Reactants, products, and transition states of elementary chemical reactions based on quantum chemistry

Abstract

Reaction times, activation energies, branching ratios, yields, and many other quantitative attributes are important for precise organic syntheses and generating detailed reaction mechanisms. Often, it would be useful to be able to classify proposed reactions as fast or slow. However, quantitative chemical reaction data, especially for atom-mapped reactions, are difficult to find in existing databases. Therefore, we used automated potential energy surface exploration to generate 12,000 organic reactions involving H, C, N, and O atoms calculated at the ωB97X-D3/def2-TZVP quantum chemistry level. We report the results of geometry optimizations and frequency calculations for reactants, products, and transition states of all reactions. Additionally, we extracted atom-mapped reaction SMILES, activation energies, and enthalpies of reaction. We believe that this data will accelerate progress in automated methods for organic synthesis and reaction mechanism generation—for example, by enabling the development of novel machine learning models for quantitative reaction prediction.

Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies

“…We envision that this problem will be solved in the near future by deep learning approaches that can predict both TS geometries 67 and DFT-computed barriers 27 based on large, publicly available datasets. 68,69 In the end, machine learning for reaction prediction needs to reproduce experiment, and transfer learning will probably be key to utilizing small high-quality kinetic datasets together with large amounts of computationally generated data. Regardless of their construction, accurate reaction prediction models will be key components of accelerated route design, reaction optimization and process design enabling the delivery of medicines to patients faster and with reduced costs.…”

Isotopic Consequences of Host–Guest Interactions; Noncovalent Chlorine Isotope Effects

“…We have resorted to the theory level, which proved successful in our recent studies on carbon vapor pressure isotope effects of ethanol, 42 carbon isotope effects on adsorption on graphene, 43 and isotope effects of oxygen and sulfur in phosphates. 44 This level has also been used recently for studies of over 12 000 chemical reactions 45 and thus provides an excellent reference level for future studies. BSSE correction has been applied for gas-phase complexes.…”

Machine Learning Meets Mechanistic Modelling for Accurate Prediction of Experimental Activation Energies

“…We envision that this problem will be solved in the near future by deep learning approaches that can predict both TS geometries 67 and DFT-computed barriers 27 based on large, publicly available datasets. 68,69 In the end, machine learning for reaction prediction needs to reproduce experiment, and transfer learning will probably be key to utilizing small high-quality kinetic datasets together with large amounts of computationally generated data. Regardless of their construction, accurate reaction prediction models will be key components of accelerated route design, reaction optimization and process design enabling the delivery of medicines to patients faster and with reduced costs.…”