Fortunately, the recent introduction of machine learning inter-atomic potentials (MLIPs) has enabled simulations of such long-timescale processes within reasonable computational time and at sufficient fidelity for complex materials . The first generation of MLIPs achieved speedups of three orders of magnitude over DFT, making nanosecond simulations possible in many cases . Even more recently, a set of Ultra-Fast machine learning Potentials (UFPs) was introduced which provides a speedup of nearly five orders of magnitude over DFT, while maintaining the same accuracy as some of the most accurate MLIPs such as the Gaussian Approximation Potential (GAP) . With the UFP, it is now possible to routinely simulate up to the microsecond timescale almost at DFT accuracy .