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Results for “Chemical reaction network theory”

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 OSTI2024

Understanding electronic structure tunability by metal dopants for promoting MgB 2 hydrogenation

Hydrogen is a promising energy carrier, but its onboard application is limited by the need for compact, low-pressure storage solutions. Solid-state complex metal hydride systems, such as MgB 2 /Mg(BH 4 ) 2 , offer high storage capacities but suffer from sluggish kinetics and poor reversibility. One avenue for improving reactivity is to introduce metal dopants to alter electronic and atomic properties, but the role of these chemical additives remains poorly understood, particularly for the hydrogenation reaction. In this work, we used density functional theory calculations on model MgB 2 systems to rationalize the potential role of metal dopants in destabilizing B–B bonding within the MgB 2 lattice. We carried out detailed electronic structure analyses for 28 different metal dopant adatoms to identify properties that contribute to a dopant’s efficacy. Based on the simulation results, we propose that an intermediate ionic and covalent character of the bonds between adatoms and B atoms is desirable for facilitating charge redistribution, disrupting the B–B bond network, and promoting H 2 dissociation and H atom chemisorption on MgB 2 .

08 HYDROGEN↗
DOE OSTI2023

Probing reaction channels via reinforcement learning

Abstract Chemical reactions are dynamical processes involving the correlated reorganization of atomic configurations, driving the conversion of an initial reactant into a result product. By virtue of the metastability of both the reactants and products, chemical reactions are rare events, proceeding fleetingly. Reaction pathways can be modelled probabilistically by using the notion of reactive density in the phase space of the molecular system. Such density is related to a function known as the committor function, which describes the likelihood of a configuration evolving to one of the nearby metastable regions. In theory, the committor function can be obtained by solving the backward Kolmogorov equation (BKE), which is a partial differential equation (PDE) defined in the full dimensional phase space. However, using traditional methods to solve this problem is not practical for high dimensional systems. In this work, we propose a reinforcement learning based method to identify important configurations that connect reactant and product states along chemical reaction paths. By shooting multiple trajectories from these configurations, we can generate an ensemble of states that concentrate on the transition path ensemble. This configuration ensemble can be effectively employed in a neural network-based PDE solver to obtain an approximation solution of a restricted BKE, even when the dimension of the problem is very high. The resulting solution provides an approximation for the committor function that encodes mechanistic information for the reaction, paving a new way for understanding of complex chemical reactions and evaluation of reaction rates.

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

Transient Coupled Chemical-Thermal-Fluid Field Simulation for Sealed Aluminum-clad Spent Nuclear Fuel Storage Canister

As the first step toward developing three-dimensional (3D) multi-physics computational fluid dynamics (CFD) model for unsealed and vented canister storage system, a 3D CFD model coupled with bulk gas radiolysis reactions was developed first for sealed DOE standard canisters filled with inert gas and trace amount of air and water. The workflow for constructing canister-scale 3D CFD models and coupling with gas phase radiolysis reactions were established, which can be readily extended to unsealed, vented canister storage system. This interim milestone report documents the theory of the model, workflow to establish radiolysis reaction network, and initial simulations of the evolutions of thermal fields and hydrogen gas concentrations within sealed DOE standard canisters over long period of time. In addition, a mesh refinement test was done to show that increasing the models mesh refinement had negligible impact upon the temperature profiles.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Computational insights into hydrogen adsorption energies on medium-entropy oxides

High entropy oxides (HEOs) have emerged as promising catalysts for several important chemical transformations including alkane activation. Hydrogen adsorption energy (HAE) has been used as a key descriptor for many reactions including methane C–H activation and hydrogen evolution reactions. Hence, understanding the relationship between HAEs and the surface chemistry of HEO surfaces could lay the foundation for meaningful correlations among methane C–H activation, HAE, and the complex, local environment of HEO surfaces. Here, we used a medium-entropy oxide as a prototypical system – Mg 0.25 Ni 0.25 Cu 0.25 Zn 0.25 O with a rock-salt structure – to interrogate these relationships. We sampled 2000 different surfaces of its (100) plane and calculated the HAEs at randomly chosen surface O sites using density functional theory (DFT). Our analysis of the 2000 data points reveals that the HAEs at the surface O sites are significantly influenced by the local environment around the adsorption sites, particularly the nature of the metal atom directly below the surface O site where H adsorbs. After comparing several popular graph-neural-network-based machine learning models, we found that the DimeNet++ model performed best achieving satisfactory accuracy in predicting HAEs for both Mg 0.25 Ni 0.25 Cu 0.25 Zn 0.25 O and slightly varied compositions. Our work underscores the promise of such models and the need for further refinement to address the complexity of HEOs.

