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The presentation reports enhanced Mo catalysts performance prepared with metal promoters. DFT and Raman studies are utilized to interpret the activity of select cations in the MDA reaction. Binding energies of promoters and their preference site location to internal surface of ZSM-5 are discussed in context of improved aromatic yield.
This study employs Density Functional Theory (DFT) and Molecular Dynamics (MD) simulations to investigate interactions between water molecules and Poly(N-isopropylacrylamide) (PNIPAM). DFT reveals preferential water binding sites, with enhanced binding energy observed in the linker zone. Quantum Theory of Atoms in Molecules (QTAIM) and electron localization function (ELF) analyses highlight the roles of hydrogen bonding and steric hindrance. MD simulations unveil temperature-dependent hydration dynamics, with structural transitions marked by changes in the radius of gyration (Rg) and the radial distribution function (RDF), aligning with DFT findings. Our work goes beyond prior studies by combining a DFT, QTAIM and MD simulations approach across different PNIPAM monomer-to-30mer structures. It introduces a systematic quantification of pseudo-saturation thresholds and explores water clustering dynamics with structural specificity, which have not been previously reported in the literature. These novel insights establish a more complete molecular-level picture of PNIPAM hydration behavior and temperature responsiveness, emphasizing the importance of amide hydrogen and carbonyl oxygen sites in hydrogen bonding, which weakens above the lower critical solution temperature (LCST), resulting in increased hydrophobicity and paving the way for understanding water sorption mechanisms, offering guidance for future applications such as dehumidification and atmospheric water harvesting.
Incorporation of SiC/SiC ceramic matrix composite (CMC) hot section components into aircraft engines promises to increase efficiency and safety. However, SiC/SiC CMCs are subject to water vapor-induced oxidation and recession at the high temperatures of engine operation, and thus environmental barrier coatings (EBCs) are required to reduce this degradation and enable their widespread adoption. An understanding of EBCs failure mechanisms, including thermochemical and thermomechanical mechanisms, is essential as coating degradation leads to reduced CMC component service life. Computational modeling approaches can provide insight into EBC material properties important for coating design. However, density functional theory (DFT) is computationally expensive and atomistic potentials are lacking for materials of interest. In this work, we utilize a machine learning approach and DFT training data to parameterize atomistic potentials for two candidate EBC materials, Y2Si2O7 and Yb2Si2O7. These potentials enable near DFT-accurate calculations of thermodynamic and thermomechanical properties essential to EBC design.
This is the poster put together for the FY 2020 LDRD Poster Session for the concluding LDRD# 19P43-006FP: Spectroscopic Studies of Covalency in Minor Actinide-Soft Donor Ligand Complexes.
Projection‐based embedding theory (PBET) is used to calculate and assess the challenging spin‐crossover energies for a selection of small Fe‐containing systems by embedding the metal center into the frozen potential of the ligands. MP2, CCSD, and CCSD(T) are embedded in potentials from the SCAN and r 2 SCAN functionals and compared with the canonical values for the constituent methods and previously reported reference values. Considering the PBET calculations as a correction for the underlying DFT, the embedding calculations are able to provided improvement for most cases. In some cases, the PBET methods are able to compensate for limitations in the wave function methods and produce results similar to more rigorous calculations from the literature. For the systems with spin‐crossover energies near zero, the current methodology fails to provide consistent improvement. In conclusion, the isolated recalculation of the electronic structure around the metal center when embedded into a DFT treatment of the ligand field shows promise as a pragmatic and lower cost treatment compared to the canonical treatment of the whole system of the difficult class of spin‐crossover complexes.
