Kai-Zhu-2001/SelfTICA

Supporting Data for SelfTICA This repository provides supporting data, trained models, and simulation inputs for the paper: SelfTICA: contrastive learning of dynamical representations for molecular rare-event sampling and characterization SelfTICA is a self-supervised framework for learning slow dynamical representations from molecular simulation trajectories. The learned representations are subsequently analyzed with time-lagged independent component analysis… See the full description on the dataset page: https://huggingface.co/datasets/Kai-Zhu-2001/SelfTICA.

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