| On-the-fly HTMD: on-the-fly high-throughput MD |
Iterates between multiple short MD simulations (HTMD) and use of an MSM to learn a simplified model of the system to decide from where to respawn the next batch of simulations. |
[167] |
| Extended DM-d-MD: extended diffusion-map-directed MD; iMapD: intrinsic map dynamics |
Uses diffusion maps, a non-linear manifold machine learning technique for dimensionality reduction to select regions of conformational space from an initial unbiased MD simulation from which to launch new rounds of MD simulations. Unbiased simulations are used because CVs based on diffusion maps do not explicitly map to atomic coordinates, and so cannot be used in US or metadynamics, which require calculation of the gradient of the CV with respect to the atomic coordinates [55] |
[168,169] |
| VAC-metadynamics |
Uses tICA to analysis an initial WT-metadynamics simulation to obtain more effective CVs that are used in a second WT-metadynamics simulation. Not strictly iterative. |
[70] |
| RAVE: reweighted autoencoded variational Bayes for enhanced sampling |
Iterates between enhanced sampling simulations and deep learning using variational autoencoders to learn an optimum but still physically interpretable reaction coordinate, as well as the probability distribution along this coordinate, which are then used to bias the enhanced sampling simulations. |
[170] |
| REAP: reinforcement learning based adaptive sampling |
Uses reinforcement learning to estimate the importance of CVs on-the-fly while exploring the conformational landscape. Requires an initial unbiased MD simulation from which to generate a dictionary of CVs and their trial weights. |
[171] |
| MESA: molecular enhanced sampling with autoencoders |
Iterates between umbrella sampling along trial CVs and using an auto-associative artificial neural network with a nonlinear encoder and decoder to learn CVs. |
[172] |
