Computing RPA adsorption enthalpies by machine learning thermodynamic perturbation theory

Correlated quantum-chemical methods for condensed matter systems, such as the random phase approximation (RPA), hold the promise of reaching a level of accuracy much higher than that of conventional density functional theory approaches. However, the high computational cost of such methods hinders their broad applicability, in particular for finite-temperature molecular dynamics simulations. We propose a method that couples machine learning techniques with thermodynamic perturbation theory to estimate finite-temperature properties using correlated approximations [1]. We apply this approach to compute the enthalpies of adsorption in zeolites and show that reliable estimates can be obtained by training a machine learning model with as few as 10 RPA energies. This approach paves the way to the broader use of computationally expensive quantum-chemical methods to predict the finite-temperature properties of condensed matter systems.

[1] Bilal Chehaibou, Michael Badawi, Tomas Bucko, Timur Bazhirov, and Dario Rocca, J. Chem. Theory Comput. (2019) DOI: 10.1021/acs.jctc.9b00782