Abstract Machine learning has made significant progress in assessing the risk associated
with hazardous chemicals. However, most models were constructed by randomly selecting …

A recurrent neural network (RNN) was used to predict the time evolution of the Dushman
reaction for the first time. The structure of the RNN was optimized to reduce the training time …

For the optimization of a chemical reactor or its control, computational fluid dynamics (CFD)
that considers accurate reaction models is effective. Although detailed kinetic mechanisms …

Abstract Machine learning models were developed to predict product formation from time-
series reaction data for ten Buchwald-Hartwig coupling reactions. The data was provided by …

Atmospheric modeling has recently experienced a surge with the advent of deep learning.
Most of these models, however, predict concentrations of pollutants following a data-driven …

While iodine speciation is important for a wide range of nuclear safety activities,
understanding the mechanisms of the transformations of iodine between chemical forms and …