Generalization of ML-guided path-sampling and latent-space simulators

Determine the generalization properties of machine-learning–infused path-sampling strategies and latent-space simulators for molecular dynamics across diverse molecular systems and processes, and ascertain the conditions under which these approaches can reliably scale to larger systems without loss of fidelity.

Background

Within the Discussion, the authors compare TITO to complementary approaches such as machine-learning–guided path-sampling and latent space simulators. While these methods show promise for scaling to larger systems, the authors explicitly state that generalization remains an open challenge.

This problem concerns whether such methods can consistently transfer their predictive capabilities across different molecular processes and system sizes, a prerequisite for practical, broadly applicable dynamical modeling in computational chemistry and biophysics.