Desorption dynamics of interstellar molecule on amorphous solid water investigated by machine learning...

Area of Science:

• Astrochemistry
• Computational Chemistry
• Surface Science

Background:

• Interstellar amorphous solid water (ASW) surfaces are crucial for chemical evolution in molecular clouds.
• Understanding molecular interactions with ASW is key to elucidating these chemical processes.
• Accurate and efficient molecular dynamics (MD) simulations are needed to study ASW surface dynamics.

Purpose of the Study:

• To develop and apply a novel computational scheme, PaCS-MLP-MD, for simulating rare-event dynamics on ASW.
• To investigate the desorption mechanisms of aminoacetonitrile (AAN) from ASW surfaces.
• To provide insights into molecular processes relevant to astrochemistry.

Main Methods:

• Combined parallel cascade selection MD (PaCS-MD) with machine learning potential-based MD (MLP-MD) into a PaCS-MLP-MD framework.
• Simulated desorption of AAN from an ASW surface model using PaCS-MLP-MD.
• Performed unbiased MLP-MD simulations to study desorption and re-adsorption behaviors.

Main Results:

• Identified two distinct AAN desorption pathways from ASW: amino-first and nitrile-first.
• Revealed desorption and re-adsorption dynamics of AAN on the ASW surface through unbiased simulations.
• Demonstrated the capability of PaCS-MLP-MD to explore complex surface reaction dynamics without external forces or temperature increases.

Conclusions:

• The PaCS-MLP-MD method offers a robust framework for studying astrochemically relevant molecular processes on ASW.
• The identified AAN desorption pathways contribute to a deeper understanding of surface chemistry in interstellar environments.
• These findings enhance our knowledge of molecular interactions and evolution within molecular clouds.