The Dynamics of the S(1D)+H2/D2 Reactions at Low Temperature via Statistical Simulations

Two different statistical approaches, the statistical quantum model (SQM) and the mean potential phase space theory (MPPST), have been employed to calculate the integral cross sections for the reactive collisions between S(1D) and H2/ D2 in the low energy regime (below 0.3 eV collisional energy). The rate constant for the S(1D) + H2 → SH + H reaction has been also obtained and compared with previously reported experimental and theoretical results. The good agreement shows the capability of these two methods to study the dynamics of these complex-forming atom-diatom processes in the present energy regime.

Probing the rate-determining region of the potential energy surface for a prototypical ion–molecule reaction

We report a joint experimental–theoretical study of the F – + HCl → HF + Cl − reaction kinetics. The experimental measurement of the rate coefficient at several temperatures was made using the selected ion flow tube method. Theoretical rate coefficients are calculated using the quasi-classical trajectory method on a newly developed global potential energy surface, obtained by fitting a large number of high-level ab initio points with augmentation of long-range electrostatic terms. In addition to good agreement between experiment and theory, analyses suggest that the ion–molecule reaction rate is significantly affected by shorter-range interactions, in addition to the traditionally recognized ion–dipole and ion–induced dipole terms. Furthermore, the statistical nature of the reaction is assessed by comparing the measured and calculated HF product vibrational state distributions to that predicted by the phase space theory. This article is part of the theme issue ‘Modern theoretical chemistry’.

Displacement Time Series Prediction Model of Landslide Based on Phase Space Reconstruction

In order to fully reveal information about landslide displacement, it was necessary to extend a time series to a higher-dimensional state space for the characteristic of univariate time series. However, in order to control the expansion of noise, an appropriate embedded dimension of phase space reconstruction was not the bigger the better. In this paper, based on the displacement time series of landslide, the phase space theory was used to build displacement time series matrix and the entropy theory was used to get the entropy. The embedded dimension of phase space reconstruction could be adjusted according to the change of entropy and feedback of displacement prediction error and a support vector regression model was created via the support vector machine’s learning. The application on Baijiabao landslide indicates that the proposed method achieves a high accuracy and stability of prediction.