Song, Haohong [Vanderbilt Univ., Nashville, TN (Un↗
DOE OSTI2022

Accurate Prediction of Voltage of Battery Electrode Materials Using Attention-Based Graph Neural Networks

Performing first-principles calculations to discover electrodes’ properties in the large chemical space is a challenging task. While machine learning (ML) has been applied to effectively accelerate those discoveries, most of the applied methods ignore the materials’ spatial information and only use predefined features: based only on chemical compositions. Here, we propose two attention-based graph convolutional neural network techniques to learn the average voltage of electrodes. Our proposed methods, which combine both atomic composition and atomic coordinates in 3D-space, improve the accuracy in voltage prediction significantly when compared to composition-based ML models. The first model directly learns the chemical reaction of electrodes and metal ions to predict their average voltage, whereas the second model combines electrodes’ ML predicted formation energy (E form ) to compute their average voltage. Our E form -based model demonstrates improved accuracy in transferability from our subset of learned Li ions to Na ions. Moreover, we predicted the theoretical voltage of 10 Na x MPO 4 F (M = Ti, Cr, Fe, Cu, Mn, Co, and Ni) fluorophosphate battery frameworks, which are unavailable in the Material Project database. It could be shown that we can expect average voltages higher than 3.1 V from those Na battery frameworks except from the NaTiPO 4 F and TiPO 4 F pair of electrodes, which offer an average voltage of 1.32 V.

25 ENERGY STORAGE↗
NASA NTRS2014

A Radiation Chemistry Code Based on the Greens Functions of the Diffusion Equation

Ionizing radiation produces several radiolytic species such as.OH, e-aq, and H. when interacting with biological matter. Following their creation, radiolytic species diffuse and chemically react with biological molecules such as DNA. Despite years of research, many questions on the DNA damage by ionizing radiation remains, notably on the indirect effect, i.e. the damage resulting from the reactions of the radiolytic species with DNA. To simulate DNA damage by ionizing radiation, we are developing a step-by-step radiation chemistry code that is based on the Green's functions of the diffusion equation (GFDE), which is able to follow the trajectories of all particles and their reactions with time. In the recent years, simulations based on the GFDE have been used extensively in biochemistry, notably to simulate biochemical networks in time and space and are often used as the "gold standard" to validate diffusion-reaction theories. The exact GFDE for partially diffusion-controlled reactions is difficult to use because of its complex form. Therefore, the radial Green's function, which is much simpler, is often used. Hence, much effort has been devoted to the sampling of the radial Green's functions, for which we have developed a sampling algorithm This algorithm only yields the inter-particle distance vector length after a time step; the sampling of the deviation angle of the inter-particle vector is not taken into consideration. In this work, we show that the radial distribution is predicted by the exact radial Green's function. We also use a technique developed by Clifford et al. to generate the inter-particle vector deviation angles, knowing the inter-particle vector length before and after a time step. The results are compared with those predicted by the exact GFDE and by the analytical angular functions for free diffusion. This first step in the creation of the radiation chemistry code should help the understanding of the contribution of the indirect effect in the formation of DNA damage and double-strand breaks.

Plante, Ianik↗
DOE OSTI2022

Computational Aspects of Single-Molecule Kinetics for Coupled Catalytic Cycles: A Spectral Analysis

Catalysis from single active sites is analyzed using methods developed from single molecule kinetics. Using a stochastic Markov state description, the observable properties of general catalytic networks of reactions are expressed using an eigenvalue decomposition of the transition matrix for the Markov process. By the use of a sensitivity analysis, the necessary eigenvalues and eigenvectors are related to the energies of controlling barriers and wells located along the reaction routes. A generalization of the energetic span theory allows the eigenvalues to be computed from several activation energies corresponding to distinct barrier-well pairings. The formalism is demonstrated accurately for model problems for a physically realistic mechanism for an alkene hydrogenation reaction on a single atom catalyst.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1986

Do large rate coefficients for ion-polar neutral reactions have a serious effect on chemical models of dense clouds?

