Self-guided Langevin dynamics simulation method

By Xiongwu Wu, Bernard R. Brooks. 2003

449 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/8f970bfcbf141fe1a2e6183ffc622edb7e7bea1e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atomic and Molecular Physics, and Optics

1. Romelia Salomón-Ferrer
2. Tony Lelièvre
3. Ross C. Walker
4. David A. Case
5. J'erome H'enin
6. Lucie Delemotte
7. Omar Valsson
8. Michael R. Shirts

In Molecular Biology

1. Romelia Salomón-Ferrer
2. Ross C. Walker
3. David A. Case
4. J'erome H'enin
5. Tony Lelièvre
6. Lucie Delemotte
7. Omar Valsson
8. Michael R. Shirts

In Spectroscopy

1. Romelia Salomón-Ferrer
2. David A. Case
3. Tony Lelièvre
4. Michael R. Shirts
5. J'erome H'enin
6. Ross C. Walker
7. Lucie Delemotte
8. Omar Valsson

Citations in the corpus

• Enhanced Sampling Methods for Molecular Dynamics Simulations [Article v1.0] (2022, methodology): “However, SGLD with a generalized Langevin equation (where stochastic forces are not white noise but obey a given memory kernel) [351] recovers the detailed balance property of standard Langevin dynamics, and as a result, is able to sample the NVT and NPT ensembles in an unbiased way.”
• An overview of the Amber biomolecular simulation package (2013, methodology): “Self-guided Langevin dynamics (SGLD) 26–28 is a method that enhances conformational sampling by accelerating low-frequency modes through the use of an ad hoc time-averaged momentum term.”