Inefficient IPC implementation with excessive disk reading/writing can significantly bottleneck MD simulation performance. While this bottleneck used to have minimal impact compared to solving the electronic Schrödinger equation (ES evaluation step) due to limited computational resources in the past, recent advancements in semiempirical, parameterized, and Machine Learning (ML) potentials have reduced PES calculation times, exposing this interfacing issue. Moreover, the initialization time of the ES program can present an even bigger problem when implemented in higher-level languages such as Python and Julia, which have gained terrain in computational chemistry developments. This study explores an alternative to file-based IPC for interfacing ES with MD programs through system-native, language-agnostic networking IPC endpoints known as sockets. We aim to showcase potential speed improvements in MD simulations by comparing a basic ML MD implementation with a client-server architecture for managing energy and gradient requests between MD and ML-and potentially QM-programs. We will show that this architecture is particularly efficient for a Fortran MD program, which calls a Python ES program sequentially.