Linear and two-dimensional infrared (IR) spectroscopy of site-specific probe molecules provides an opportunity to gain a molecular-level understanding of the local hydrogen-bonding network, conformational dynamics, and long-range electrostatic interactions in condensed-phase and biological systems. A challenge in computation is to determine the time-dependent vibrational frequencies that incorporate explicitly both nuclear quantum effects of vibrational motions and an electronic structural representation of the potential energy surface. In this paper, a nuclear quantum vibrational perturbation (QVP) method is described for efficiently determining the instantaneous vibrational frequency of a chromophore in molecular dynamics simulations. Computational efficiency is achieved through the use of (a) discrete variable representation of the vibrational wave functions, (b) a perturbation theory to evaluate the vibrational energy shifts due to solvent dynamic fluctuations, and (c) a combined QM/MM potential for the systems. It was found that first-order perturbation is sufficiently accurate, enabling time-dependent vibrational frequencies to be obtained on the fly in molecular dynamics. The QVP method is illustrated in the mode-specific linear and 2D-IR spectra of the H–Cl stretching frequency in the HCl–water clusters and the carbonyl stretching vibration of acetone in aqueous solution. To further reduce computational cost, a hybrid strategy was proposed, and it was found that the computed vibrational spectral peak position and line shape are in agreement with experimental results. In addition, it was found that anharmonicity is significant in the H–Cl stretching mode, and hydrogen-bonding interactions further enhance anharmonic effects. The present QVP method complements other computational approaches, including path integral-based molecular dynamics, and represents a major improvement over the electrostatics-based spectroscopic mapping procedures.

Dynamic task allocation of unmanned aerial vehicle swarms for ground targets is an important part of unmanned aerial vehicle (UAV) swarms task planning and the key technology to improve autonomy. The realization of dynamic task allocation in UAV swarms for ground targets is very difficult because of the large uncertainty of swarms, the target and environment state, and the high real-time allocation requirements. Hence, dynamic task allocation of UAV swarms oriented to ground targets has become a key and difficult problem in the field of mission planning. In this work, a dynamic task allocation method for UAV swarms oriented to ground targets is comprehensively and systematically summarized from two aspects: the establishment of an allocation model and the solution of the allocation model. First, the basic concept and trigger scenario are introduced.Second, the research status and the advantages and disadvantages of the two allocation models are analyzed.Third, the research status and the advantages and disadvantages of several common dynamic task allocation algorithms, such as the algorithm based on market mechanisms, intelligent optimization algorithm, and clustering algorithm, are evaluated. Finally, the specific problems of the current UAV swarm dynamic task allocation method for ground targets are highlighted, and future research directions are established. This work offers important reference significance for fully understanding the current situation of UAV swarm dynamic task allocation technology.

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