Very recently, we proposed an automated method for finding transition states of chemical reactions using dynamics simulations; the method has been termed Transition State Search using Chemical Dynamics Simulations (TSSCDS) (E. Martínez-Núñez, J. Comput. Chem., 2015, 36, 222–234). In the present work, an improved automated search procedure is developed, which consists of iteratively running different ensembles of trajectories initialized at different minima. The iterative TSSCDS method is applied to the complex C₃H₄O system, obtaining a total of 66 different minima and 276 transition states. With the obtained transition states and paths, statistical RRKM calculations and Kinetic Monte Carlo simulations are carried out to study the fragmentation dynamics of propenal, which is the global minimum of the system. The kinetic simulations provide a (three-body dissociation)/(CO elimination) ratio of 1.49 for an excitation energy of 148 kcal mol⁻¹, which agrees well with the corresponding value obtained in the photolysis of propenal at 193 nm (1.1), suggesting that at least these two channels: three-body dissociation (to give H₂ + CO + C₂H₂) and CO elimination occur on the ground electronic state.

AutoMeKin2021 is an updated version of tsscds2018, a program for the automated discovery of reaction mechanisms (J. Comput. Chem.2018,39, 1922). This release features a number of new capabilities: rare‐event molecular dynamics simulations to enhance reaction discovery, extension of the original search algorithm to study van der Waals complexes, use of chemical knowledge, a new search algorithm based on bond‐order time series analysis, statistics of the chemical reaction networks, a web application to submit jobs, and other features. The source code, manual, installation instructions and the website link are available at:https://rxnkin.usc.es/index.php/AutoMeKin

A novel computational method is proposed in this work for use in discovering reaction mechanisms and solving the kinetics of transition metal-catalyzed reactions. The method does not rely on either chemical intuition or assumed a priori mechanisms, and it works in a fully automated fashion. Its core is a procedure, recently developed by one of the authors, that combines accelerated direct dynamics with an efficient geometry-based post-processing algorithm to find transition states (Martinez-Nunez, E., J. Comput. Chem.2015, 36, 222–234). In the present work, several auxiliary tools have been added to deal with the specific features of transition metal catalytic reactions. As a test case, we chose the cobalt-catalyzed hydroformylation of ethylene because of its well-established mechanism, and the fact that it has already been used in previous automated computational studies. Besides the generally accepted mechanism of Heck and Breslow, several side reactions, such as hydrogenation of the alkene, emerged from our calculations. Additionally, the calculated rate law for the hydroformylation reaction agrees reasonably well with those obtained in previous experimental and theoretical studies.