Can classical mechanics sense conical intersection?

Conical intersection (CI) leads to fast electronic energy transfer. However, Hamm and Stock [Phys. Rev. Lett. 109, 173201 (2012)] showed the existence of a vibrational CI and its role in vibrational energy relaxation. In this paper, we further investigate the vibrational energy relaxation using an isolated model Hamiltonian system of four vibrational modes with two distinctively different timescales (two fast modes and two slow modes). We show that the excitation of the slow modes plays a crucial role in the energy relaxation mechanism. We also analyze the system from a mixed quantum-classical (surface hopping method) and a completely classical point of view. Notably, surface hopping and even classical simulations also capture fast energy relaxation, which is a signature of CI's existence.

A Data-Driven Workflow for Assigning and Predicting Generality in Asymmetric Catalysis.

The development of chiral catalysts that can provide high enantioselectivities across a wide assortment of substrates or reaction range is a priority for many catalyst design efforts. While several approaches are available to aid in the identification of general catalyst systems, there is currently no simple procedure for directly measuring how general a given catalyst could be. Herein, we present a catalyst-agnostic workflow centered on unsupervised machine learning that enables the rapid assessment and quantification of catalyst generality. The workflow uses curated literature data sets and reaction descriptors to visualize and cluster chemical space coverage. This reaction network can then be applied to derive a catalyst generality metric through designer equations and interfaced with other regression techniques for general catalyst prediction. As validating case studies, we have successfully applied this method to identify-through-quantification the most general catalyst chemotype for an organocatalytic asymmetric Mannich reaction and predicted the most general chiral phosphoric acid catalyst for the addition of nucleophiles to imines. The mechanistic basis for catalyst generality can then be gleaned from the calculated values by deconstructing the contributions of chemical space and enantiomeric excess to the overall result. Finally, our generality techniques permitted the development of mechanistically informative catalyst screening sets that allow experimentalists to rationally select catalysts that have the highest probability of achieving a good result in the first round of reaction development. Overall, our findings represent a framework for interrogating catalyst generality, and this strategy should be relevant to other catalytic systems widely applied in asymmetric synthesis.

DeepWEST: Deep Learning of Kinetic Models with the Weighted Ensemble Simulation Toolkit for Enhanced Sampling.

Recent advances in computational power and algorithms have enabled molecular dynamics (MD) simulations to reach greater time scales. However, for observing conformational transitions associated with biomolecular processes, MD simulations still have limitations. Several enhanced sampling techniques seek to address this challenge, including the weighted ensemble (WE) method, which samples transitions between metastable states using many weighted trajectories to estimate kinetic rate constants. However, initial sampling of the potential energy surface has a significant impact on the performance of WE, i.e., convergence and efficiency. We therefore introduce deep-learned kinetic modeling approaches that extract statistically relevant information from short MD trajectories to provide a well-sampled initial state distribution for WE simulations. This hybrid approach overcomes any statistical bias to the system, as it runs short unbiased MD trajectories and identifies meaningful metastable states of the system. It is shown to provide a more refined free energy landscape closer to the steady state that could efficiently sample kinetic properties such as rate constants.