We have experimentally investigated the soliton interaction in a passively mode-locked fiber ring laser and revealed the existence of three types of strong soliton interaction: a global type of soliton interaction caused by the existence of unstable cw components, a local type of soliton interaction mediated through the radiative dispersive waves, and the direct soliton interaction. We found that the appearance of the various soliton operation modes observed in the passively mode-locked fiber soliton lasers are the direct consequences of these three types of soliton interactions. The soliton interaction in the laser is further numerically simulated based on a pulse tracing technique. The numerical simulations confirmed the existence of the dispersive-wave-mediated soliton interaction and the direct soliton interaction. Furthermore, it was shown that the resonant dispersivewave-mediated soliton interaction in the laser always has the consequence of causing random irregular relative soliton movement and the experimentally observed states of bound solitons are caused by the direct soliton interaction. In particular, as the solitons generated in the laser could have a profile with long tails, the direct soliton interaction could extend to a soliton separation that is larger than 5 times the soliton pulse width.

A passively mode-locked soliton fiber ring laser with dispersion managed cavity is reported. The laser emits intense bunched noise-like pulses including the transform limited pulses. The optical spectrum of the laser emission has a bandwidth as broad as 32.10 nm. It was found that purely depending on the linear cavity phase delay the laser could be switched between the soliton operation and the noise-like pulse emission. Numerical simulations showed that the laser emission was caused by the combined effect of soliton collapse and positive cavity feedback in the laser.

We survey the recent advances in theoretical understanding of quantum state resolved dynamics, using the title reactions as examples. It is shown that the progress was made possible by major developments in two areas. First, an accurate analytical representation of many high-level ab initio points over a large configuration space can now be made with high fidelity and the necessary permutation symmetry. The resulting full-dimensional global potential energy surfaces enable dynamical calculations using either quasi-classical trajectory or more importantly quantum mechanical methods. The second advance is the development of accurate and efficient quantum dynamical methods, which are necessary for providing a reliable treatment of quantum effects in reaction dynamics such as tunneling, resonances, and zero-point energy. The powerful combination of the two advances has allowed us to achieve a quantitatively accurate characterization of the reaction dynamics, which unveiled rich dynamical features such as steric steering, strong mode specificity, and bond selectivity. The dependence of reactivity on reactant modes can be rationalized by the recently proposed sudden vector projection model, which attributes the mode specificity and bond selectivity to the coupling of reactant modes with the reaction coordinate at the relevant transition state. The deeper insights provided by these theoretical studies have advanced our understanding of reaction dynamics to a new level.

Representation of multidimensional global potential energy surfaces suitable for spectral and dynamical calculations from high-level ab initio calculations remains a challenge. Here, we present a detailed study on constructing potential energy surfaces using a machine learning method, namely, Gaussian process regression. Tests for the 3 A″ state of SH 2 , which facilitates the SH + H ↔ S( 3 P) + H 2 abstraction reaction and the SH + H′ ↔ SH′ + H exchange reaction, suggest that the Gaussian process is capable of providing a reasonable potential energy surface with a small number (∼1 × 10 2 ) of ab initio points, but it needs substantially more points (∼1 × 10 3 ) to converge reaction probabilities. The implications of these observations for construction of potential energy surfaces are discussed.

An efficient and trajectory-free active learning method is proposed to automatically sample data points for constructing globally accurate reactive potential energy surfaces (PESs) using neural networks (NNs). Although NNs do not provide the predictive variance as the Gaussian process regression does, we can alternatively minimize the negative of the squared difference surface (NSDS) given by two different NN models to actively locate the point where the PES is least confident. A batch of points in the minima of this NSDS can be iteratively added into the training set to improve the PES. The configuration space is gradually and globally covered with no need to run classical trajectory (or equivalently molecular dynamics) simulations. Through refitting the available analytical PESs of H3 and OH3 reactive systems, we demonstrate the efficiency and robustness of this new strategy, which enables fast convergence of the reactive PESs with respect to the number of points in terms of quantum scattering probabilities.