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Laura J. S. Lopes

Publications and source records attributed to Laura J. S. Lopes.

2 recordsLinked to original sources

Human learning for molecular simulations: the Collective Variables Dashboard in VMD

The Collective Variables Dashboard is a software tool for real-time, seamless exploration of molecular structures and trajectories in a customizable space of collective variables. The Dashboard arises from the integration of the Collective Variables Module with the visualization software VMD, augmented with a fully discoverable graphical interface offering interactive workflows for the design and analysis of collective variables. Typical use cases include a priori design of collective variables for enhanced sampling and free energy simulations as well as post-mortem analysis of any type of simulation or collection of structures in a collective variable space. A combination of those cases commonly occurs when preliminary simulations, biased or unbiased, reveal that an optimized set of collective variables is necessary to improve sampling in further simulations. Then the Dashboard provides an efficient way to intuitively explore the space of likely collective variables, validate them on existing data, and use the resulting collective variable definitions directly in further biased simulations using the Collective Variables Module. We illustrate the use of the Dashboard on two applications: discovering coordinates to describe ligand unbinding from a protein binding site, and designing volume-based variables to bias the hydration of a transmembrane pore.

physics.comp-ph↗

Analysis of the Adaptive Multilevel Splitting method on the isomerization of alanine dipeptide

We apply the Adaptive Multilevel Splitting method to the Ceq -> Cax transition of alanine dipeptide in vacuum. Some properties of the algorithm are numerically illustrated, such as the unbiasedness of the probability estimator and the robustness of the method with respect to the choice of the reaction coordinate. We also calculate the transition time obtained via the probability estimator, using an appropriate ensemble of initial conditions. Finally, we show how the Adaptive Multilevel Splitting method can be used to compute an approximation of the committor function.

physics.chem-ph↗