Biomedical subjects

Sergei Manzhos

Publications and source records attributed to Sergei Manzhos.

5 recordsLinked to original sources

PubMed2006

Using neural networks to represent potential surfaces as sums of products.

By using exponential activation functions with a neural network (NN) method we show that it is possible to fit potentials to a sum-of-products form. The sum-of-products form is desirable because it reduces the cost of doing the quadratures required for quantum dynamics calculations. It also greatly facilitates the use of the multiconfiguration time dependent Hartree method. Unlike potfit product representation algorithm, the new NN approach does not require using a grid of points. It also produces sum-of-products potentials with fewer terms. As the number of dimensions is increased, we expect the advantages of the exponential NN idea to become more significant.

Algorithms↗

PubMed2006

A random-sampling high dimensional model representation neural network for building potential energy surfaces.

We combine the high dimensional model representation (HDMR) idea of Rabitz and co-workers [J. Phys. Chem. 110, 2474 (2006)] with neural network (NN) fits to obtain an effective means of building multidimensional potentials. We verify that it is possible to determine an accurate many-dimensional potential by doing low dimensional fits. The final potential is a sum of terms each of which depends on a subset of the coordinates. This form facilitates quantum dynamics calculations. We use NNs to represent HDMR component functions that minimize error mode term by mode term. This NN procedure makes it possible to construct high-order component functions which in turn enable us to determine a good potential. It is shown that the number of available potential points determines the order of the HDMR which should be used.

Journal Article↗

PubMed2006

A nested molecule-independent neural network approach for high-quality potential fits.

It is shown that neural networks (NNs) are efficient and effective tools for fitting potential energy surfaces. For H2O, a simple NN approach works very well. To fit surfaces for HOOH and H2CO, we develop a nested neural network technique in which we first fit an approximate NN potential and then use another NN to fit the difference of the true potential and the approximate potential. The root-mean-square error (RMSE) of the H2O surface is 1 cm(-1). For the 6-D HOOH and H2CO surfaces, the nested approach does almost as well attaining a RMSE of 2 cm(-1). The quality of the NN surfaces is verified by calculating vibrational spectra. For all three molecules, most of the low-lying levels are within 1 cm(-1) of the exact results. On the basis of these results, we propose that the nested NN approach be considered a method of choice for both simple potentials, for which it is relatively easy to guess a good fitting function, and complicated (e.g., double well) potentials for which it is much harder to deduce an appropriate fitting function. The number of fitting parameters is only moderately larger for the 6-D than for the 3-D potentials, and for all three molecules, decreasing the desired RMSE increases only slightly the number of required fitting parameters (nodes). NN methods, and in particular the nested approach we propose, should be good universal potential fitting tools.

Journal Article↗

PubMed2004

Two-photon state selection and angular momentum polarization probed by velocity map imaging: application to H atom photofragment angular distributions from the photodissociation of two-photon state selected HCl and HBr.

A formalism for calculating the angular momentum polarization of an atom or a molecule following two-photon excitation of a J-selected state is presented. This formalism is used to interpret the H atom photofragment angular distributions from single-photon dissociation of two-photon rovibronically state selected HCl and HBr prepared via a Q-branch transition. By comparison of the angular distributions measured using the velocity map imaging technique with the theoretical model it is shown that single-photon dissociation of two-photon prepared states can be used for pathway identification, allowing for the identification of the virtual state symmetry in the two-photon absorption and/or the symmetry of the dissociative state. It is also shown that under conditions of excitation with circularly polarized light, or for excitation via non-Q-branch transitions with linearly polarized light the angular momentum polarization is independent of the dynamics of the two-photon transition and analytically computable.

Journal Article↗

PubMed2004

Superexcited state reconstruction of HCl using photoelectron and photoion imaging.

The velocity-map imaging technique was used to record photoelectron and photofragment ion images of HCl following two-photon excitation of the E Sigma(+)(0+), V 1Sigma(+)(0+) (nu=9,10,11) states and subsequent ionization. The images allowed us to determine the branching ratios between autoionization and dissociation channels for the different intermediate states. These branching ratios can be explained on the basis of intermediate state electron configurations, since the configuration largely prohibits direct ionization in a one-electron process, and competition between autoionization and dissociation into H* (n=2)+Cl and H+Cl*(4s,4p,3d) is observed. From a fit to the vibrationally resolved photoelectron spectrum of HCl+ it is apparent that a single superexcited state acts as a gateway to autoionization and dissociation into H+Cl*(4s). Potential reconstruction of the superexcited state to autoionization was undertaken and from a comparison of different autoionization models it appears most likely that the gateway state is a purely repulsive and low-n Rydberg state with a (4Pi) ion core.

Journal Article↗