Machine learning enabling prediction of the bond dissociation enthalpy of hypervalent iodine from SMILES.

ABSTRACT: Machine learning to create models on the basis of big data enables predictions from new input data. Many tasks formerly performed by humans can now be achieved by machine learning algorithms in various fields, including scientific areas. Hypervalent iodine compounds (HVIs) have long been applied as useful reactive molecules. The bond dissociation enthalpy (BDE) value is an important indicator of reactivity and stability. Experimentally measuring the BDE value of HVIs is difficult, however, and the value has been estimated by quantum calculations, especially density functional theory (DFT) calculations. Although DFT calculations can access the BDE value with high accuracy, the process is highly time-consuming. Thus, we aimed to reduce the time for predicting the BDE by applying machine lear

SUBMITTER: Nakajima M 

PROVIDER: S-EPMC8511102 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature