Fe−TPP Coordination Network with Metalloporphyrinic Neutral Radicals and Face-to-Face and Edge-to-Face π−π Stacking

Artículo científico Inorg. Chem. 2013, 52, 8074−8081

Theoretical and experimental absorption spectra study of [(FeTPPbipy]·)n provided by TD-DFT calculations

Comunicación a congreso (póster): 11th European Biological Inorganic Chemistry Conference EUROBIC 11. 12-16 September, 2012 - Granada (Spain)

Photorelease of Pyridyl Esters in Organometallic Ru(II) Arene Complexes

New Ru(II) arene complexes of formula [((6)-p-cym)Ru(N-N)(X)](2+) (where p-cym = para-cymene, N-N = 2,2'-bipyrimidine (bpm) or 2,2'-bipyridine (bpy) and X = m/p-COOMe-Py, 1-4) were synthesised and characterized, including the molecular structure of complexes [((6)-p-cym)Ru(bpy)(m-COOMe-Py)](2+) (3) and [((6)-p-cym)Ru(bpy)(p-COOMe-Py)](2+) (4) by single-crystal X-ray diffraction. Complexes 1-4 are stable in the dark in aqueous solution over 48 h and photolysis studies indicate that they can photodissociate the monodentate m/p-COOMe-Py ligands selectively with yields lower than 1%. DFT and TD-DFT calculations (B3LYP/LanL2DZ/6-31G**) performed on singlet and triplet states pinpoint a low-energy triplet state as the reactive state responsible for the selective dissociation of the monodentate pyridyl ligands.

A influência dos elementos da primeira série de transição nas propriedades eletrônicas de aglomerados de paládio: um estudo baseado na Teoria do Funcional de Densidade (TFD)

Na área da catálise, interações de moléculas pequenas como H2,N2,O2, CO, NO etc, com aglomerados metálicos, têm nas suas adsorções químicas etapa fundamental nas atividades catalíticas. O paládio está entre os metais mais empregados em catalisadores usados em muitos processos catalíticos heterogêneos. Neste presente trabalho foi realizada uma série de cálculos baseado na Teoria do Funcional de Densidade (TFD ou DFT) empregando o método BP86/LANL2DZ/6-311+G(d,p) para estudar a adsorção da molécula NO que foi utilizada como protótipo molecular para interagir sobre aglomerados de paládio puros e dopados com metais de transição da primeira série de transição (Pd3M e Pd9M). Neste contexto, o escopo deste trabalho foi: (i) obter informações a respeito das possíveis alterações estruturais e eletrônicas ocorridas em aglomerados de paládio quando dopados com metais da primeira série de transição (MT), (ii) estudar as alterações no comportamento do átomo de paládio, nos seus respectivos aglomerados puros, frente aos aglomerados dopados com metais de transição da primeira série,(iii) buscar possíveis padrões de influência dos MT nas propriedades de aglomerados de paládio (ex: energias dos orbitais de fronteira, transferências eletrônicas de doação e retrodoação, densidades de spin, hibridizações dos orbitais responsáveis pelas ligações com moléculas adsorventes etc). Após a realização dos cálculos baseados na DFT, os resultados mostram que, para o estudo sobre os aglomerados contendo quatro átomos, foi obtida correlação direta entre a carga adquirida pelo metal M dentro do aglomerado metálico e os orbitais moleculares de fronteira LUMO calculados para Pd3M. Esta correlação direta não se mantém quando o tamanho do aglomerado de paládio é aumento de quatro para dez átomos. A energia de adsorção apresentada pela molécula de NO apresenta boa correlação com a energia do LUMO, independentemente do número de átomos e da geometria do aglomerado e da natureza do metal dopante. A molécula de NO adsorve mais favoravelmente no modo Bridge, independentemente de qual metal está dopando o aglomerado de Pd9M. Entretanto, para o aglomerado de Pd3M, o modo de adsorção dependerá da natureza do metal dopante. A mudança na geometria e no número de átomos de paládio existentes no aglomerado provoca mudanças no modo e na energia de adsorção da molécula de NO adsorvente. Uma investigação mais aprofundada deve encontrar outras possíveis correlações entre propriedades dos metais da primeira série e aglomerados de paládio dopados com os mesmos

QM/MM simulation of liquid water with an adaptive quantum region.

