The present work describes a parallel computational framework for CO2 sequestration simulation by coupling reservoir simulation and molecular dynamics (MD) on massively parallel HPC systems. In this framework, a parallel reservoir simulator, Reservoir Simulation Toolbox (RST), solves the flow and transport equations that describe the subsurface flow behavior, while the molecular dynamics simulations are performed to provide the required physical parameters. Numerous technologies from different fields are employed to make this novel coupled system work efficiently. One of the major applications of the framework is the modeling of large scale CO2 sequestration for long-term storage in the subsurface geological formations, such as depleted reservoirs and deep saline aquifers, which has been proposed as one of the most attractive and practical solutions to reduce the CO2 emission problem to address the global-warming threat. To effectively solve such problems, fine grids and accurate prediction of the properties of fluid mixtures are essential for accuracy. In this work, the CO2 sequestration is presented as our first example to couple the reservoir simulation and molecular dynamics, while the framework can be extended naturally to the full multiphase multicomponent compositional flow simulation to handle more complicated physical process in the future. Accuracy and scalability analysis are performed on an IBM BlueGene/P and on an IBM BlueGene/Q, the latest IBM supercomputer. Results show good accuracy of our MD simulations compared with published data, and good scalability are observed with the massively parallel HPC systems. The performance and capacity of the proposed framework are well demonstrated with several experiments with hundreds of millions to a billion cells. To our best knowledge, the work represents the first attempt to couple the reservoir simulation and molecular simulation for large scale modeling. Due to the complexity of the subsurface systems, fluid thermodynamic properties over a broad range of temperature, pressure and composition under different geological conditions are required, for which the experimental results are limited. Although equations of state can reproduce the existing experimental data within certain ranges of conditions, their extrapolation out of the experimental data range is still limited. The presented framework will definitely provide better flexibility and predictability compared with conventional methods.