SelfTICA reformulates collective-variable discovery as contrastive dynamical representation learning on time-lagged data, decoupling feature learning from slow-mode extraction to produce reusable collective variables from limited or biased trajectories.
arXiv preprint arXiv:2410.18019 , year=
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Contrastive learning of dynamical representations for enhanced molecular sampling
SelfTICA reformulates collective-variable discovery as contrastive dynamical representation learning on time-lagged data, decoupling feature learning from slow-mode extraction to produce reusable collective variables from limited or biased trajectories.