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Machine learning techniques have found their way into computational chemistry as indispensable tools to accelerate atomistic simulations and materials design. In addition, machine learning approaches hold the potential to boost the predictive power of computationally efficient electronic structure methods, such as density functional theory, to chemical accuracy and to correct for fundamental errors in density functional approaches. Here, recent progress in applying machine learning to improve the accuracy of density functional and related approximations is reviewed. Promises and challenges in devising machine learning models transferable between different chemistries and materials classes are discussed with the help of examples applying promising models to systems far outside their training sets. [ABSTRACT FROM AUTHOR]

Kinetic models parameterized by ab‐initio calculations have led to significant improvements in understanding chemical reactions in heterogeneous catalysis. These studies have been facilitated by implementations which determine steady‐state coverages and rates of mean‐field micro‐kinetic models. As implemented in the open‐source kinetic modeling program, CatMAP, the conventional solution strategy is to use a root‐finding algorithm to determine the coverage of all intermediates through the steady‐state expressions, constraining all coverages to be non‐negative and to properly sum to unity. Though intuitive, this root‐finding strategy causes issues with convergence to solution due to these imposed constraints. In this work, we avoid explicitly imposing these constraints, solving the mean‐field steady‐state micro‐kinetic model in the space of number of sites instead of solving it in the space of coverages. We transform the constrained root‐finding problem to an unconstrained least‐squares minimization problem, leading to significantly improved convergence in solving micro‐kinetic models and thus enabling the efficient study of more complex catalytic reactions. [ABSTRACT FROM AUTHOR]