relevant quantities in Markovian milestoning and thereby avoid to make the second twisted sampling to compute the rate. Indeed, the formalism developed in Ref.  to approximate the dynamics by a continuous-time Markov chain does not rely on the dynamics being at equilibrium. For completeness let us briefly recall the main objects in Markovian milestoning and indicate how to compute them in the present context. If, following Ref. , we define the milestones as the common boundaries between any two adjacent Voronoi cells and denote these milestones by $S_i$ with $i=1,2,\dots,N$, the key quantity to approximate the transitions between the milestones by a continuous-time Markov chain is the rate matrix whose off-diagonal elements can be estimated as $$\label{eq:qMLE}
  q_{ij} = 
  \begin{cases}
  N_{ij}/R_i \quad &  \text{if $R_i\ne 0$}\\
 \quad 0  \quad &\text{if $R_i = 0$}.
  \end{cases}$$ where $$\label{eq:NijRi}
    N_{ij} = 
    \sum_{\alpha=1}^{\Lambda}\pi_{\alpha} \frac{N_{ij}^{\alpha}}{T_\alpha}, \qquad
    R_{i} = 
    \sum_{\alpha=1}^{\Lambda} \pi_{\alpha} \frac{R_{i}^{\alpha}}{T_\alpha}.$$ Here $T_{\alpha}$ is the total simulation time in cell $B_{\alpha}$ (as before), whereas $N_{ij}^\alpha $ is the total number of times the trajectory went from milestone $S_i$ to milestone $S_j$ and $R_i^\alpha$ is the total amount of time the trajectory is assigned to milestone $S_i$ in cell $B_\alpha$, i.e. the total amount of time 