In this study, Quantum Chemical (QC) calculations, utilizing Density Functional Theory (DFT), were performed to investigate the mechanistic aspects of the chemoselective catalyzed reaction of [Cp*Rh(bpy)H] + with the biomimetic NAD + analogues, N-benzylnicotinamide triflate, 1, and β-nicotinamide ribose-5'-methyl phosphate, 2, in the conversion to their 1,4-NADH analogues, 1,4-dihydro-N-benzylnicotinamide, 4, and β-1,4-dihydronicotinamide-5'-ribose methyl phosphate, 5. This reaction was in tandem with the 1,4-NADH dependent HLADH-Zn(II)- catalyzed reduction of achiral ketones to chiral S-alcohols. The [Cp*Rh(bpy)H] + complex, and not its equilibrium tautomer, [η 4 -Cp*HRh(bpy)] + , was found to control the hydride transfer during the biomimetic NAD + /1,4-NADH conversion, through the non-covalent interactions of the biomimetic co-factors with [Cp*Rh(bpy)H] + . The thermodynamics and kinetics for the chiral reduction of the Zn(II) bound ketones, 2-pentanone and 4-phenyl-2-butanone, with co-factor, 4, catalyzed by Zn(SCH 3 ) 2 (Imidazole), a core model of the Zn(II)-based catalytic center of HLADH, was also investigated by the evaluation of two possible reaction pathways: (1) formation of a ZnH from the C4-H hydride transfer of co-factor, 4, followed by reaction of the postulated ZnH with the bound 2-pentanone or 4-phenyl-2-butanone substrate, and (2), the direct C4-H transfer to the bound achiral ketone substrates, to provide the dominant chiral alcohols, S-2-pentanol or S-4-phenyl-2-butanol. The latter pathway was found most viable, and DFT calculations also revealed an essential η 2 -coordination of the 5,6 double bond of co-factor, 4, to the HLADH-Zn(II) metal ion center, upon imidazole decomplexation, providing an asymmetric differentiation of S-η 2 -5,6-1,4-NADH-Zn(II) binding. A proposed new paradigm for the Zn(II)'s non-innocent role in the HLADH-Zn(II) biocatalysis reduction mechanism, for enantioselective hydride transfer to a Zn(II) bound ketone, providing S-alcohols.
Thermocatalytic decomposition (TCD) of methane can produce hydrogen and valuable nanocarbon co-products with low to near-zero CO2 emission. In this study, a series of Pd promoted Ni catalysts, prepared with various Ni/Pd ratios on a CNT support, were evaluated for methane TCD performance. Characterization and calculations using density functional theory (DFT) were carried out to elucidate the activity–structure relationship and growth mechanism of carbon nanomaterials. It was found that the methane conversion and stability of the catalysts were highly dependent on the Ni/Pd ratio and reaction temperature. DFT calculations revealed that the diffusion of carbon in the metal sublayer required for CNT growth was more favorable in the Ni–Pd alloy lattice suggesting that the buildup of carbon in the metal alloy sublayer facilitated the formation of CNTs and CNFs. A cyclic reaction–regeneration process for self-sustained TCD was experimentally demonstrated. In each cycle, a portion of the separated CNT product was used to re-synthesize the Ni–Pd/CNT catalyst for use in the next reaction cycle. After five cycles of operation, the CH4 conversion, morphology and crystallinity of the carbon product remained unchanged.
