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At least 487 records · Page 27

Toward Realistic Models of the Electrocatalytic Oxygen Evolution Reaction

The electrocatalytic oxygen evolution reaction (OER) supplies the protons and electrons needed to transform renewable electricity into chemicals and fuels. However, the OER is kinetically sluggish; it operates at significant rates only when the applied potential far exceeds the reversible voltage. The origin of this overpotential is hidden in a complex mechanism involving multiple electron transfers and chemical bond making/breaking steps. Our desire to improve catalytic performance has then made mechanistic studies of the OER an area of major scientific inquiry, though the complexity of the reaction has made understanding difficult. While historically, mechanistic studies have relied solely on experiment and phenomenological models, over the past twenty years ab initio simulation has been playing an increasingly important role in developing our understanding of the electrocatalytic OER and its reaction mechanisms. In this Review we cover advances in our mechanistic understanding of the OER, organized by increasing complexity in the way through which the OER is modeled. We begin with phenomenological models built using experimental data before reviewing early efforts to incorporate ab initio methods into mechanistic studies. We go on to cover how the assumptions in these early ab initio simulations-no electric field, electrolyte, or explicit kinetics-have been relaxed. Through comparison with experimental literature, we explore the veracity of these different assumptions. We summarize by discussing the most critical open challenges in developing models to understand the mechanisms of the OER.

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Non-Boltzmann Effects in Chain Branching and Pathway Branching for Diethyl Ether Oxidation

Low-temperature (LT) engine applications have several potential benefits, including reduced emissions and increased efficiency. Attaining these benefits requires accurate kinetic modeling of LT chain branching, which depends heavily on ketohydroperoxide (KHP) decomposition. For diethyl ether (DEE), a promising biofuel, current estimates of the KHP decomposition rate constant are largely based on empirical fits to data. In this study, we investigate the most important KHP isomer in DEE LT oxidation by applying variable reaction coordinate transition state theory to the main pathway for KHP decomposition: OO bond fission to produce •OH and a keto-alkoxy radical, •OQ'O. We also use ab initio kinetics methods to investigate the decomposition of •OQ'O, where we find dominant branching to acetic acid, with the remaining flux going to CH 3 C(O)OCHO. Additionally, new time-resolved measurements of DEE and acetic acid concentrations during LT (450–600 K) DEE oxidation are obtained in a laser photolysis flow reactor coupled with multiplexed photoionization mass spectrometry. These new experimental data, along with jet-stirred reactor data in the literature, are compared with the predictions of a recent DEE mechanism (Tran et al. Proc. Comb. Inst. 2019, 37, 511-519) that was modified with the newly calculated ab initio rate constants for KHP and •OQ'O decomposition. The predictions of the modified mechanism are quite poor when compared to the experimental data; this is primarily due to the new KHP ⇌ •OQ'O + •OH rate constant, which is 1–2 orders of magnitude slower than empirical values employed in recent mechanisms. To reconcile the new KHP rate constant and the experimental data, we explore and quantify the possible role of non-Boltzmann (nB) reaction sequences. The nB reactions have a substantial effect on both the overall mechanism reactivity and the •OQ'O branching to acetic acid. We also provide guidance on the proper implementation of nB reactions in kinetic mechanisms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

A Rhenium Bis -tetramethylphenanthroline Catalyst for CO 2 Reduction to Formate

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] + .

Beverages↗

Hierarchical Galloaluminosilicate MFI Catalysts for Ethane Nonoxidative Dehydroaromatization

