Connecting dynamic reweighting Algorithms: Derivation of the dynamic reweighting family tree
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2020-12-21
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Journal Article
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yes
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Abstract
Thermally driven processes of molecular systems include transitions of energy barriers on the microsecond timescales and higher. Sufficient sampling of such processes with molecular dynamics simulations is challenging and often requires accelerating slow transitions using external biasing potentials. Different dynamic reweighting algorithms have been proposed in the past few years to recover the unbiased kinetics from biased systems. However, it remains an open question if and how these dynamic reweighting approaches are connected. In this work, we establish the link between the two main reweighting types, i.e., path-based and energy-based reweighting. We derive a path-based correction factor for the energy-based dynamic histogram analysis method, thus connecting the previously separate reweighting types. We show that the correction factor can be used to combine the advantages of path-based and energy-based reweighting algorithms: it is integrator independent, more robust, and at the same time able to reweight time-dependent biases. We can furthermore demonstrate the relationship between two independently derived path-based reweighting algorithms. Our theoretical findings are verified on a one-dimensional four-well system. By connecting different dynamic reweighting algorithms, this work helps to clarify the strengths and limitations of the different methods and enables a more robust usage of the combined types.
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153 (23)
Pages / Article No.
234106
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American Institute of Physics
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09458 - Riniker, Sereina Z. / Riniker, Sereina Z.
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178762 - Passive Membrane-Permeability Prediction for Peptides and Peptidomimetics Using Computational Methods (SNF)
ETH-34 17-2 - Combining Machine Learning and Molecular Dynamics to Predict Physicochemical Quantities of Molecules (ETHZ)
ETH-34 17-2 - Combining Machine Learning and Molecular Dynamics to Predict Physicochemical Quantities of Molecules (ETHZ)