Gas sorption in polymer matrices is crucial for various industrial applications, ranging from gas separation membranes to food packaging materials. Understanding and predicting these phenomena requires molecular-level insights that account for polymer-gas interactions and potential matrix swelling effects. This presentation demonstrates advanced automated simulation workflows that combine molecular dynamics (MD) and Monte Carlo (MC) methods to predict gas sorption behavior in polymeric systems with high efficiency and statistical reliability.

Our approach addresses the computational challenge of achieving robust statistical sampling by automatically generating multiple independent initial configurations and orchestrating large-scale simulation campaigns. The automated workflow handles the entire simulation pipeline, including intelligent input file generation, optimal job dispatch across computational resources, and systematic results collection and analysis. This automation not only eliminates user error and reduces time-to-results but also enables comprehensive statistical analysis.

Case studies will illustrate the workflow's capabilities in predicting sorption isotherms, and swelling behavior for industrially relevant polymer-gas systems. The methodology provides quantitative predictions that can guide material selection, process optimization, and new material design, bridging the gap between molecular-level understanding and industrial application. This computational approach offers a cost-effective alternative to extensive experimental screening while providing molecular-level insights unavailable through traditional characterization methods.