Fast and Automated Identification of Reactions with Low Barriers: The Decomposition of 3-Hydroperoxypropanal

Abstract

\n \n \n We show how fast semiempirical QM methods can be used to significantly decrease the CPU requirements for automated reaction mechanism discovery, using two different method for generating\nreaction products: graph-based systematic enumeration of all possible products and the meta-dynamics approach by Grimme (J. Chem. Theory. Comput. 2019, 15, 2847). We test the two approaches\non the low-barrier reactions of 3-hydroperoxypropanal, which have been studied by a large variety\nof reaction discovery approaches and therefore provides a good benchmark. By using PM3 and\nGFN2-xTB for reaction energy and barrier screening the systematic approach identifies 64 reactions\n(out of 27,577 possible reactions) for DFT refinement, which in turn identifies the three reactions\nwith lowest barriers plus a previously undiscovered reaction. With optimised hyperparameters meta-dynamics followed by PM3/GFN2-xTB-based screening identifies 15 reactions for DFT refinement,\nwhich in turn identifies the three reactions with lowest barrier. The number of DFT refinements can\nbe further reduced to as little as six for both approaches by first verifying the transition states with\nGFN1-xTB. The main conclusion is that the semiempirical methods are accurate and fast enough\nto automatically identify promising candidates for DFT refinement for the low barrier reactions of\n3-hydroperoxypropanal in a few hours using modest computational resources.\n\n \n \n