We report on the impact of magnetoelastic coupling on the magnetocaloric properties of LaFe 11.4 Si 1.6 H 1.6 in terms of the vibrational (phonon) density of states (VDOS), which we determined with 57 Fe nuclear resonant inelastic x-ray scattering (NRIXS) measurements and with density functional theory (DFT) based first-principles calculations in the ferromagnetic (FM) low-temperature and paramagnetic (PM) high-temperature phase. In experiments and calculations, we observe pronounced differences in the shape of the Fe-partial VDOS between nonhydrogenated and hydrogenated samples. Furthermore, this shows that hydrogen not only shifts the temperature of the first-order phase transition, but also affects the elastic response of the Fe subsystem significantly. In turn, the anomalous redshift of the Fe VDOS, observed by going to the low-volume PM phase, survives hydrogenation. As a consequence, the change in the Fe-specific vibrational entropy ΔS lat across the phase transition has the same sign as the magnetic and electronic contribution. DFT calculations show that the same mechanism, which is a consequence of the itinerant electron metamagnetism associated with the Fe subsystem, is effective in both the hydrogenated and the hydrogen-free compounds. Although reduced by 50% as compared to the hydrogen-free system, the measured change ΔS lat of 3.2 ± 1.9)J/ kg K across the FM-to-PM transition contributes with ~35 % significantly and cooperatively to the total isothermal entropy change ΔS iso . Hydrogenation is observed to induce an overall blueshift of the Fe VDOS with respect to the H-free compound; this effect, together with the enhanced Debye temperature observed, is a fingerprint of the hardening of the Fe sublattice by hydrogen incorporation. In addition, the mean Debye velocity of sound of LaFe 11.4 Si 1.6 H 1.6 was determined from the NRIXS and the DFT data.
The selective hydrogenation of biomass-derived hydroxymethyl furfural (HMF)-acetone-HMF (HAH) presents an alternative route to producing highly functionalized polyesters and polyurethanes. While HAH undergoes furan ring hydrogenation over Pd, Ru, and Ni catalysts, furan ring hydrogenation is not observed over Cu. Herein, we combine reaction kinetics experiments and density functional theory calculations to elucidate the selective hydrogenation behavior of HAH over Cu catalysts. We identified furfural acetone (FAc) as a suitable surrogate for modeling HAH hydrogenation over Cu surfaces and performed reaction kinetics experiments between temperatures of 313–393 K and a H 2 partial pressure of 55 bar. Similar to the behavior of HAH, hydrogenation of FAc follows a consecutive two-step hydrogenation pathway over Cu and does not undergo furan ring hydrogenation. The apparent activation energy barriers for hydrogenation of the aliphatic double bond (0.58 eV) and carbonyl (0.43 eV) of FAc measured in a continuous flow reactor setup are consistent with those reported in prior studies for batch HAH hydrogenation. Reaction orders with respect to each reactant, including H 2 and FAc, were determined to be nearly one. Density functional theory (DFT; GGA-PBE-D3) calculations on Cu(111) showed that the hydrogenation of the aliphatic double bond of FAc is more facile than the hydrogenation of the furan ring, which displays weak interactions with the Cu surface. We determined an apparent activation energy barrier for FAc hydrogenation (0.58 eV) that agreed with our DFT predictions (highest barrier for FAc hydrogenation of 0.57 eV). Finally, our DFT calculations further show that weak interactions between the furan ring and Cu surface are responsible for the selective hydrogenation behavior.
INQ is an engine for electronic structure calculations. INQ is implemented as a C++ library of components designed to build a parallel, portable code for electronic structure calculations, including ground state and excited states. INQ is based on density functional theory (DFT). It can calculate ground state properties in DFT, and also excited states using time-dependent DFT (TDDFT) in real-time and linear-response. It implements different approximations for the exchange and correlation part: semi-local functionals like LDAs, GGAs and metaGGAs, and hybrid functionals that are implemented through the ACE approach for fast execution.
EA-EOM-CCSD electron affinities and LUMO energies of various Kohn–Sham density functional theory (DFT) methods are calculated for an a priori IP benchmark set of 64 small, closed-shell molecules. The purpose of these calculations was to investigate whether the QTP KS-DFT functionals can emulate EA-EOM-CC with only a mean-field approximation. We show that the accuracy of DFT—relative to CCSD—improves significantly when elements of correlated orbital theory are introduced into the parameterization to define the QTP family of functionals. In particular, QTP(02), which has only a single range separation parameter, provides results accurate to a MAD of <0.15 eV for the whole set of 64 molecules compared to EA-EOM-CCSD, far exceeding the results from the non-QTP family of density functionals.
