# Assessing the Accuracy of Machine Learning Thermodynamic Perturbation Theory: Density Functional Theory and Beyond.

@article{Herzog2022AssessingTA, title={Assessing the Accuracy of Machine Learning Thermodynamic Perturbation Theory: Density Functional Theory and Beyond.}, author={Basile Herzog and Maur{\'i}cio Chagas da Silva and Bastien Casier and Michael Badawi and Fabien Pascale and Tom{\'a}{\vs} Bu{\vc}ko and S{\'e}bastien Leb{\`e}gue and Dario Rocca}, journal={Journal of chemical theory and computation}, year={2022} }

Machine learning thermodynamic perturbation theory (MLPT) is a promising approach to compute finite temperature properties when the goal is to compare several different levels of ab initio theory and/or to apply highly expensive computational methods. Indeed, starting from a production molecular dynamics trajectory, this method can estimate properties at one or more target levels of theory from only a small number of additional fixed-geometry calculations, which are used to train a machine…

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