The simulation of complex chemical systems often requires a multi-level description, in which a region of special interest is treated using a computationally expensive quantum mechanical (QM) model while its environment is described by a faster, simpler molecular mechanical (MM) model. Furthermore, studying dynamic effects in solvated systems or bio-molecules requires a variable definition of the two regions, so that atoms or molecules can be dynamically re-assigned between the QM and MM descriptions during the course of the simulation. Such reassignments pose a problem for traditional QM/MM schemes by exacerbating the errors that stem from switching the model at the boundary. Here we show that stable, long adaptive simulations can be carried out using density functional theory with the BLYP exchange-correlation functional for the QM model and a flexible TIP3P force field for the MM model without requiring adjustments of either. Using a primary benchmark system of pure water, we investigate the convergence of the liquid structure with the size of the QM region, and demonstrate that by using a sufficiently large QM region (with radius 6 Å) it is possible to obtain radial and angular distributions that, in the QM region, match the results of fully quantum mechanical calculations with periodic boundary conditions, and, after a smooth transition, also agree with fully MM calculations in the MM region. The key ingredient is the accurate evaluation of forces in the QM subsystem which we achieve by including an extended buffer region in the QM calculations. We also show that our buffered-force QM/MM scheme is transferable by simulating the solvated Cl(-) ion.

Channel selective tunnelling through a nanographene assembly.

We report selective tunnelling through a nanographene intermolecular tunnel junction achieved via scanning tunnelling microscope tip functionalization with hexa-peri-hexabenzocoronene (HBC) molecules. This leads to an offset in the alignment between the energy levels of the tip and the molecular assembly, resulting in the imaging of a variety of distinct charge density patterns in the HBC assembly, not attainable using a bare metallic tip. Different tunnelling channels can be selected by the application of an electric field in the tunnelling junction, which changes the condition of the HBC on the tip. Density functional theory-based calculations relate the imaged HBC patterns to the calculated molecular orbitals at certain energy levels. These patterns bear a close resemblance to the π-orbital states of the HBC molecule calculated at the relevant energy levels, mainly below the Fermi energy of HBC. This correlation demonstrates the ability of an HBC functionalized tip as regards accessing an energy range that is restricted to the usual operating bias range around the Fermi energy with a normal metallic tip at room temperature. Apart from relating to molecular orbitals, some patterns could also be described in association with the Clar aromatic sextet formula. Our observations may help pave the way towards the possibility of controlling charge transport between organic interfaces.

Gaussian Approximation Potential: an interatomic potential derived from first principles Quantum Mechanics

Simulation of materials at the atomistic level is an important tool in studying microscopic structure and processes. The atomic interactions necessary for the simulation are correctly described by Quantum Mechanics. However, the computational resources required to solve the quantum mechanical equations limits the use of Quantum Mechanics at most to a few hundreds of atoms and only to a small fraction of the available configurational space. This thesis presents the results of my research on the development of a new interatomic potential generation scheme, which we refer to as Gaussian Approximation Potentials. In our framework, the quantum mechanical potential energy surface is interpolated between a set of predetermined values at different points in atomic configurational space by a non-linear, non-parametric regression method, the Gaussian Process. To perform the fitting, we represent the atomic environments by the bispectrum, which is invariant to permutations of the atoms in the neighbourhood and to global rotations. The result is a general scheme, that allows one to generate interatomic potentials based on arbitrary quantum mechanical data. We built a series of Gaussian Approximation Potentials using data obtained from Density Functional Theory and tested the capabilities of the method. We showed that our models reproduce the quantum mechanical potential energy surface remarkably well for the group IV semiconductors, iron and gallium nitride. Our potentials, while maintaining quantum mechanical accuracy, are several orders of magnitude faster than Quantum Mechanical methods.

