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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 si...
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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: On-the-fly 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 off-equilibrium 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 data-driven, intrinsic spatial coordinates from observations ...
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Transferable neural networks for enhanced sampling of protein dynamics
Variational auto-encoder frameworks have demonstrated success in reducin...
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DeepDriveMD: Deep-Learning Driven Adaptive Molecular Simulations for Protein Folding
Simulations of biological macromolecules play an important role in under...
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Automated design of collective variables using supervised machine learning
Selection of appropriate collective variables for enhancing sampling of 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 multi-state systems. In this work, we solve the initial CV problem using a data-driven approach inspired by the filed of supervised machine learning. In particular, we show how the decision functions in supervised machine learning (SML) algorithms can be used as initial CVs (SML_cv) for accelerated sampling. Using solvated alanine dipeptide and Chignolin mini-protein as our test cases, we illustrate how the distance to the Support Vector Machines' decision hyperplane, the output probability estimates from Logistic Regression, the outputs from deep neural network classifiers, 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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