To cope with legacy greenhouse gas emissions and to achieve net-zero emissions by 2050, the U.S. Department of Energy (DOE) is funding efforts to develop direct air capture (DAC), a method for removing CO2 directly from air. Metal organic frameworks (MOFs) have been studied as DAC sorbent materials because of their structural and chemical diversity. Thermodynamic calculations using classical force fields are often used to screen MOFs for their performance in separations such as CO2 capture. Machine-learned force fields (MLFFs) can use machine learning to form quantitative relationships between a material’s chemical structure and the forces and energies predicted by more accurate quantum mechanical calculations, such as dispersion-corrected density functional theory (DFT). These descriptions of forces and energies can be used to improve the accuracy of adsorption calculations. In this work, MLFF models were developed for MOFs to achieve DFT-level accuracy for the forces and energies associated with MOF flexibility and CO2 adsorption. These methods were parametrized based on thousands of DFT calculations of CO2 in flexible MOFs and used to predict MOF structural properties as well as CO2 adsorption properties.
A low-cost approach for stochastically sampling static exchange during time-dependent Hartree–Fock-type propagation is presented. This enables the use of an excellent hybrid density functional theory (DFT) starting point for stochastic GW quasiparticle energy calculations. Generalized Kohn–Sham molecular orbitals and energies, rather than those of a local-DFT calculation, are used for building the Green function and effective Coulomb interaction. The use of an optimally tuned hybrid diminishes the starting point dependency in one-shot stochastic GW, effectively avoiding the need for self-consistent GW iterations.
Understanding laser–target coupling is of the utmost importance for achieving high performance in laser-direct-drive (LDD) inertial confinement fusion (ICF) experiments. Thus, accurate modeling of electron transport and deposition through ICF-relevant materials and conditions is necessary to quantify the total thermal conduction and ablation. The stopping range is a key transport quantity used in thermal conduction models; in this work, we review the overall role that the electron mean free path (MFP) plays in thermal conduction and hydrodynamic simulations. The currently used modified Lee–More model employs various physics approximations. We discuss a recent model that uses time-dependent density functional theory (TD-DFT) to eliminate these approximations in both the calculation of the electron stopping power and corresponding MFP in conduction zone polystyrene (CH) plasma. In general, the TD-DFT calculations showed a larger MFP (lower stopping power) than the standard modified Lee–More model. Using the TD-DFT results, an analytical model for the electron deposition range, λTD−DFT(ρ,T,K), was devised for CH plasmas between ρ=[0.05−1.05] g/cm3, kBT=[100−1000] eV. We implemented this model into LILAC, for simulations of a National Ignition Facility-scale LDD implosion and compared key physics quantities to ones obtained by simulations using the standard model. The implications of the obtained results and the path moving forward to calculate this same quantity in conduction-zone deuterium–tritium plasmas are further discussed, to hopefully close the understanding gap for laser target coupling in LDD-ICF simulations.
Resolving anion configurations in heteroanionic materials is crucial for understanding and controlling their properties. For anion-disordered oxyfluorides, conventional Bragg diffraction cannot fully resolve the anionic structure, necessitating alternative structure determination methods. We have investigated the anionic structure of anion-disordered cubic (ReO 3 -type) TiOF 2 using X-ray pair distribution function (PDF), 19 F MAS NMR analysis, density functional theory (DFT), cluster expansion modeling, and genetic-algorithm structure prediction. Our computational data predict short-range anion ordering in TiOF 2 , characterized by predominant cis-[O 2 F 4 ] titanium coordination, resulting in correlated anion disorder at longer ranges. To validate our predictions, we generated partially disordered supercells using genetic-algorithm structure prediction and computed simulated X-ray PDF data and 19 F MAS NMR spectra, which we compared directly to experimental data. To construct our simulated 19 F NMR spectra, we derived new transformation functions for mapping calculated magnetic shieldings to predicted magnetic chemical shifts in titanium (oxy)fluorides, obtained by fitting DFT-calculated magnetic shieldings to previously published experimental chemical shift data for TiF 4 . We find good agreement between our simulated and experimental data, which supports our computationally predicted structural model and demonstrates the effectiveness of complementary experimental and computational techniques in resolving anionic structure in anion-disordered oxyfluorides. From additional DFT calculations, we predict that increasing anion disorder makes lithium intercalation more favorable by, on average, up to 2 eV, highlighting the significant effect of variations in short-range order on the intercalation properties of anion-disordered materials.