In order to incorporate large ion-polar neutral rate coefficients into existing gas phase reaction networks, it is necessary to utilize simplified theoretical treatments because of the significant number of rate coefficients needed. The authors have used two simple theoretical treatments: the locked dipole approach of Moran and Hamill for linear polar neutrals and the trajectory scaling approach of Su and Chesnavich for nonlinear polar neutrals. The former approach is suitable for linear species because in the interstellar medium these are rotationally relaxed to a large extent and the incoming charged reactants can lock their dipoles into the lowest energy configuration. The latter approach is a better approximation for nonlinear neutral species, in which rotational relaxation is normally less severe and the incoming charged reactants are not as effective at locking the dipoles. The treatments are in reasonable agreement with more detailed long range theories and predict an inverse square root dependence on kinetic temperature for the rate coefficient. Compared with the locked dipole method, the trajectory scaling approach results in rate coefficients smaller by a factor of approximately 2.5.

Herbst, E.↗
DOE OSTI2024

Diffusion-Limited Kinetics in Reactive Systems

A proper representation of chemical kinetics is vital to understanding, modeling, and optimizing many important chemical processes. In liquid and surface phases, where diffusion is slow, the rate at which the reactants diffuse together limits the overall rate of many elementary reactions. Commonly, the textbook Smoluchowski theory is utilized to estimate effective rate coefficients in the liquid phase. On surfaces, modelers commonly resort to much more complex and expensive Kinetic Monte Carlo (KMC) simulations. Here, in this study, we extend the Smoluchowski model to allow the diffusing species to undergo chemical reactions and derive analytical formulas for the diffusion-limited rate coefficients for 3D, 2D, and 2D/3D interface cases. With these equations, we are able to demonstrate that when species react faster than they diffuse they can react orders of magnitude faster than predicted by Smoluchowski theory, through what we term “the reactive transport effect”. We validate the derived steady-state equations against particle Monte Carlo (PMC) simulations, KMC simulations, and non-steady-state solutions. Furthermore, using PMC and KMC simulations, we propose corrections that agree with all limits and the computed data for the 2D and 2D/3D interface steady-state equations, accounting for unique limitations in the associated derived equations. Additionally, we derive equations to handle couplings between diffusion-limited rate coefficients in reaction networks. We believe these equations should make it possible to run much more accurate mean-field simulations of liquids, surfaces, and liquid–surface interfaces accounting for diffusion limitations and the reactive transport effect.

Johnson, Matthew Sean↗
DOE OSTI2021

Reactive Flows in Porous Media: Challenges in Theoretical and Numerical Methods

We review theoretical and computational research, primarily from the past 10 years, addressing the flow of reactive fluids in porous media. The focus is on systems where chemical reactions at the solid–fluid interface cause dissolution of the surrounding porous matrix, creating nonlinear feedback mechanisms that can often lead to greatly enhanced permeability. Here, we discuss insights into the evolution of geological forms that can be inferred from these feedback mechanisms, as well as some geotechnical applications such as enhanced oil recovery, hydraulic fracturing, and carbon sequestration. Until recently, most practical applications of reactive transport have been based on Darcy-scale modeling, where averaged equations for the flow and reactant transport are solved. We summarize the successes and limitations of volume averaging, which leads to Darcy-scale equations, as an introduction to pore-scale modeling. Pore-scale modeling is computationally intensive but offers new insights as well as tests of averaging theories and pore-network models. We include recent research devoted to validation of pore-scale simulations, particularly the use of visual observations from microfluidic experiments.

wormhole formation↗
DOE OSTI2020

Reaction Mechanism of Vapor-Phase Formic Acid Decomposition over Platinum Catalysts: DFT, Reaction Kinetics Experiments, and Microkinetic Modeling

A combination of periodic density functional theory (DFT, PW91-GGA) calculations, reaction kinetics experiments, and mean-field microkinetic modeling is used to derive insights on the reaction mechanism and determine the nature of the active site under reaction conditions for the vapor-phase decomposition of formic acid (FA, HCOOH) over Pt/C catalysts. Microkinetic models formulated using DFT energetics derived on the clean Pt(100) and Pt(111) required large parameter adjustments to reproduce the experimentally measured apparent activation energies and reaction orders. Further, these models predicted high surface coverage of adsorbed carbon monoxide (CO*), inconsistent with the environment of the active site in the DFT calculations on the clean surfaces. Consequently, we reperformed DFT calculations for the entire reaction network on partially CO*-covered (4/9 monolayer, ML) Pt(111) and Pt(100). The resultant microkinetic models, with thermochemistry and kinetics explicitly dependent on CO* coverage, were able to reproduce the experimentally determined activation energies and reaction orders, in addition to being self-consistent in CO* coverage. Our results suggest that Pt(100) is likely poisoned by CO* under typical reaction conditions and does not contribute significantly to the experimentally observed reactivity. Instead, we find that Pt(111) better represents the active site for FA decomposition reaction on Pt/C catalysts. The optimized model on 4/9 ML CO*-covered Pt(111) suggests that the reaction occurs via the carboxyl (COOH*) intermediate and that the spectator CO*-assisted pathways play a significant role under reaction conditions. Lastly, this study underscores the importance of spectator species on the energetics and the mechanism of a catalytic reaction and their key role in developing a model that better addresses the nature of the active site under realistic catalytic reaction conditions.

