Machine learning for accuracy in density functional approximations
This work addresses the problem of enhancing computational efficiency and accuracy in electronic structure methods for computational chemistry and materials design, but it is incremental as it reviews existing progress.
The paper reviews recent progress in using machine learning to improve the accuracy of density functional approximations, aiming to boost predictive power to chemical accuracy and correct fundamental errors, with examples of models applied to systems outside their training sets.
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.