Automated Exploration of Reaction Network and Mechanism via Meta-dynamics Nanoreactor

We developed an automated approach to construct the complex reaction network and explore the reaction mechanism for several reactant molecules. The nanoreactor type molecular dynamics was employed to generate possible chemical reactions, in which the meta-dynamics was taken to overcome reaction barriers and the semi-empirical GFN2-xTB method was used to reduce computational cost. The identification of reaction events from trajectories was conducted by using the hidden Markov model based on the evolution of the molecular connectivity. This provided the starting points for the further transition state searches at the more accurate electronic structure levels to obtain the reaction mechanism. Then the whole reaction network with multiply pathways was obtained. The feasibility and efficiency of this automated construction of the reaction network was examined by two examples. The first reaction under study was the HCHO + NH3 biomolecular reaction. The second example focused on the reaction network for a multi-species system composed of dozens of HCN and H2O compounds. The result indicated that the proposed approach was a valuable and effective tool for the automated exploration of reaction networks

Realization of the Trajectory Propagation in the MM-SQC Dynamics by Using Machine Learning

The supervised machine learning (ML) approach is applied to realize the trajectory-based nonadiabatic dynamics within the framework of the symmetrical quasi-classical dynamics method based on the Meyer-Miller mapping Hamiltonian (MM-SQC). After the construction of the long short-term memory recurrent neural network (LSTM-RNN) model, it is used to perform the entire trajectory evolutions from initial sampling conditions. The proposed idea is proven to be reliable and accurate in the simulations of the dynamics of several site-exciton electron-phonon coupling models, which cover two-site and three-site systems with biased and unbiased energy levels, as well as include a few or many phonon modes. The LSTM-RNN approach also shows the powerful ability to obtain the accurate and stable results for the long-time evolutions. It indicates that the LSTM-RNN model perfectly captures of dynamical correction information in the trajectory evolution in the MM-SQC dynamics. Our work provides the possibility to employ the ML methods in the simulation of the trajectory-based nonadiabatic dynamic of complex systems with a large number of degrees of freedoms

Ab initio insight into ultrafast nonadiabatic decay of hypoxanthine: keto-N7H and keto-N9H tautomers

National Science Foundation of China (NSFC) [21133007, 21103213, 91233106]; Ministry of Science and Technology [2011CB808504, 2012CB214900]; CAS; Director Innovation Foundation of CAS-QIBEBTNonadiabatic dynamics simulations at the SA-CASSCF level were performed for the two most stable keto-N7H and keto-N9H tautomers of hypoxanthine in order to obtain deep insight into the lifetime of the optically bright S-1((1)pi pi*) excited state and the relevant decay mechanisms. Supporting calculations on the ground-state (S-0) equilibrium structures and minima on the crossing seams of both tautomers were carried out at the MR-CIS and CASSCF levels. These studies indicate that there are four slightly different kinds of conical intersections in each tautomer, exhibiting a chiral character, each of which dominates a barrierless reaction pathway. Moreover, both tautomers reveal the ultrafast S-1 -> S-0 decay, in which the S1 state of keto-N9H in the gas phase has a lifetime of 85.5 fs, whereas that of keto-N7H has a longer lifetime of 137.7 fs. An excellent agreement is found between the present results and the experimental value of 130 +/- 20 fs in aqueous solution