Nonadiabatic dynamics simulations based on machine learning potentials are still in their early development stage. As developers, our goal has been to create a flexible software interface, paving the way for easily plugin new models into an automated framework to accelerate the performance tests and deployment of machine learning potentials. The interface between Newton-X CS and MLatom enables machine learning for nonadiabatic dynamics bearing these goals in mind. Potential energies, energy gradients, and state couplings can be obtained with diverse kernel methods and neural networks, with various local and global descriptors. In this interface, Newton-X does not know that the electronic properties are ML-based. It calls MLatom as if it were a conventional electronic structure program.