This study presented an approach to overcome diffusion limitation and imbalance between Brønsted and Lewis acidities that affect conversion and aromatic yield in alkane nonoxidative dehydroaromatization (DHA) conversion. Three different Ga-promoted ZSM-5 catalysts were synthesized for the ethane DHA reaction. The hierarchical galloaluminosilicate Meso-GaZSM-5 was prepared by the solid-state crystallization technique wherein Ga was introduced through the in situ synthesis process. The microporous galloaluminosilicate Micro-GaZSM-5 was synthesized by the hydrothermal technique wherein Ga was introduced in situ during zeolite formation. The hierarchically structured Ga-Meso-ZSM-5 was synthesized by the solid-state crystallization method, but the Ga was introduced by the incipient wetness method. Additional Pt was incorporated by the incipient wetness technique for all three catalysts to enhance the DHA activity. The solid-state crystallization technique created the hierarchical structure without using a template. With the in situ technique, Ga species replaced the Al framework, reducing the strong Brønsted acidity. Meanwhile, they created the extraframework GaO + , introducing the strong Lewis acid sites into catalysts. Due to better synergistic interaction between the oxidation state of metals, acidic properties, and catalyst porosity, Pt-Meso-GaZSM-5 showed higher C 2 H 6 conversion and aromatic selectivity. The improvement on diffusivity not only reduces the effect of channel blockage but also improves the utilization of active sites. As a result, this work sheds light on the direct, nonoxidative conversion of shale gas without going through the syngas route.

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Beyond Idealized Models of Nanoscale Metal Hydrides for Hydrogen Storage

Metal hydrides are attractive for compact, low-pressure hydrogen storage, yet a foundational understanding of factors governing their thermodynamics and kinetics is still lacking. Predictive modeling from the atomic to the microstructural scale plays a critical role in addressing these gaps, particularly for nanoscale materials, which promise improved performance but are difficult to probe. In this paper, we summarize strategies being developed within the Hydrogen Materials—Advanced Research Consortium (HyMARC) for going beyond conventional models to incorporate more complex physics, more realistic structures, and better approximation of operation conditions in simulations of nanoscale metal hydrides. We highlight four beyond-ideal factors that influence predicted performance: (1) surface anharmonic dynamics, (2) interface and surface energy penalties, (3) mechanical stress under confinement, and (4) the presence of native surface oxide. Approaches for addressing these factors are demonstrated on model materials representative of high-capacity hydrogen storage systems, and implications for understanding performance under operating conditions are discussed.

08 HYDROGEN↗

Effect of Spin–Orbit Coupling on Phonon-Mediated Magnetic Relaxation in a Series of Zero-Valent Vanadium, Niobium, and Tantalum Isocyanide Complexes

Spin-vibronic coupling leads to spin relaxation in paramagnetic molecules, and an understanding of factors that contribute to this phenomenon is essential for designing next-generation spintronics technology, including single-molecule magnets and spin-based qubits, wherein long-lifetime magnetic ground states are desired. We report spectroscopic and magnetic characterization of the isoelectronic and isostructural series of homoleptic zerovalent transition metal triad M(CNDipp) 6 (M = V, Nb, Ta; CNDipp = 2,6-diisopropylphenyl isocyanide) and show experimentally the significant increase in spin relaxation rate upon going from V to Nb to Ta. Correlated electronic calculations and first principle spin–phonon computations support the role of spin–orbit coupling in modulating spin–phonon relaxation. Furthermore, our results provide experimental evidence that increasing magnetic anisotropy through spin–orbit coupling interactions leads to increased spin–vibronic relaxation, which is detrimental to long spin lifetime in paramagnetic molecules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Rare Earth Nitrate Hybrid Double Perovskites [Me 4 N] 2 [MLn(NO 3 ) 6 ] (M = Na–Cs; Ln = La–Gd, ex. Pm)

