Equivariant Neural Networks Reveal How Host-Guest Interactions Shape ¹²⁹Xe NMR in Porous Liquids.

ABSTRACT: Understanding host-guest interactions in porous liquids (PLs) formed from porous organic cages (POCs) is pivotal in tailoring their physicochemical properties, therefore providing an avenue for engineering new PLs with enhanced functionalities. In this work, we demonstrate, for the first time, the use of an accurate and efficient machine-learning-based approach for atomistic modeling of host-guest interactions in large-scale PLs. The approach uses E(3)-equivariant graph neural networks (EGNNs) to construct a machine learning interatomic potential and a nuclear magnetic resonance machine learning model. The former enables machine learning molecular dynamics (MLMD) simulations, while the latter computes the ¹²⁹Xe isotropic chemical shift, δ_iso, from MLMD snapshot

SUBMITTER: Zakary O 

PROVIDER: S-EPMC12641476 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature