Computing RPA adsorption enthalpies by machine learning thermodynamic perturbation theory

B Chehaibou, M Badawi, T Bucko… - Journal of Chemical …, 2019 - ACS Publications
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 …

Exploring the limits of second-and third-order Møller–Plesset perturbation theories for noncovalent interactions: Revisiting MP2. 5 and assessing the importance of …

M Loipersberger, LW Bertels, J Lee… - Journal of chemical …, 2021 - ACS Publications
This work systematically assesses the influence of reference orbitals, regularization, and
scaling on the performance of second-and third-order Møller–Plesset perturbation theory …

Quantifying the impact of halogenation on intermolecular interactions and binding modes of aromatic molecules

R Tyagi, A Zen, VK Voora - The Journal of Physical Chemistry A, 2023 - ACS Publications
Halogenation of aromatic molecules is frequently used to modulate intermolecular
interactions with ramifications for optoelectronic and mechanical properties. In this work, we …

Beyond the random phase approximation with a local exchange vertex

M Hellgren, N Colonna, S De Gironcoli - Physical Review B, 2018 - APS
With the aim of constructing an electronic structure approach that systematically goes
beyond the GW and random phase approximation (RPA) we introduce a vertex correction …

Benchmarking several van der Waals dispersion approaches for the description of intermolecular interactions

J Claudot, WJ Kim, A Dixit, H Kim, T Gould… - The Journal of …, 2018 - pubs.aip.org
Seven methods, including three van der Waals density functionals (vdW-DFs) and four
different variants of the Tkatchenko-Scheffler (TS) methods, are tested on the A24, L7, and …

Assessing the accuracy of machine learning thermodynamic perturbation theory: Density functional theory and beyond

B Herzog, M Chagas da Silva, B Casier… - Journal of Chemical …, 2022 - ACS Publications
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 …

Diffusion Monte Carlo Study on Relative Stabilities of Boron Nitride Polymorphs

Y Nikaido, T Ichibha, K Hongo… - The Journal of …, 2022 - ACS Publications
Although boron nitride (BN) is a well-known compound widely used for engineering and
scientific purposes, the phase stability of its polymorphs, one of its most fundamental …

Performance and scope of perturbative corrections to random-phase approximation energies

GP Chen, MM Agee, F Furche - Journal of chemical theory and …, 2018 - ACS Publications
It has been suspected since the early days of the random-phase approximation (RPA) that
corrections to RPA correlation energies result mostly from short-range correlation effects and …

Efficient calculation of beyond RPA correlation energies in the dielectric matrix formalism

M Beuerle, D Graf, HF Schurkus… - The Journal of chemical …, 2018 - pubs.aip.org
We present efficient methods to calculate beyond random phase approximation (RPA)
correlation energies for molecular systems with up to 500 atoms. To reduce the …

Range-separated double-hybrid density-functional theory with coupled-cluster and random-phase approximations

C Kalai, B Mussard, J Toulouse - The Journal of Chemical Physics, 2019 - pubs.aip.org
We construct range-separated double-hybrid (RSDH) schemes which combine coupled-
cluster or random-phase approximations (RPAs) with a density functional based on a two …