Unconventional growth of methane hydrates: A molecular dynamics and machine learning study

Abstract

Natural gas hydrate reservoirs in natural settings subjected to external driving forces are deformed and locally dissociated, while they reform due to endothermic dissociation reaction. Here we report a molecular dynamics (MD) and machine learning (ML) study of unconventional growth characteristics of methane hydrates (MH). MD results show that depending on the strain of MH substrate, methane clathrate hydrates grow via four distinct growth modes, in which diverse unconventional cages are critically involved. Interestingly, a novel new MH structure named as MH-VII composed of 51263, 51472 and 425861 polyhedral cages is discovered. Moreover, a long short-term memory (LSTM) neural network-based ML model using short-time MD information is developed to effectively predict the complex dynamic growth of MH in terms of clathrate cages and F4 order parameter. This work provides new insights and perspectives into the growth of clathrate hydrates, and as-developed LSTM-based ML model opens a critical pathway for exploring time-dependent behaviors of clathrate hydrates under complex conditions.