CO adsorption sites on interstellar water ices explored with machine learning potentials

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 water clusters of different sizes (22–60 water molecules). The DFT method was selected after extensive benchmarking. With this potential, we built realistic nonporous and porous ASW surfaces and computed a BE distribution. We used symmetry-adapted perturbation theory to rationalize the interactions of CO with the different binding sites. Results. We find that both ASW morphologies yield similar Gaussian-like BE distributions, with mean values near 900 K. However, the nature of the binding interactions is rather different and is critically dependent on surface roughness and dangling OH bonds. Simulated temperature-programmed desorption (TPD) curves reproduce experimental trends across several coverage regimes. From an astrochemical point of view, the application of the full BE distribution has a dramatic influence on the CO distribution in protoplanetary disks, leading to a broader CO snowline region, improving predictions of CO gas-ice partitioning, and suggesting an equally broader distribution of organics in these objects.