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

Catalytic Resonance Theory: The Catalytic Mechanics of Programmable Ratchets

Catalytic reaction networks of multiple elementary steps operating under dynamic conditions via a programmed input oscillation are difficult to interpret and optimize due to reaction system complexity. To understand these dynamic systems, individual elementary catalytic reactions oscillating between catalyst states were evaluated to identify their three fundamental characteristics that define their ability to promote reactions away from equilibrium. First, elementary catalytic reactions exhibit directionality to promote reactions forward or backward from equilibrium as determined by a ratchet directionality metric comprised of the input oscillation duty cycle and the reaction rate constants. Second, catalytic ratchets are defined by the catalyst state of strong or weak binding that permit reactants to proceed through the transition state. Third, elementary catalytic ratchets exhibit a cutoff frequency which defines the transition in applied frequency for which the catalytic ratchet functions to promote chemistry away from equilibrium. All three ratchet characteristics are calculated from chemical reaction parameters including rate constants derived from linear scaling parameters, reaction conditions, and catalyst state. The characteristics of the reaction network’s constituent elementary catalytic reactions provided an interpretation of complex reaction networks and a method of predicting the behavior of dynamic surface chemistry on oscillating catalysts.

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

Modeling Oxidative Dehydrogenation of Propane with Supported Vanadia Catalysts Using Multireference Methods

The oxidative dehydrogenation of propane over supported vanadium oxide catalysts poses significant computational challenges due to complex electronic structure changes along the reaction coordinate, driven primarily by changes in the oxidation states of vanadium. To address these challenges, we systematically test quantum chemical methods, including multireference (MR) approaches, domain-based local pair natural orbital coupled cluster theory (DLPNO-CCSD(T)), and density functional theory (DFT). The initial C–H bond-breaking transition state requires MR treatment due to its multireference character, while subsequent steps permit efficient single-reference calculations. For the rate-limiting C–H activation step mediated by the vanadyl moiety, complete 1 active space second-order perturbation theory (CASPT2) yields an apparent activation barrier (E app 600K ) of 138 kJ/mol, consistent with experimental values (134 ± 4 kJ/mol; Gruene et al. Catal. Today 2010, 157, 137). In contrast, DLPNO-CCSD(T) overestimates this barrier (198 kJ/mol), whereas DFT predictions span 125–150 kJ/mol, depending on the functional. Our multireference investigation of this transition metal oxide-catalyzed process demonstrates that an active space that incorporates the C–H σ and V=O σ/π bonding orbitals, oxygen lone pairs, and their antibonding counterparts adequately captures electronic structure changes along the chemical transformation. Furthermore, these findings provide a general strategy for active space selection in transition metal oxide-catalyzed C/O–H bond activation reactions. The reference dataset from this work, which includes MR calculations with manually selected active spaces for all intermediates and transition states in the propane ODH reaction network, will serve as a benchmark for automating active space selection in similar systems.

Catalysts↗
DOE OSTI2024

INSPIRED: Inelastic neutron scattering prediction for instantaneous results and experimental design

Inelastic neutron scattering (INS) has unique advantages in probing how atoms vibrate and how the vibrations propagate and interact. Such dynamic information is crucial in understanding various material properties, from heat capacity, thermal conductivity, phase transitions, and chemical reactions to more exotic quantum behavior. The analysis and interpretation of the INS spectra often start from a model structure of the sample, followed by a series of calculations to obtain the simulated spectra to compare with experiments. The conventional way to perform such calculations usually requires significant time, computing resources, and specialized expertise. Here, we present a new program named INSPIRED (Inelastic Neutron Scattering Prediction for Instantaneous Results and Experimental Design), which enables users to perform rapid INS simulations in several different ways on their personal computers in just a few clicks, with the crystal structure as the only input file. Specifically, the users can choose a pre-trained symmetry-aware neural network (coupled with an autoencoder) to predict the phonon density of states (DOS), 1D S(E) and 2D S(|Q|,E) spectra for any given structure. One can also choose an existing density functional theory (DFT) calculation from a database (containing over 12,000 crystals), and quickly obtain the simulated INS spectra for single crystals and powders. It is also possible to use pre-trained universal machine learning force fields to relax a given crystal structure, calculate the phonon dispersion and DOS, and, subsequently, the INS spectra. All these functions are implemented with a PyQt graphic user interface. Finally, we expect these new tools will benefit broad user communities and significantly improve the efficiency of experiment design, execution, and data analysis for INS.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Selective reduction of CO to acetaldehyde with CuAg electrocatalysts

