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Herzog B, Chagas da Silva M, Casier B, Badawi M, Pascale F, Bučko T, Lebègue S, Rocca D. Assessing the Accuracy of Machine Learning Thermodynamic Perturbation Theory: Density Functional Theory and Beyond. J Chem Theory Comput 2022; 18:1382-1394. [PMID: 35191699 DOI: 10.1021/acs.jctc.1c01034] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
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 learning model. However, as MLPT is based on thermodynamic perturbation theory (TPT), inaccuracies might arise when the starting point trajectory samples a configurational space which has a small overlap with that of the target approximations of interest. By considering case studies of molecules adsorbed in zeolites and several different density functional theory approximations, in this work we assess the accuracy of MLPT for ensemble total energies and enthalpies of adsorption. It is shown that problematic cases can be detected even without knowing reference results and that even in these situations it is possible to recover target level results within chemical accuracy by applying a machine-learning-based Monte Carlo (MLMC) resampling. Finally, on the basis of the ideas developed in this work, we assess and confirm the accuracy of recently published MLPT-based enthalpies of adsorption at the random phase approximation level, whose high computational cost would completely hinder a direct molecular dynamics simulation.
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Affiliation(s)
- Basile Herzog
- Université de Lorraine and CNRS, Laboratoire de Physique et Chimie Théorique, UMR 7019, 54506 Vandœuvre-lés-Nancy, France
| | - Maurício Chagas da Silva
- Université de Lorraine and CNRS, Laboratoire de Physique et Chimie Théorique, UMR 7019, 54506 Vandœuvre-lés-Nancy, France
| | - Bastien Casier
- Université de Lorraine and CNRS, Laboratoire de Physique et Chimie Théorique, UMR 7019, 54506 Vandœuvre-lés-Nancy, France
| | - Michael Badawi
- Université de Lorraine and CNRS, Laboratoire de Physique et Chimie Théorique, UMR 7019, 54506 Vandœuvre-lés-Nancy, France
| | - Fabien Pascale
- Université de Lorraine and CNRS, Laboratoire de Physique et Chimie Théorique, UMR 7019, 54506 Vandœuvre-lés-Nancy, France
| | - Tomáš Bučko
- Department of Physical and Theoretical Chemistry, Faculty of Natural Sciences, Comenius University in Bratislava, Mlynská Dolina, Ilkovičova 6, SK-84215 Bratislava, Slovakia.,Institute of Inorganic Chemistry, Slovak Academy of Sciences, Dúbravská cesta 9, SK-84236 Bratislava, Slovakia
| | - Sébastien Lebègue
- Université de Lorraine and CNRS, Laboratoire de Physique et Chimie Théorique, UMR 7019, 54506 Vandœuvre-lés-Nancy, France
| | - Dario Rocca
- Université de Lorraine and CNRS, Laboratoire de Physique et Chimie Théorique, UMR 7019, 54506 Vandœuvre-lés-Nancy, France
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