  In this work, we seek to simulate rare transitions between metastable states
using score-based generative models. An efficient method for generating
high-quality transition paths is valuable for the study of molecular systems
since data is often difficult to obtain. We develop two novel methods for path
generation in this paper: a chain-based approach and a midpoint-based approach.
The first biases the original dynamics to facilitate transitions, while the
second mirrors splitting techniques and breaks down the original transition
into smaller transitions. Numerical results of generated transition paths for
the M\"uller potential and for Alanine dipeptide demonstrate the effectiveness
of these approaches in both the data-rich and data-scarce regimes.
