
Automated design of collective variables using supervised machine learning
Selection of appropriate collective variables for enhancing sampling of ...
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Nonlinear Discovery of Slow Molecular Modes using Hierarchical Dynamics Encoders
The success of enhanced sampling molecular simulations that accelerate a...
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Molecular enhanced sampling with autoencoders: Onthefly collective variable discovery and accelerated free energy landscape exploration
Macromolecular and biomolecular folding landscapes typically contain hig...
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Variational Koopman models: slow collective variables and molecular kinetics from short offequilibrium simulations
Markov state models (MSMs) and Master equation models are popular approa...
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Learning emergent PDEs in a learned emergent space
We extract datadriven, intrinsic spatial coordinates from observations ...
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Variational Selection of Features for Molecular Kinetics
The modeling of atomistic biomolecular simulations using kinetic models ...
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Turning machines
Molecular robotics is challenging, so it seems best to keep it simple. W...
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Decision functions from supervised machine learning algorithms as collective variables for accelerating molecular simulations
Selection of appropriate collective variables for enhancing molecular simulations remains an unsolved problem in computational biophysics. In particular, picking initial collective variables (CVs) is particularly challenging in higher dimensions. Which atomic coordinates or transforms there of from a list of thousands should one pick for enhanced sampling runs? How does a modeler even begin to pick starting coordinates for investigation? This remains true even in the case of simple two state systems and only increases in difficulty for multistate systems. In this work, we attempt to solve the initial CV problem using a datadriven approach inspired by supervised machine learning literature. In particular, we show how the decision functions in supervised machine learning (SML) algorithms can be used as initial CVs for accelerated sampling. Using solvated alanine dipeptide and Chignolin miniprotein as our test cases, we illustrate how the distance to the Support Vector Machines decision hyperplane, the output probability estimates from Logistic Regression, and other classifiers may be used to reversibly sample slow structural transitions. We discuss the utility of other SML algorithms that might be useful for identifying CVs for accelerating molecular simulations.
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