Alanine dipeptide has been commonly used as a model system for testing new methods. It is a 22-atom system with an acetyl group at the N-terminus and N-methyl amide at the C-terminus (Figure ). Initial coordinates for alanine dipeptide were obtained from . AM-BER ff14SB forcefield was used to prepare the system for MD simulations. It was then subjected to explicit solvation using the TIP3P water model. Alanine dipeptide was sub-jected to 50 ns of unbiased MD simulations. Trajectories were trained using VAMP neural networks with a time lag of 80 ps, training ratio of 0.9, and a batch size of 1000 that learned discretization in three metastable states. A list of hyperparameters used for network training is provided in Table . Starting structures were selected from the metastable states as per the DeepWEST protocol, followed by three independent WE simulation of 12 µs each with a total simulation time of 36 µs. Resampling time, τ was kept to be identical as used in WE and GaMD-WE runs, i.e., 10 ps. CVs were set to be the dihedral angles, ϕ and ψ, which were evenly spaced in bins of 0.17 rad with ϕ and ψ ranging between [-3.14 rad, 3.14 rad ].