Nonphysical sampling distributions in Monte Carlo free-energy estimation: Umbrella sampling

By G. Torrie, J. Valleau. 1977

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Citations in the corpus

Equivariant Flows: exact likelihood generative learning for symmetric densities (2020, methodology): “…to speed up rare events by adding a suitable bias energy to u(x) and subsequently correcting the generated distribution by reweighing or Monte-Carlo estimators using the ratio of true over generated density, e.g. (Torrie & Valleau, 1977; Bennett, 1976; Laio & Parrinello, 2002; Wu et al., 2016).”
Simulating Normalizing Constants: From Importance Sampling to Bridge Sampling to Path Sampling (1998, unknown): “See Meng and Wong (1996) for a discussion of the relationship between bridge sampling and umbrella sampling, another method developed in computational physics (Torrie and Valleau, 1977), which has been termed ratio importance sampling by Chen and Shao (1997a, 1997b) in the statistical literature.”
Alchemical Binding Free Energy Calculations in AMBER20: Advances and Best Practices for Drug Discovery (2020, unknown): “7,75 ÅÅÅÅÅÅÅÅÅÅ where f ( x ) = 1/[1 + exp( x )] is the Fermi function and C is a constant with units of energy.”
Advances in characterizing and understanding the microstructure of cementitious materials (2019, methodology): “While umbrella sampling technique [283] measures free energy difference by applying a bias potential, usually a harmonic potential, between two adjacent states, the weighted histogram analysis method [284] streamlines free energy calculations along a series of consecutive steps.”
Standard binding free energies from computer simulations: What is the best strategy? (2012, methodology): “All simulations are done using NAMD 2.9, which supports a number of advanced free-energy techniques, including FEP, 24,45 REMD-US, 46–49 and ABF algorithms.”