Mixed ionic/electronic polymer-based conductors(MIECs) are promising materials and have been widely used for a variety of applications in recent years, ranging from energy devices (batteries, fuel cells) to robotics and biomaterials (biosensors, biointerfaces). The design of MIECs involves a combination of multiple targeted properties such as a percolated phase architecture, balanced electronic/ionic conduction, flexibility and mechanical strength. By using a unique chain architecture that couples organic substituents (e.g., polythiolphenes) which are capable of electronic conductivity with ionic conductive hydrophilic blocks (e.g., ionic liquids and polyethylene glycol), the electronic conducting blocks are able to self-assemble into a crystalline structure while the ionic blocks remain amorphous inside percolating channels. Our research centers on designing ionic blocks with improved ionic conductivity within MIEC materials. To evaluate the ionic performance of different side chain chemistry in the ionic blocks, I will adopt a normalized ionic conductivity score. This score is calculated from molecular dynamics (MD) simulation and takes into account not only the ionic mobility and the number of mobile ions, but also the dissociation energy of ion pairs. In the traditional approach to material design, researchers usually synthesize the new materials based on intuition and prior experience. Such an approach, however, is rather inefficient due to the large size of the chemical space and the modest predictive accuracy of material properties based on limited experience. I propose to establish a new design approach that integrates MD and machine learning (ML) to efficiently sample the design space. By combining the advantages of high-throughput screening strategies and the accurate simulation of ionic transport afforded by our MD model, I can largely accelerate the material discovery process.