An exploration of the synthetic and structural phase space of rare earth hybrid double perovskites A 2 B'BX 6 (A = organocation, B' = M + , B = M 3+ , X = molecular bridging anion) that include X = NO 3 – and B' = alkali metal is reported, complementing earlier studies of the [Me 4 N] 2 [KB(NO 3 ) 6 ] (B = Am, Cm, La–Nd, Sm–Lu, Y) (Me 4 N = (CH 3 ) 4 N + ) compounds. In the present efforts, the synthetic phase space of these systems is explored by varying the identity of the alkali metal ion at the B'-site. Herein, we report three new series of the form [Me 4 N] 2 [B'B(NO 3 ) 6 ] (B = La–Nd, Sm–Gd; B' = Na, Rb, Cs). The early members of the Na-series crystallize in the trigonal space group R3̅ from La to Nd where a phase transition occurs in the phase between 273 and 300 K, going from R3̅ to the high-symmetry, cubic space group Fm3̅m. The preceding trigonal members of the Na-series also undergo phase transitions to cubic symmetry at temperatures above 300 K, establishing a decreasing trend in the phase-transition temperature. The remainder of the Na-series, as well as the Rb- and Cs-series, all crystallize in Fm3̅m at 300 K. The temperature-dependent phase behavior of the synthesized phases is studied via variable-temperature spectroscopic methods and high-resolution powder X-ray diffractometry. All phases were characterized via single-crystal and powder X-ray diffraction and Fourier transform infrared (FT-IR) and Raman spectroscopic methods. These results demonstrate the versatility of the perovskite structure type to include rare earth ions, nitrate ions, and a suite of alkali metal ions and serve as a foundation for the design of functional rare earth hybrid double perovskite materials such as those possessing useful multiferroic, optical, and magnetic properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Adaptive Ensemble Refinement of Protein Structures in High Resolution Electron Microscopy Density Maps with Radical Augmented Molecular Dynamics Flexible Fitting

Recent advances in cryo-electron microscopy (cryo-EM) have enabled modeling macromolecular complexes that are essential components of the cellular machinery. The density maps derived from cryo-EM experiments are often integrated with manual, knowledge or artificial intelligence driven, and physics-guided computational methods to build, fit, and refine molecular structures. Going beyond a single stationary- structure determination scheme, it is becoming more common to interpret the experimental data with an ensemble of models, which contributes to an average observation. Hence, there is a need to decide on the quality of an ensemble of protein structures on-the-fly, while refining them against the density maps. Here, we introduce such an adaptive decision making scheme during the molecular dynamics flexible fitting (MDFF) of biomolecules. Using RADICAL-Cybertools, and the new RADICAL augmented MDFF implementation (R-MDFF) is examined in high-performance computing environments for refinement of two protein systems, Adenylate Kinase and Carbon Monoxide Dehydrogenase. For the test cases, use of multiple replicas in flexible fitting with adaptive decision making in R-MDFF improves the overall correlation to the density by 40% relative to the refinements of the brute-force MDFF. The improvements are particularly significant at high, 2 - 3 Å, map resolutions. More importantly, the ensemble model captures key features of biologically relevant molecular dynamics that is inaccessible to a single-model interpretation. Finally, the pipeline is applicable to systems of growing sizes, which is demonstrated using ensemble refinement of capsid proteins from Chimpanzee adenovirus. The overhead for decision making remaining low and robust to computing environments. The software is publicly available on GitHub and includes a short user guide to install the R-MDFF on different computing environments, from local Linux based workstations to High Performance Computing (HPC) environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Ligand-Based Compound Activity Prediction via Few-Shot Learning

Predicting the activities of new compounds against biophysical or phenotypic assays based on the known activities of one or a few existing compounds is a common goal in early stage drug discovery. This problem can be cast as a “few-shot learning” challenge, and prior studies have developed few-shot learning methods to classify compounds as active versus inactive. However, the ability to go beyond classification and rank compounds by expected affinity is more valuable. We describe Few-Shot Compound Activity Prediction (FS-CAP), a novel neural architecture trained on a large bioactivity data set to predict compound activities against an assay outside the training set, based on only the activities of a few known compounds against the same assay. Our model aggregates encodings generated from the known compounds and their activities to capture assay information and uses a separate encoder for the new compound whose activity is to be predicted. The new method provides encouraging results relative to traditional chemical-similarity-based techniques as well as other state-of-the-art few-shot learning methods in tests on a variety of ligand-based drug discovery settings and data sets.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Quantum Simulations of Fermionic Hamiltonians with Efficient Encoding and Ansatz Schemes

