CO Adsorption Sites on Interstellar Water Ices Explored with Machine Learning Potentials. Binding energy distributions and snowline
Giulia M. Bovolenta, Germ\'an Molpeceres, Kenji Furuya, Johannes K\"astner, Stefan Vogt-Geisse

TL;DR
This study uses machine learning potentials trained on DFT data to accurately characterize CO binding energy distributions on amorphous water ice surfaces, impacting astrochemical models of protoplanetary disks.
Contribution
We developed a machine learning potential to derive realistic CO binding energy distributions on interstellar water ices, enhancing astrochemical modeling accuracy.
Findings
BE distributions are Gaussian-like with mean near 900 K
Surface roughness and dangling bonds influence binding interactions
Broader CO snowline region in protoplanetary disks predicted
Abstract
Context. Carbon monoxide (CO) is arguably the most important molecule for interstellar organic chemistry. Its binding to amorphous solid water (ASW) ice regulates both diffusion and desorption processes. Accurately characterizing the CO binding energy (BE) is essential for realistic astrochemical modeling. Aims. We aim to derive a statistically robust and physically accurate distribution of CO BEs on ASW surfaces, and to evaluate its implications for laboratory temperature-programmed desorption experiments and interstellar chemistry, with a focus on protoplanetary disks. Methods. We trained a machine-learned potential (MLP) on 8321 density functional theory (DFT) energies and gradients of CO interacting with differently-sized water clusters (22-60 water molecules). The DFT method was selected after extensive benchmark. With this potential we built realistic non-porous and porous ASW…
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Taxonomy
TopicsMethane Hydrates and Related Phenomena · Carbon Dioxide Capture Technologies · Atmospheric and Environmental Gas Dynamics
