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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