Electrochemical CO reduction can serve as a sequential step in the transformation of CO 2 into multicarbon fuels and chemicals. In this study, we provide insights on how to steer selectivity for CO reduction almost exclusively toward a single multicarbon oxygenate by carefully controlling the catalyst composition and its surrounding reaction conditions. Under alkaline reaction conditions, we demonstrate that planar CuAg electrodes can reduce CO to acetaldehyde with over 50% Faradaic efficiency and over 90% selectivity on a carbon basis at a modest electrode potential of −0.536 V vs. the reversible hydrogen electrode. The Faradaic efficiency to acetaldehyde was further enhanced to 70% by increasing the roughness factor of the CuAg electrode. Density functional theory calculations indicate that Ag ad-atoms on Cu weaken the binding energy of the reduced acetaldehyde intermediate and inhibit its further reduction to ethanol, demonstrating that the improved selectivity to acetaldehyde is due to the electronic effect from Ag incorporation. These findings will aid in the design of catalysts that are able to guide complex reaction networks and achieve high selectivity for the desired product.

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

Accurate Thermochemistry of Complex Lignin Structures via Density Functional Theory, Group Additivity, and Machine Learning

A molecular-level understanding of lignin structures and bond dissociation energies could facilitate depolymerization technologies. Still, this information is currently limited due to the lack of databases and the simplification of surrogate models. Here, substitution effects on seven common linkages in lignin polymers are systematically investigated. An automated reaction network generator is employed to create a database of structures. A new group additivity (GA) model based on principal component analysis (PCA) descriptors is introduced and trained on gas-phase density functional theory data of 4100 species at the M06-2X/6-311++G(d,p) level. Hydrogen bonds, local steric, and nonaromatic ring contributions are also incorporated. Lastly, we improve the accuracy of the group additivity model to reach the G4 theory by computing a data set of 770 species at this level and using a data fusion approach.

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

Formation of U(VI) peroxide nanoclusters from cascade reactions with a persulfate radical initiator

Radiolysis of water in high radiation fields generates a variety of reactive oxygen species that influence the chemical behavior and complexation of hexavalent uranium. This study investigates the behavior of interaction of a uranyl cation (UO 2 2+ (VI)) with a series of free radicals that are formed in situ via activation of the free radical initiator persulphate (S 2 O 8 2− ), which releases both SO 4 ˙ − and ˙OH species in the solution. Electron Paramagnetic Resonance (EPR) and Raman spectroscopy were used to evaluate the presence of the hydroperoxyl radical (HO 2 ˙) and superoxide radicals (O 2 ˙ − ) that are formed within the solution through radical cascade reactions. In addition, a uranyl peroxide cluster solid (NaU 24 ) was crystallized and characterized using single crystal X-ray diffraction (SCXRD), vibrational spectroscopy, and EPR spectroscopy. The presence of the hydroperoxyl radical (HO 2 ˙) and superoxide radicals (O 2 ˙ − ) was also observed in the solid-state compound, but spectroscopic evidence suggests that it was associated with the Na + network and not the cluster itself. Density functional theory (DFT) calculations were also utilized to further confirm the radical species produced and determine the potential stabilization of radicals detected within the cluster and lattice.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Modified Energy Span Analysis of Catalytic Parallel Pathways and Selectivity

Mechanistic modeling provides vital insights into catalytic reactions. To analyze complex reaction networks with parallel pathways, we leverage the graph theory approach of the Energy Span Model (ESM) to develop a modified energy span analysis (MESA). A new method of cycle plots is proposed to perform reaction pathways analysis visually. We demonstrate this method on two published models: one describing carbon monoxide oxidation and the other simulating ethylene conversion to propanal via hydroformylation or ethane via hydrogenation. Fundamental insights explain kinetic observables, such as a reactant’s negative reaction order. General principles are revealed, such as rate-determining surface species being outside the primary reaction flux cycle and pathway selectivity being a purely kinetic property when reaction conditions are not near equilibrium. Lastly, we demonstrate MESA’s consistency with published microkinetic modeling results, highlighting this technique’s extension of the ESM to heterogeneous catalysts using collision theory to describe adsorption steps and concentration effects.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