Quantum Monte Carlo study on Van der Waals interactions in hydrogen adsorption on a silicon-carbide nanotube
ORAL
Abstract
Hydrogen is one of the alternatives being pursued for a clean energy resource. In order to remain competitive with other technologies, further improvements to its storage capacity and safety are necessary. Physisorption on high surface area nanostructures is one of the promising material-based solution to the problem. This solution relies on van der Waals (vdW) interaction to bind the hydrogen, with ideal interaction energy estimated at around 3.5-11.5 kcal/mol for achieving optimal adsorption at room temperature. To accelerate screening of materials capable of achieving these values, a reliable materials simulation scheme is essential. Unfortunately, vdW interaction is not treated by conventional density functional theory (DFT). Recent years have seen the development of new vdW corrections to the DFT based on diverse approach, ranging from simpler pairwise corrections to nonlocal functionals; however their accuracy can be strongly dependent on the studied system. In this work we applied, for the first time, diffusion Monte Carlo (DMC) to model the interaction of hydrogen on an (5,5) armchair silicon carbide nanotube (SiCNT) [1]. Unlike DFT, DMC is able to directly capture the dispersion interactions through stochastic solution of the exact many-body Hamiltonian. This provides a reliable benchmark for the vdW corrections. We found all of the tested vdW corrections to be reasonably accurate, thus present a clear improvement over conventional DFT. The vdW contribution to the adsorption was not insignificant at about 1 kcal/mol or 9-29% of the expected adsorption energy.
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Publication: G. I. Prayogo, H. Shin, A. Benali, R. Maezono, K. Hongo, Importance of Van der Waals Interactions in Hydrogen Adsorption on a Silicon-carbide Nanotube Revisited with vdW-DFT and Quantum Monte Carlo, ACS Omega 6, 38, 24630–24636 (2021), DOI: 10.1021/acsomega.1c03318
Presenters
Genki I Prayogo
School of Information Science, JAIST, Nomi, Ishikawa, Japan, School of Information Science, JAIST
Authors
Genki I Prayogo
School of Information Science, JAIST, Nomi, Ishikawa, Japan, School of Information Science, JAIST
Hyeondeok Shin
Computational Science Division, Argonne National Laboratory, Argonne, IL, United States
Anouar Benali
Computational Science Division, Argonne National Laboratory, Computational Science Division, Argonne National Laboratory, Argonne, IL, United States, Argonne National Labratory, Argonne National Laboratory
Ryo Maezono
School of Information Science, JAIST, School of Information Science, JAIST, Nomi, Ishikawa, Japan, School of Information Science, JAIST, Nomi, Ishikawa, Japan.
Kenta Hongo
Research Center for Advanced Computing Infrastructure, JAIST, Research Center for Advanced Computing Infrastructure, JAIST, Nomi, Ishikawa, Japan, Research Center for Advanced Computing Infrastructure, JAIST, Nomi, Ishikawa, Japan.