Correcting systematic errors in the likelihood optimization of underdamped Langevin models of molecular dynamics trajectories
Since Kramers' pioneering work in 1940, significant efforts have been devoted to studying Langevin equations applied to physical and chemical reactions projected onto few collective variables, with particular focus on the inference of their parameters. While the inference for overdamped Langevin equations is well-established and widely applied, a notable gap remains in the literature for underdamped Langevin equation. This gap arises from the challenge of accessing velocities solely through finite differences of positions, resulting in spurious correlations. In this letter, we propose an analytical correction for these correlations, specifically designed for a likelihood-maximization algorithm that exploits short, non-ergodic trajectories that can be obtained at reasonable numerical cost. The accuracy and robustness of our approach are tested on a benchmark case and a realistic system. This work paves the way for applying generalized Langevin equation inference to activated phenomena, such as chemical reactions, in several scientific domains.