In every day chemistry, solvents are used to influence the outcome of chemical synthesis. Electrostatic effects stabilize polar configurations during the reaction and in addition dynamic solvent effects can emerge. How the dynamic effects intervene on the ultrafast time scale is in the focus of this theoretical study. We selected the photoinduced bond cleavage of Ph2CH-PPh3(+) for which the electrostatic interactions are negligible. Elaborate ultrafast pump-probe studies already exist and serve as a reference. We compared quantum dynamical simulations with and without environment and noticed the necessity to model the influence of the solvent cage on the reactive motions of the solute. The frictional force induced by the dynamic viscosity of the solvent is implemented in the quantum mechanical formalism with a newly developed approach called the dynamic continuum ansatz. Only when the environment is included are the experimentally observed products reproduced on the subpicosecond time scale.
In quantum chemistry methods to describe environmental effects on different levels of complexity are available in the common program packages. Electrostatic effects of a solvent for example can be included in an implicit or explicit way. For chemical reactions with large structural changes additional mechanical effects come into play. Their treatment within a quantum dynamical context is a major challenge, especially when excited states are involved. Recently, we introduced a method that realizes an implicit description. Here, we present an approach combining quantum dynamics and molecular dynamics. It explicitly incorporates the solvent environment, whereby the electrostatic as well as the dynamic effects are captured. This new method is demonstrated for the ultrafast photoinduced bond cleavage of diphenylmethylphosphonium ions (Ph2CH-PPh3(+)), a common precursor to generate reactive carbocations in solution.
The curse of dimensionality still remains as the central challenge of molecular quantum dynamical calculations. Either compromises on the accuracy of the potential landscape have to be made or methods must be used that reduce the dimensionality of the configuration space of molecular systems to a low dimensional one. For dynamic approaches such as grid-based wave packet dynamics that are confined to a small number of degrees of freedom this dimensionality reduction can become a major part of the overall problem. A common strategy to reduce the configuration space is by selection of a set of internal coordinates using chemical intuition. We devised two methods that increase the degree of automation of the dimensionality reduction as well as replace chemical intuition by more quantifiable criteria. Both methods reduce the dimensionality linearly and use the intrinsic reaction coordinate as guidance. The first one solely relies on the intrinsic reaction coordinate (IRC), whereas the second one uses semiclassical trajectories to identify the important degrees of freedom.
A challenge for molecular quantum dynamics (QD) calculations is the curse of dimensionality with respect to the nuclear degrees of freedom. A common approach that works especially well for fast reactive processes is to reduce the dimensionality of the system to a few most relevant coordinates. Identifying these can become a very difficult task, because they often are highly unintuitive. We present a machine learning approach that utilizes an autoencoder that is trained to find a low-dimensional representation of a set of molecular configurations. These configurations are generated by trajectory calculations performed on the reactive molecular systems of interest. The resulting low-dimensional representation can be used to generate a potential energy surface grid in the desired subspace. Using the G-matrix formalism to calculate the kinetic energy operator, QD calculations can be carried out on this grid. In addition to step-by-step instructions for the grid construction, we present the application to a test system.
Shaped laser pulses offer a powerful tool to manipulate molecular quantum systems. Their application to chemical reactions in solution is a promising concept to redesign chemical synthesis. Along this road, theoretical developments to include the solvent surrounding are necessary. An appropriate theoretical treatment is helpful to understand the underlying mechanisms. In our approach we simulate the solvent by randomly selected snapshots from molecular dynamics trajectories. We use multi target optimal control theory to optimize pulses for the various arrangements of explicit solvent molecules simultaneously. This constitutes a major challenge for the control algorithm, as the solvent configurations introduce a large inhomogeneity to the potential surfaces. We investigate how the algorithm handles the new challenges and how well the controllability of the system is preserved with increasing complexity. Additionally, we introduce a way to statistically estimate the efficiency of the optimized laser pulses in the complete thermodynamical ensemble.
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