RESUMO

A realistic double many-body expansion potential energy surface (PES) is developed for the 2A″ state of the carbon-nitrogen-oxygen (CNO) system based on MRCI-F12/cc-pVQZ-F12 ab initio energies. The new PES reproduces the fitted points with chemical accuracy (root mean square deviation up to 0.043 eV) and explicitly incorporates long range energy terms that can accurately describe the electrostatic and dispersion interactions. Thermal rate coefficients were computed for the C(3P) + NO(2Π) reaction for temperatures ranging from 15 K to 10 000 K, and the values are compared to previously reported results. The differences are rationalized, and the major importance of long range forces in predicting the rate coefficients for barrierless reactions is emphasized.

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Hopfield neural network was used to retrieve liquid gallium radial distribution function from an experimental structure factor, obtained at 959 K. The inversion framework was carried out under two initial conditions: (a) a constant radial distribution function corresponding to an ideal gas and (b) a step function, simulating a gas with square well potential of interaction. Both situations lead to accurate inverse results if compared with the radial distribution function obtained by Bellisent-Funel et al., using the Fourier transform method and Monte Carlo simulation. The Hopfield neural network has shown to be a powerful strategy to calculate the radial distribution function from experimental data.

RESUMO

The fractional derivative concept to treat non-isothermal solid state thermal decomposition was employed in this work. Simulated data were compared with the exact solutions for the method validation. Calculated fractional kinetics data for four heating rates were initially considered and the Kissinger-Akahira-Sunose (KAS) method demonstrate that, although the activation energy is not retrieved, it can be useful to determine a single or multistep process. Experimental thermal decomposition data of lumefantrine heated at 5, 10 ,15, and 20 oC min- 1 were fitted for a single-step process. The kinetic parameters were retrieved for integer and fractional kinetics, considering some ideal and general models. Application of the KAS method to these data demonstrated an activation energy dependent on the conversion rate, indicating a multistep process. Five data subintervals were fitted separately using the general model with variable derivative order. It was found a process that occours with integer order derivative until α = 0.3 and fractional order for α > 0.3 with combination of simultaneous reactions, since the parameters do not correspond to any ideal model. The determined activation energies showed the same increasing behavior observed in the KAS approach. The results for multistep process presented an error 102 times smaller if compared with the best result, considering a single-step process. Therefore, the fractional kinetic model presents a powerful extension to the usual thermal data analysis.

RESUMO

The predicted rate constants for C + NO and O + CN collisions in three potential energy surfaces (PESs) for the 2A' state of the CNO molecule are compared using quasiclassical trajectories. Different temperature dependencies are obtained for the C + NO reaction, which are explained in terms of the long-range properties of the PESs. Recommended values and mechanistic details are also reported. For O + CN collisions, a better agreement between the theoretical results is found, except for temperatures below 100 K.

RESUMO

The DFT potential energy hypersurfaces of closed-shell nitrogen clusters up to ten atoms are explored via a genetic algorithm (GA). An atom-atom distance threshold parameter, controlled by the user, and an "operator manager" were added to the standard evolutionary procedure. Both B3LYP and PBE exchange-correlation functionals with 6-31G basis set were explored using the GA. Further evaluation of the structures generated were performed through reoptimization and vibrational analysis within MP2 and CCSD(T) levels employing larger correlation consistent basis set. The binding energies of all stable structures found are calculated and compared, as well as their energies relative to the dissociation into N2, [Formula: see text] and [Formula: see text] molecules. With the present approach, we confirmed some previously reported polynitrogen structures and predicted the stability of new ones. We can also conclude that the energy surface profile clearly depends on the calculation method employed.

RESUMO

We report a new global double many-body expansion potential energy surface for the ground state of the CNO(2A') manifold, calculated by the explicit correlation multireference configuration interaction method. The functional form was accurately fitted to 3701 ab initio points with a root mean squared deviation of 0.99 kcal mol-1. All stationary points reported in previous forms are systematically described and improved, in addition to three new ones and a characterization of an isomerization transition state between the CNO and NCO minima. The novel proposed form gives a realistic description of both short-range and long-range interactions and hence is commended for dynamics studies.

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The electronic quenching reaction N((2)D) + N2 → N((4)S) + N2 is studied using the trajectory surface hopping method and employing two doublet and one quartet accurate potential energy surfaces. State-specific properties are analyzed, such as the dependence of the cross section on the initial quantum state of the reactants, vibrational energy transfer, and rovibrational distribution of the product N2 molecule in thermalized conditions. It is found that rotational energy on the reactant N2 molecule is effective in promoting the reaction, whereas vibrational excitation tends to reduce the reaction probability. For initial states and collision energy thermalized in an initial bath, it is found that the products are "hotter", both vibration and rotation wise.

RESUMO

Diffusion ordered spectroscopy (DOSY) is a powerful two-dimensional NMR method to study molecular translation in various systems. The diffusion coefficients are usually retrieved, at each frequency, from a fit procedure on the experimental data, considering a unique coefficient for each molecule or mixture. However, the fit can be improved if one regards the decaying curve as a multiexponential function and the diffusion coefficient as a distribution. This work presents a computer code based on the Hopfield neural network to invert the data. One small-molecule binary mixture with close diffusion coefficients is treated with this approach, demonstrating the effectiveness of the method.

Assuntos

RESUMO

Inversion of transverse relaxation time decay curve from spin-echo experiments was carried out using Hopfield neural network, to obtain the transverse relaxation time distribution. The performance of this approach was tested against simulated and experimental data. The initial guess, necessary for the integration procedure, was established as the analytical Laplace inversion. Together with errors in the simulated data, inversion was also carried out with errors in this initial guess. The probability density function, calculated by the neural network, is used in multiple sclerosis diagnostics.

Assuntos

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The aim of this work is to present a new training algorithm for SVMs based on the pattern selection strategy called Error Dependent Repetition (EDR). With EDR, the presentation frequency of a pattern depends on its error: patterns with larger errors are selected more frequently and patterns with smaller error (or learned) are presented with minor frequency. Using a simple iterative process based on gradient ascent, SVM-EDR can solve the dual problem without any assumption about support vectors or the Karush-Kuhn-Tucker (KKT) conditions.

Assuntos

RESUMO

Inversion of positron annihilation lifetime spectroscopy, based on a neural network Hopfield model, is presented in this paper. From a previous reported density function for lysozyme in water a simulated spectrum, without the superposition of statistical fluctuation and spectrometer resolution effects, was generated. These results were taken as the exact results from which the neural network was trained. The precision of the inverted density function was analyzed taking into account the number of neurons and the learning time of the neural network. A fair agreement was obtained when comparing the neural network results with the exact results. For example, the maximum of the density function, with a precision of 0.4% for the percentual relative error, was obtained for 64 neurons.