The utilization of sockets relies on data stored exclusively in memory, excluding the requirement to exchange information as data stored on physical drives (except for logging purposes). This improves efficiency, particularly in local communication scenarios such as those encountered in MD simulations. The second approach employs a client-server architecture with two Python programs communicating via socket-based IPC, illustrated in Figure . The server program (server.py) is initialized before the dynamics begin, listening for requests on a designated port (code in Figure ). Then, Newton-X is launched, triggering the client script (client.py) at each timestep with specific state arguments. The client reads the command line (CLI) arguments given by Newton-X and requests a connection to the server, transmitting data upon successful socket connection (code in Figure ). Using the known directory structure of Newton-X, the server effortlessly accesses the current molecular coordinates, executes energy and gradient predictions with MLatom, and stores them correctly (code in Figure ). Following this, the server updates the client on the operation status, leading to client termination upon receipt. Newton-X then uses the predictions to progress with the dynamics. To demonstrate the potential enhancement of the client-server architecture, we devised a simple benchmark comprised of NAMD simulations using the two approaches described in the last subsection.