In fact, an emerging tool in modeling dynamics is machine learning of interatomic potentials that can approach both the accuracy of the underlying ab-initio method and the speed of analytical potentials. For applications in simulations of femtosecond laser-induced desorption, some of us have recently used the embedded atom neural network method to create a machine learning potential of CO/Pd(111) that accurately reproduced the ab-initio results of ref 18 at a much lower computational cost. Since the ab-initio data for the paradigmatic system of H/Ru(0001) already exist, we have used these data to create a neural network potential. We then use this potential to simulate femtosecond laser-induced desorption with longer integration times, larger cell sizes, more coverages and isotope ratios as well as laser fluences than hitherto possible. This way, new and more detailed insight is gained into a non-trivial surface photoreaction.