Benchmarking of Fast and Interpretable UF Machine Learning Potentials

Ab initio methods offer great promise for materials design, but they come with a hefty computational cost. Recent advances with Machine Learning potentials (MLPs) have revolutionized molecular dynamic simulations by providing high accuracies similar to ab initio models but at much reduced computational cost. Our study evaluates the Ultra-Fast Potential (UF3), employing linear regression with cubic B-spline basis for assessing effective two- and three-body potentials. On benchmarking, UF3 displays comparable precision to established models like GAP, MTP, NNP(Behler Parrinello), and qSNAP MLPs, yet is significantly faster by two to three orders of magnitude. A distinct feature of UF3 is its capability to render visual representations of learned two- and three-body potentials, shedding light on potential gaps in the learning model. In refining UF3's performance, a comprehensive sweep of the hyperparameter space was undertaken, emphasizing finer granularity in zones indicative of optimal performance. This endeavor aims to provide insights into the UF3 hyperparameter space smoothness, and offer users a foundational set of default set of hyperparameters as a starting point for optimization. While our current optimizations are concentrated on energies and forces, we are primed to broaden UF3’s evaluation spectrum, focusing on its applicability in critical areas of Molecular Dynamics simulations. The outcome of these investigations will not only enhance the predictability and usability of UF3 but also pave the way for its broader applications in advanced materials discovery and simulations.