Here, we propose a computational protocol for quantum simulations of fermionic Hamiltonians on a quantum computer, enabling calculations on spin defect systems which were previously not feasible using conventional encodings and a unitary coupled-cluster ansatz of variational quantum eigensolvers. We combine a qubit-efficient encoding scheme mapping Slater determinants onto qubits with a modified qubitcoupled cluster ansatz and noise-mitigation techniques. Our strategy leads to a substantial improvement in the scaling of circuit gate counts and in the number of required qubits, and to a decrease in the number of required variational parameters, thus increasing the resilience to noise. We present results for spin defects of interest for quantum technologies, going beyond minimum models for the negatively charged nitrogen vacancy center in diamonds and the double vacancy in 4H silicon carbide (4H-SiC) and tackling a defect as complex as negatively charged silicon vacancy in 4H-SiC for the first time.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Integrated Quantum-Classical Protocol for the Realistic Description of Solvated Multinuclear Mixed-Valence Transition-Metal Complexes and Their Solvatochromic Properties

Linear cyanide-bridged polymetallic complexes, which undergo photoinduced metal-to-metal charge transfer, represent prototypical systems for studying long-range electron-transfer reactions and understanding the role played by specific solute–solvent interactions in modulating the excited-state dynamics. Here, to tackle this problem, while achieving a statistically meaningful description of the solvent and of its relaxation, one needs a computational approach capable of handling large polynuclear transition-metal complexes, both in their ground and excited states, as well as the ability to follow their dynamics in several environments up to nanosecond time scales. Here, we present a mixed quantum classical approach, which combines large-scale molecular dynamics (MD) simulations based on an accurate quantum mechanically derived force field (QMD-FF) and self-consistent QMD polarized point charges, with IR and UV–vis spectral calculations to model the solvation dynamics and optical properties of a cyano-bridged trinuclear mixed-valence compound (trans-[(NC) 5 Fe III (μ-CN)Ru II (pyridine) 4 (μ-NC)Fe III (CN) 5 ] 4– ). We demonstrate the reliability of the QMD-FF/MD approach in sampling the solute conformational space and capturing the local solute–solvent interactions by comparing the results with higher-level quantum mechanics/molecular mechanics (QM/MM) MD reference data. The IR spectra calculated along the classical MD trajectories in different solvents correctly predict the red shift of the CN stretching band in the aprotic medium (acetonitrile) and the subtle differences measured in water and methanol, respectively. By explicitly including the solvent molecules around the cyanide ligands and calculating the thermal averaged absorption spectra using time-dependent density functional theory calculations within the Tamm–Dancoff approximation, the experimental solvatochromic shift is quantitatively reproduced going from water to methanol, while it is overestimated for acetonitrile. This discrepancy can likely be traced back to the lack of important dispersion interactions between the solvent cyano groups and the pyridine substituents in our micro solvation model. The proposed protocol is applied to the ground state in water, methanol, and acetonitrile and can be flexibly generalized to study excited-state nonequilibrium solvation dynamics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Beyond Single-Reference Fixed-Node Approximation in Ab Initio Diffusion Monte Carlo Using Antisymmetrized Geminal Power Applied to Systems with Hundreds of Electrons

