The main bottleneck in the file-based IPC program was attributed to library import time (1.82 ± 0.04 s), which succeeded by loading ML models and executing predictions (0.78 ± 0.02 s) and then file operations (0.5 ± 0.3 ms). The key advantage of the client-server architecture lies in the handling of third-party libraries-those beyond the standard Python library. These are imported exclusively on the server side, while the client only imports built-in libraries. The impact on startup is substantial: while the server requires 1.8 s on average to import all necessary libraries, which are imported at each time step in the file-based IPC program, the client starts up in a mere 12 ms. Aiming to improve the performance of MD simulations, we have successfully implemented socketbased IPC to facilitate the communication between Newton-X and MLatom. This implementation resulted in a significant reduction in computational time compared to the direct file-based IPC approach, mainly due to the elimination of startup overhead in the Python script used. As a consequence, these results will enable more trajectories to be run for longer timescales, in less computational time, and with a reduced carbon footprint.