Kinetics of the hydrogen abstraction ·C2H5 + alkane → C2H6 + alkyl reaction class: an application of the reaction class transition state theory

This paper presents an application of the reaction class transition state theory (RC-TST) to predict thermal rate constants for hydrogen abstraction reactions at alkane by the C2H5 radical on-the-fly. The linear energy relationship (LER), developed for acyclic alkanes, was also proven to hold for cyclic alkanes. We have derived all RCTST parameters from rate constants of 19 representative reactions, coupling with LER and the barrier height grouping (BHG) approach. Both the RC-TST/LER, where only reaction energy is needed, and the RC-TST/BHG, where no other information is needed, can predict rate constants for any reaction in this reaction class with satisfactory accuracy for combustion modeling. Our analysis indicates that less than 50% systematic errors on the average exist in the predicted rate constants using either the RC-TST/LER or RC-TST/BHG method, while in comparison with explicit rate calculations, the differences are within a factor of 2 on the average. The results also show that the RC-TST method is not sensitive to the choice of density functional theory used

Kinetics of 1,6-hydrogen migration in alkyl radical reaction class

The kinetics of the 1,6-intramolecular hydrogen migration in the alkyl radical reaction class has been studied using the reaction class transition state theory (RC-TST) combined with the linear energy relationship (LER) and the barrier height grouping (BHG) approach. The RC-TST/LER, where only reaction energy is needed, and RC-TST/BHG, where no other information is needed, are found to be promising methods for predicting rate constants for any reaction in the 1,6-intramolecular H migration in alkyl radicals reaction class. Direct comparison with available experimental data indicates that the RC-TST/LER, where only reaction energy is needed, can predict rate constants for any reaction in this reaction class with satisfactory accuracy

ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules

One of the grand challenges in modern theoretical chemistry is designing and implementing approximations that expedite ab initio methods without loss of accuracy. Machine learning (ML) methods are emerging as a powerful approach to constructing various forms of transferable atomistic potentials. They have been successfully applied in a variety of applications in chemistry, biology, catalysis, and solid-state physics. However, these models are heavily dependent on the quality and quantity of data used in their fitting. Fitting highly flexible ML potentials, such as neural networks, comes at a cost: a vast amount of reference data is required to properly train these models. We address this need by providing access to a large computational DFT database, which consists of more than 20 M off equilibrium conformations for 57,462 small organic molecules. We believe it will become a new standard benchmark for comparison of current and future methods in the ML potential community