Diffusion Monte Carlo (DMC) is an exact technique to project out the ground state (GS) of a Hamiltonian. Since the GS is always bosonic, in Fermionic systems, the projection needs to be carried out while imposing antisymmetric constraints, which is a nondeterministic polynomial hard problem. In practice, therefore, the application of DMC on electronic structure problems is made by employing the fixed-node (FN) approximation, consisting of performing DMC with the constraint of having a fixed, predefined nodal surface. How do we get the nodal surface? The typical approach, applied in systems having up to hundreds or even thousands of electrons, is to obtain the nodal surface from a preliminary mean-field approach (typically, a density functional theory calculation) used to obtain a single Slater determinant. This is known as single reference. In this paper, we propose a new approach, applicable to systems as large as the C 60 fullerene, which improves the nodes by going beyond the single reference. In practice, we employ an implicitly multireference ansatz (antisymmetrized geminal power wave function constraint with molecular orbitals), initialized on the preliminary mean-field approach, which is relaxed by optimizing a few parameters of the wave function determining the nodal surface by minimizing the FN-DMC energy. We highlight the improvements of the proposed approach over the standard single-reference method on several examples and, where feasible, the computational gain over the standard multireference ansatz, which makes the methods applicable to large systems. We also show that physical properties relying on relative energies, such as binding energies, are affordable and reliable within the proposed scheme.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Static Subspace Approximation for Random Phase Approximation Correlation Energies: Implementation and Performance

Developing theoretical understanding of complex reactions and processes at interfaces requires using methods that go beyond semilocal density functional theory to accurately describe the interactions between solvent, reactants and substrates. Methods based on many-body perturbation theory, such as the random phase approximation (RPA), have previously been limited due to their computational complexity. However, this is now a surmountable barrier due to the advances in computational power available, in particular through modern GPU-based supercomputers. In this work, we describe the implementation of RPA calculations within BerkeleyGW and show its favorable computational performance on large complex systems relevant for catalysis and electrochemistry applications. Our implementation builds off of the static subspace approximation which, by employing a compressed representation of the frequency dependent polarizability, enables the evaluation of the RPA correlation energy with significant acceleration and systematically controllable accuracy. We find that the computational cost of calculating the RPA correlation energy scales only linearly with system size for systems containing up to 50 thousand bands, and is expected to scale quadratically thereafter. We also show excellent strong scaling results across several supercomputers, demonstrating the performance and portability of this implementation.

algorithmic development↗

Reducing the Cost of Energy Differences in Variational Monte Carlo with Spotlight Sampling

Here, we investigate an approximate sampling scheme that can significantly reduce the cost scaling of variational Monte Carlo when it is employed to predict the energy differences associated with local chemical changes. Inspired by side-chaining and embedding methods, this spotlight sampling approach adopts an approximate fragmented Hamiltonian and correlated sampling to reduce cost scaling to the point that it is essentially linear with system size, with the potential to go sublinear if certain conditions are met. In tests on bond stretching energies in alcohols, hydrogen dimer chains, and molecules with various degrees of π-system delocalization, we observe the anticipated linear scaling and an explicit cost crossover with standard variational Monte Carlo.

Bumann, Sonja [University of California, Berkeley,↗

Atomic-Level Features for Kinetic Monte Carlo Models of Complex Chemistry from Molecular Dynamics Simulations

The high computational cost of evaluating atomic interactions recently motivated the development of computationally inexpensive kinetic models, which can be parametrized from MD simulations of complex chemistry of thousands of species or other processes and accelerate the prediction of the chemical evolution by up to four order of magnitude. Such models go beyond the commonly employed potential energy surface fitting methods in that they are aimed purely at describing kinetic effects. So far, such kinetic models utilize molecular descriptions of reactions and have been constrained to only reproduce molecules previously observed in MD simulations. Therefore, these descriptions fail to predict the reactivity of unobserved molecules, for example in the case of large molecules or solids. In this work, we propose a new approach for the extraction of reaction mechanisms and reaction rates from MD simulations, namely the use of atomic-level features. Using the complex chemical network of hydrocarbon pyrolysis as example, it is demonstrated that kinetic models built using atomic features are able to explore chemical reaction pathways never observed in the MD simulations used to parametrize them, a critical feature to describe rare events. Atomic-level features are shown to construct reaction mechanisms and estimate reaction rates of unknown molecular species from elementary atomic events. Through comparisons of the model ability to extrapolate to longer simulation timescales and different chemical compositions than the ones used for parameterization, it is demonstrated that kinetic models employing atomic features retain the same level of accuracy and transferability as the use of features based on molecular species, while being more compact and parametrized with less data. We also find that atomic features can better describe the formation of large molecules enabling the simultaneous description of small molecules and condensed phases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Modeling the Solvation and Acidity of Carboxylic Acids Using an Ab Initio Deep Neural Network Potential

