graph LR;
1[Choose Basis]-->A(Legendre polynomial)-->AA[Choose basis size];
1-->B(Neural network)-->BB[Choose network architecture];
2[Choose Target]-->C(Uniform distribution);
2-->D(Well-tempered distribution)-->D1[Choose bias factor];
D-->D2[Choose free energy localization]
D2-->D2A(No localization)
D2-->D2B(Localized with sigmoidal cutoff)
3(Update)-->E[Choose averaging method]
E-->E1(Instantaneous)
E-->E2(Running average)
E-->E3(Weighted running average)-->E3A[Choose exponent]
E-->E4(Moving window average)-->E4A[Choose window size]
3-->F[Choose optimizer]
F-->F1(Stochastic PyTorch optimizers)
F-->F2(Original VES 2nd order optimizer)
3-->G[Choose learning rate]
G-->G1[Choose learning rate decay]
G1-->G1A(No decay)
G1-->G1B(Decay below KL-divergence cutoff)
G1B-->G1B1[Choose cutoff]
G1B-->G1B2[Choose exponent]
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Variationally enhanced sampling for single-particle langevin dynamics with neural network bias potentials and path collective variables. Based on OpenMM + PyTorch.
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apallath/pyves
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Variationally enhanced sampling for single-particle langevin dynamics with neural network bias potentials and path collective variables. Based on OpenMM + PyTorch.
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