Predicting Long-Time-Scale Kinetics under Variable Experimental Conditions with Kinetica.jl.

ABSTRACT: Predicting the degradation processes of molecules over long time scales is a key aspect of industrial materials design. However, it is made computationally challenging by the need to construct large networks of chemical reactions that are relevant to the experimental conditions that kinetic models must mirror, with every reaction requiring accurate kinetic data. Here, we showcase Kinetica.jl, a new software package for constructing large-scale chemical reaction networks in a fully automated fashion by exploring chemical reaction space with a kinetics-driven algorithm; coupled to efficient machine-learning models of activation energies for sampled elementary reactions, we show how this approach readily enables generation and kinetic characterization of networks containing ∼10^3</

SUBMITTER: Gilkes J 

PROVIDER: S-EPMC11209948 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature