to, and the properties of, these metastable materials are poorly understood and may unlock new optoelectronic and magnetic functionalities for expanded applications. In this work, we demonstrate topochemical reduction of α-V 2 O 5 to produce metastable vanadium oxide phases with tunable oxygen vacancies (>6%) and simultaneous substitutional tin incorporation (>3.5%). The chemistry is carried out at low temperature (65 °C) with solution-phase SnCl 2 , where Sn 2+ is oxidized to Sn 4+ as V 5+ sites are reduced to V 4+ during oxygen vacancy formation. Despite high oxygen vacancy and tin concentrations, the transformations are topochemical in that the symmetry of the parent crystal remains intact, although the unit cell expands. Band structure calculations show that these vacancies contribute electrons to the lattice, whereas substitutional tin contributes holes, yielding a compensation doping effect and control over the electronic properties. The SnCl 2 redox chemistry is effective on both solution-processed V 2 O 5 nanoparticle inks and mesoporous films cast from untreated inks, enabling versatile routes toward functional films with tunable optical and electronic properties. The electrical conductance rises concomitantly with the SnCl 2 concentration and treatment time, indicating a net increase in density of free electrons in the host lattice. This work provides a valuable demonstration of kinetic tailoring of electronic properties of vanadium–oxygen systems through top-down chemical manipulation from known thermodynamic phases.
In the past decade, artificial intelligence and deep learning have played increasingly prominent roles in materials design and discovery. Among these, generative AI models, known for their ability to create unique and complex structures, have emerged as state-of-the-art tools for materials screening due to their high efficiency and low computational cost. In catalysis, one of the major challenges is identifying promising material candidates within an immense chemical space. This challenge can be addressed using generative approaches, such as diffusion-based inverse design models. In this study, we present a machine learning-guided workflow that employed a diffusion model for the inverse design of bimetallic alloy catalysts for low-carbon ammonia decomposition, a key reaction for ammonia emission control and sustainable hydrogen production. Catalyst candidates were evaluated using nitrogen adsorption energy as the key descriptor, inspired by multiscale modeling. The proposed workflow identified low-cost, environmentally friendly catalysts with excellent catalytic performance, which have been validated theoretically and experimentally. Our framework decoupled the generative and property-prediction components, enhancing both flexibility and accuracy in the catalytic material design process.
The growing demand for energy-efficient processes to support a sustainable future drives the need for research to rapidly explore chemical and material space through accelerated catalyst discovery initiatives. Recent breakthroughs in high-throughput experimental and computational methods are transforming the catalysis field, surpassing traditional approaches to manipulating variables in catalytic processes. Key advancements in innovation include the integration of machine learning for efficient catalyst screening, high-throughput experimentation, data-driven methodologies employing comprehensive databases, and in situ and in operando techniques for realistic observations. This progress has undoubtedly been intertwined with a collaborative framework across disciplines, reshaping catalyst discovery methods in both industry and academia. This Opinion article presents a multifaceted perspective from coauthors with expertise spanning various stages of the Technology Readiness Level spectrum, highlighting both opportunities and persistent challenges in integrating computational and experimental approaches in catalysis. These challenges span from obtaining high-quality experimental data, scaling simulations to industrially relevant materials and process conditions to navigating the complexity and predictive accuracy of computational models.