First-principles energetics of water: a many-body analysis

Standard forms of density-functional theory (DFT) have good predictive power for many materials, but are not yet fully satisfactory for solid, liquid and cluster forms of water. We use a many-body separation of the total energy into its 1-body, 2-body (2B) and beyond-2-body (B2B) components to analyze the deficiencies of two popular DFT approximations. We show how machine-learning methods make this analysis possible for ice structures as well as for water clusters. We find that the crucial energy balance between compact and extended geometries can be distorted by 2B and B2B errors, and that both types of first-principles error are important.

Machine learning for predictive condensed-phase simulation

We show how machine learning techniques based on Bayesian inference can be used to reach new levels of realism in the computer simulation of molecular materials, focusing here on water. We train our machine-learning algorithm using accurate, correlated quantum chemistry, and predict energies and forces in molecular aggregates ranging from clusters to solid and liquid phases. The widely used electronic-structure methods based on density-functional theory (DFT) give poor accuracy for molecular materials like water, and we show how our techniques can be used to generate systematically improvable corrections to DFT. The resulting corrected DFT scheme gives remarkably accurate predictions for the relative energies of small water clusters and of different ice structures, and greatly improves the description of the structure and dynamics of liquid water.

Communication: energy benchmarking with quantum Monte Carlo for water nano-droplets and bulk liquid water.

We show the feasibility of using quantum Monte Carlo (QMC) to compute benchmark energies for configuration samples of thermal-equilibrium water clusters and the bulk liquid containing up to 64 molecules. Evidence that the accuracy of these benchmarks approaches that of basis-set converged coupled-cluster calculations is noted. We illustrate the usefulness of the benchmarks by using them to analyze the errors of the popular BLYP approximation of density functional theory (DFT). The results indicate the possibility of using QMC as a routine tool for analyzing DFT errors for non-covalent bonding in many types of condensed-phase molecular system.

First-principles energetics of water clusters and ice: a many-body analysis.

Standard forms of density-functional theory (DFT) have good predictive power for many materials, but are not yet fully satisfactory for cluster, solid, and liquid forms of water. Recent work has stressed the importance of DFT errors in describing dispersion, but we note that errors in other parts of the energy may also contribute. We obtain information about the nature of DFT errors by using a many-body separation of the total energy into its 1-body, 2-body, and beyond-2-body components to analyze the deficiencies of the popular PBE and BLYP approximations for the energetics of water clusters and ice structures. The errors of these approximations are computed by using accurate benchmark energies from the coupled-cluster technique of molecular quantum chemistry and from quantum Monte Carlo calculations. The systems studied are isomers of the water hexamer cluster, the crystal structures Ih, II, XV, and VIII of ice, and two clusters extracted from ice VIII. For the binding energies of these systems, we use the machine-learning technique of Gaussian Approximation Potentials to correct successively for 1-body and 2-body errors of the DFT approximations. We find that even after correction for these errors, substantial beyond-2-body errors remain. The characteristics of the 2-body and beyond-2-body errors of PBE are completely different from those of BLYP, but the errors of both approximations disfavor the close approach of non-hydrogen-bonded monomers. We note the possible relevance of our findings to the understanding of liquid water.