Formic and acetic acid constitute the simplest of carboxylic acids, yet they exhibit fascinating chemistry in the condensed phase such as proton transfer and dimerization. The go-to method of choice for modeling these rare events have been accurate but expensive ab-initio molecular dynamics simulations. Here, we present a deep neural network potential trained using accurate ab-initio data that can be used in tandem with enhanced-sampling methods to perform an efficient exploration of the free-energy surface of aqueous solutions of weak carboxylic acids. In particular, we show that our model captures proton dissociation and provides a good estimate of the pK a , as well as the dimerization of formic and acetic acid. This provides a suitable starting point for applications in different research areas where computational efficiency coupled with the accuracy of ab-initio methods is required.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Comparative Study of Neutral and Cationic Sn 2 H 2 : Toward Laboratory Detection of the Cation

Group 14 M 2 H 2 isomers (M = Si, Ge, Sn, and Pb) have attracted interest due to their radically differing electronic structures from acetylene. To better understand the Sn–H interactions of the neutral and cationic Sn 2 H 2 structures, we present the most rigorous study of these systems to date. CCSD(T)/cc-pwCVTZ harmonic frequencies are presented as the first predictions for the neutral and cationic species to date. CCSDT(Q)/CBS relative energies are reported using the focal point approach, confirming the butterfly isomer as the global minimum on the potential energy surface for both the neutral and cationic species. In all, there exist 7 minima and 15 transition states. NBO analysis is also performed to elucidate the changes in bond order going from neutral to cation across all isomers of Sn 2 H 2 . Our results provide insights into the important Sn–H interaction and provide guidance for future work that may detect Sn 2 H$^{+}_{2}$ in the laboratory for the first time.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Energetic and Electronic Properties of UX +/0/– for X = Li and Be and Comparison of the Properties of the Uranium Atom Binding to 2nd Row Elements Li–F

The bonding and spectroscopic properties of ULi +/0/– and UBe +/0/– to complete the series for UX +/0/– for X = Li to F were investigated by high-level ab initio SO-CASPT2 and CCSD(T) electronic structure calculations. The low-lying spin–orbit states were obtained at the SA-CASPT2/aQ-PP level; bond dissociation energies (BDEs), ionization energies (IEs), adiabatic electronic affinities (AEAs), and vertical detachment energies (VDEs) were calculated at the Feller-Peterson-Dixon (FPD) level. A dense manifold of low-lying states was predicted for ULi +/0/– and UBe +/0/– . Here, the calculated BDEs for ULi (37.7 kJ/mol) and UBe (8.0 kJ/mol) show that UBe is weakly bound. For redox processes, the BDEs increased for ULi + (109.3 kJ/mol), ULi – (47.4 kJ/mol), UBe + (35.6 kJ/mol), and UBe – (72.3 kJ/mol). The IE(ULi) = 4.650 eV is lower than IE(Li); the IE(UBe) = 5.901 eV is close to the IE(U) and to the IEs of UB, UC, UN, UO, and UF. The AEAs of ULi (0.708 eV) and UBe (0.989 eV) are lower than those for UB, UC, UN, and UO but higher than that for EA(UF). Natural bond orbital (NBO) calculations show that ULi has the 5f 3 6d 1 7s 2 configuration for U and 2s 1 for Li, with a small partial negative charge slightly delocalized on U. UBe arises from the U(5f 3 6d 1 7s 2 ) and Be(2s 2 ) electron configurations with no charge separation. The same calculations were made for WX (X = Li, Be, C–F) to enable detailed comparisons of the properties for UX with WX (X = Li–F). For WX, BDE(WX) is higher than that for UX for X = Li to N and lower than BDE(UX) for X = O and F, mostly due to the higher IE of W than U as ionic character becomes more important going from Li to F.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