Benchmarking local geometry optimization algorithms for computational materials discovery

Most current computational materials discovery workflows are dependent on efficient and reliable structure relaxations in order to predict a hypothetical material's equilibrium structure. Besides an accurate description of the potential energy surface, this requires efficient and reliable optimization algorithms. In this study, we investigate how different optimization algorithms affect the relaxation of inorganic crystal structures using general-purpose machine-learned interatomic potentials. Our findings emphasize the critical role of optimizer selection in large-scale computational materials science workflows, particularly in the context of element-substitution-based materials discovery. This can guide the community toward choosing appropriate algorithms for the efficient and reliable prediction of new crystalline materials.