Stabilization of a complex perovskite superstructure under ambient conditions: Influence of cation composition and ordering, and evaluation as an SOFC cathode

Ba1.6Ca2.3Y1.1Fe5O13 is an Fe3+ oxide adopting a complex perovskite superstructure, which is an ordered intergrowth between the Ca2Fe2O5 and YBa2Fe3O8 structures featuring octahedral, square pyramidal, and tetrahedral B sites and three distinct A site environments. The distribution of A site cations was evaluated by combined neutron and X-ray powder diffraction. Consistent with the Fe3+ charge state, the material is an antiferromagnetic insulator with a Néel temperature of 480-485 °C and has a relatively low d.c. conductivity of 2.06 S cm-1 at 700 °C. The observed area specific resistance in symmetrical cell cathodes with the samarium-doped ceria electrolyte is 0.87 Ω cm2 at 700 °C, consistent with the square pyramidal Fe3+ layer favoring oxide ion formation and mobility in the oxygen reduction reaction. Density functional theory calculations reveal factors favoring the observed cation ordering and its influence on the electronic structure, in particular the frontier occupied and unoccupied electronic states. © 2010 American Chemical Society.

Revealing lithium-silicide phase transformations in nano-structured silicon-based lithium ion batteries via in situ NMR spectroscopy.

Nano-structured silicon anodes are attractive alternatives to graphitic carbons in rechargeable Li-ion batteries, owing to their extremely high capacities. Despite their advantages, numerous issues remain to be addressed, the most basic being to understand the complex kinetics and thermodynamics that control the reactions and structural rearrangements. Elucidating this necessitates real-time in situ metrologies, which are highly challenging, if the whole electrode structure is studied at an atomistic level for multiple cycles under realistic cycling conditions. Here we report that Si nanowires grown on a conducting carbon-fibre support provide a robust model battery system that can be studied by (7)Li in situ NMR spectroscopy. The method allows the (de)alloying reactions of the amorphous silicides to be followed in the 2nd cycle and beyond. In combination with density-functional theory calculations, the results provide insight into the amorphous and amorphous-to-crystalline lithium-silicide transformations, particularly those at low voltages, which are highly relevant to practical cycling strategies.

First principles interatomic potential for tungsten based on Gaussian process regression

An accurate description of atomic interactions, such as that provided by first principles quantum mechanics, is fundamental to realistic prediction of the properties that govern plasticity, fracture or crack propagation in metals. However, the computational complexity associated with modern schemes explicitly based on quantum mechanics limits their applications to systems of a few hundreds of atoms at most. This thesis investigates the application of the Gaussian Approximation Potential (GAP) scheme to atomistic modelling of tungsten - a bcc transition metal which exhibits a brittle-to-ductile transition and whose plasticity behaviour is controlled by the properties of $\frac{1}{2} \langle 111 \rangle$ screw dislocations. We apply Gaussian process regression to interpolate the quantum-mechanical (QM) potential energy surface from a set of points in atomic configuration space. Our training data is based on QM information that is computed directly using density functional theory (DFT). To perform the fitting, we represent atomic environments using a set of rotationally, permutationally and reflection invariant parameters which act as the independent variables in our equations of non-parametric, non-linear regression. We develop a protocol for generating GAP models capable of describing lattice defects in metals by building a series of interatomic potentials for tungsten. We then demonstrate that a GAP potential based on a Smooth Overlap of Atomic Positions (SOAP) covariance function provides a description of the $\frac{1}{2} \langle 111 \rangle$ screw dislocation that is in agreement with the DFT model. We use this potential to simulate the mobility of $\frac{1}{2} \langle 111 \rangle$ screw dislocations by computing the Peierls barrier and model dislocation-vacancy interactions to QM accuracy in a system containing more than 100,000 atoms.

Accuracy and transferability of GAP models for tungsten

We introduce interatomic potentials for tungsten in the bcc crystal phase and its defects within the Gaussian Approximation Potential (GAP) framework, fitted to a database of first principles density functional theory (DFT) calculations. We investigate the performance of a sequence of models based on databases of increasing coverage in configuration space and showcase our strategy of choosing representative small unit cells to train models that predict properties only observable using thousands of atoms. The most comprehensive model is then used to calculate properties of the screw dislocation, including its structure, the Peierls barrier and the energetics of the vacancy-dislocation interaction. All software and raw data are available at www.libatoms.org.