Increased demand for high-performance permanent magnets in the electric vehicle and wind-turbine industries has prompted the search for cost-effective alternatives. Discovering magnetic materials with the desired intrinsic and extrinsic permanent magnet properties presents a significant challenge to researchers because of issues with the global supply of rare-earth elements, material stability, and a low maximum magnetic energy product BH max . While first-principles density functional theory (DFT) predicts materials’ magnetic moments, magnetocrystalline anisotropy constants, and exchange interactions, it cannot compute extrinsic properties such as coercivity (H c ). Although it is possible to calculate H c theoretically with micromagnetic simulations, the predicted value is larger than the experiment by almost an order of magnitude due to the Brown paradox. To circumvent these issues, we employ machine-learning (ML) methods on an extensive database obtained from experiments, DFT calculations, and micromagnetic modeling. The use of a large experimental dataset enables realistic H c predictions for materials such as Ce-doped Nd 2 Fe 14 B, comparing favorably against micromagnetically simulated coercivities. Remarkably, our ML model accurately identifies uniaxial magneto-crystalline anisotropy as the primary contributor to H c . With DFT calculations, we predict the Nd-site-dependent magnetic anisotropy behavior in Nd 2 Fe 14 B, confirming that Nd 4g sites mainly contribute to uniaxial magnetocrystalline anisotropy, and also calculate the Curie temperature (T c ). Finally, both calculated results are in good agreement with the experiments. The coupled experimental dataset and ML modeling with DFT input predict H c with far greater accuracy and speed than was previously possible using micromagnetic modeling. Further, we reverse engineer the grain-boundary and intergrain exchange coupling with micromagnetic simulations by employing the ML predictions.
Recently developed free-energy density functional theory (DFT)-based methodology for optical property calculations of warm dense matter has been applied for studying L-shell opacity of iron and chromium at T = 182 eV. We use Mermin–Kohn–Sham density functional theory with a ground-state and a fully temperature-dependent generalized gradient approximation exchange-correlation (XC) functionals. It is demonstrated that the role of XC at such a high-T is negligible due to the total free-energy of interacting system being dominated by the noninteracting free-energy term in agreement with estimations for the homogeneous electron gas. Furthermore, our DFT predictions are compared to the radiative emissivity and opacity of dense plasmas model, to the real-space Greens function method, and to experimental measurements. Good agreement is found between all three theoretical methods, and in the bound–continuum region for Cr when compared to the experiment, while the discrepancy between direct DFT calculations and the experiment for Fe remains essentially the same as for plasma-physics models.
Nonvolatile resistive switching in two-dimensional monolayers opens a new avenue for high-density memory/computing devices. However, questions remain as to why the current on/off ratio and switching voltage vary significantly among different devices. Here, we simulate electronic transport of large systems consisting of a h-BN monolayer sandwiched by gold electrodes, enabled by an implementation of the nonequilibrium Green’s function method in the exascale density functional theory (DFT) code: Real-space MultiGrid. Systematic calculations reveal that the wide range of on/off ratios is due to variations in interface distances between the electrode and h-BN that significantly modulate their wavefunction overlap. In addition, DFT calculations demonstrate that the energy barrier of a gold atom dissociating from the electrode to h-BN increases dramatically with the interface distance, thereby explaining the strong dependence of the switching voltage on distance. Our work demonstrates the significance of interface distance in governing the current on/off ratio and switching voltage.