Machine learning enabled molecular dynamics-Monte Carlo framework for nanoconfined fluid adsorption (opens in new tab)

Nanoconfined fluid is central to many engineering applications such as shale energy production, carbon sequestration, and molecular separations. While classical molecular dynamics (MD) simulation provides essential atomistic detail, its prohibitive computational cost severely limits accessible time and length scales. Hybrid MD-Monte Carlo (MDMC) methods accelerate sampling but lack generality beyond their trained conditions. In this work, we introduce an AI-assisted MDMC framework that overco...