Accurate machine learning interatomic potentials for polyacene molecular crystals: application to single molecule host-guest systems

Abstract Emerging machine learning interatomic potentials (MLIPs) offer a promising solution for large-scale accurate material simulations, but stringent tests related to the description of vibrational dynamics in molecular crystals remain scarce. Here, we develop a general MLIP by leveraging the graph neural network-based MACE architecture and active-learning strategies to accurately capture vibrational dynamics across a range of polyacene-based molecular crystals, namely naphthalene, anthracene, tetracene and pentacene. Through careful error propagation, we show that these potentials are accurate and enable the study of anharmonic vibrational features, vibrational lifetimes, and vibrational coupling. In particular, we investigate large-scale host-guest systems based on these molecular crystals, showing the capacity of molecular-dynamics-based techniques to explain and quantify vibrational coupling between host and guest nuclear motion. Our results establish a framework for understanding vibrational signatures in large-scale complex molecular systems and thus represent an important step for engineering vibrational interactions in molecular environments.