TY - JOUR
T1 - Symmetry- and gradient-enhanced Gaussian process regression for the active learning of potential energy surfaces in porous materials
AU - Krondorfer, Johannes K.
AU - Binder, Christian W.
AU - Hauser, Andreas W.
N1 - Publisher Copyright:
© 2023 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
PY - 2023/7/7
Y1 - 2023/7/7
N2 - The theoretical investigation of gas adsorption, storage, separation, diffusion, and related transport processes in porous materials relies on a detailed knowledge of the potential energy surface of molecules in a stationary environment. In this article, a new algorithm is presented, specifically developed for gas transport phenomena, which allows for a highly cost-effective determination of molecular potential energy surfaces. It is based on a symmetry-enhanced version of Gaussian process regression with embedded gradient information and employs an active learning strategy to keep the number of single point evaluations as low as possible. The performance of the algorithm is tested for a selection of gas sieving scenarios on porous, N-functionalized graphene and for the intermolecular interaction of CH4 and N2.
AB - The theoretical investigation of gas adsorption, storage, separation, diffusion, and related transport processes in porous materials relies on a detailed knowledge of the potential energy surface of molecules in a stationary environment. In this article, a new algorithm is presented, specifically developed for gas transport phenomena, which allows for a highly cost-effective determination of molecular potential energy surfaces. It is based on a symmetry-enhanced version of Gaussian process regression with embedded gradient information and employs an active learning strategy to keep the number of single point evaluations as low as possible. The performance of the algorithm is tested for a selection of gas sieving scenarios on porous, N-functionalized graphene and for the intermolecular interaction of CH4 and N2.
UR - http://www.scopus.com/inward/record.url?scp=85164255442&partnerID=8YFLogxK
U2 - 10.1063/5.0154989
DO - 10.1063/5.0154989
M3 - Article
C2 - 37417752
AN - SCOPUS:85164255442
SN - 0021-9606
VL - 159
JO - The Journal of Chemical Physics
JF - The Journal of Chemical Physics
IS - 1
M1 - 014115
ER -