The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
Adaptive biasing force method for scalar and vector free energy calculations
Abstract
In free energy calculations based on thermodynamic integration, it is necessary to compute the derivatives of the free energy as a function of one (scalar case) or several (vector case) order parameters. We derive in a compact way a general formulation for evaluating these derivatives as the average of a mean force acting on the order parameters, which involves first derivatives with respect to both Cartesian coordinates and time. This is in contrast with the previously derived formulas, which require first and second derivatives of the order parameter with respect to Cartesian coordinates. As illustrated in a concrete example, the main advantage of this new formulation is the simplicity of its use, especially for complicated order parameters. It is also straightforward to implement in a molecular dynamics code, as can be seen from the pseudocode given at the end. We further discuss how the approach based on time derivatives can be combined with the adaptive biasing force method, an enhanced sampling technique that rapidly yields uniform sampling of the order parameters, and by doing so greatly improves the efficiency of free energy calculations. Using the backbone dihedral angles Φ and Ψ in N-acetylalanyl-N′-methylamide as a numerical example, we present a technique to reconstruct the free energy from its derivatives, a calculation that presents some difficulties in the vector case because of the statistical errors affecting the derivatives.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Comparison of simple potential functions for simulating liquid water
Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W., & Klein, M. L. (1983) · The Journal of Chemical Physics · doi:10.1063/1.445869 · Cited by 41,298
Canonical dynamics: Equilibrium phase-space distributions
Hoover, W. G. (1985) · Physical Review A · doi:10.1103/physreva.31.1695 · Cited by 23,465
Scalable molecular dynamics with NAMD
Phillips, J. C., Braun, R., Wang, W., Gumbart, J., Tajkhorshid, E., Villa, E., Chipot, C., Skeel, R. D., Kalé, L., & Schulten, K. (2005) · Journal of Computational Chemistry · doi:10.1002/jcc.20289 · Cited by 16,914
A Second Generation Force Field for the Simulation of Proteins, Nucleic Acids, and Organic Molecules
Cornell, W. D., Cieplak, P., Bayly, C. I., Gould, I. R., Merz, K. M., Ferguson, D. M., Spellmeyer, D. C., Fox, T., Caldwell, J. W., & Kollman, P. A. (1995) · Journal of the American Chemical Society · doi:10.1021/ja00124a002 · Cited by 13,103
A molecular dynamics method for simulations in the canonical ensemble
Nosé, S. (1984) · Molecular Physics · doi:10.1080/00268978400101201 · Cited by 10,076
Goldstein, H. (1951) · UCLA - Biblioteca de Ciencias y Tecnologia Felix Morales Bueno · Cited by 10,004
THE weighted histogram analysis method for free‐energy calculations on biomolecules. I. The method
Kumar, S., Rosenberg, J. M., Bouzida, D., Swendsen, R. H., & Kollman, P. A. (1992) · Journal of Computational Chemistry · doi:10.1002/jcc.540130812 · Cited by 6,808
Nosé–Hoover chains: The canonical ensemble via continuous dynamics
Martyna, G. J., Klein, M. L., & Tuckerman, M. (1992) · The Journal of Chemical Physics · doi:10.1063/1.463940 · Cited by 5,924
Laio, A., & Parrinello, M. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.202427399 · Cited by 5,626
Understanding Molecular Simulation
Frenkel, D., Smit, B., Tobochnik, J., McKay, S. R., & Christian, W. (1997) · Computers in Physics · doi:10.1063/1.4822570 · Cited by 5,395
Statistical Mechanics of Fluid Mixtures
Kirkwood, J. G. (1935) · The Journal of Chemical Physics · doi:10.1063/1.1749657 · Cited by 3,454
Rattle: A “velocity” version of the shake algorithm for molecular dynamics calculations
Andersen, H. C. (1983) · Journal of Computational Physics · doi:10.1016/0021-9991(83)90014-1 · Cited by 3,081
Efficient, Multiple-Range Random Walk Algorithm to Calculate the Density of States
Wang, F., & Landau, D. P. (2001) · Physical Review Letters · doi:10.1103/physrevlett.86.2050 · Cited by 2,896
Optimized intermolecular potential functions for liquid hydrocarbons
Jorgensen, W. L., Madura, J. D., & Swenson, C. J. (1984) · Journal of the American Chemical Society · doi:10.1021/ja00334a030 · Cited by 2,356
Multicanonical ensemble: A new approach to simulate first-order phase transitions
Berg, B. A., & Neuhaus, T. (1992) · Physical Review Letters · doi:10.1103/physrevlett.68.9 · Cited by 1,581
Error estimates on averages of correlated data
Flyvbjerg, H., & Petersen, H. G. (1989) · The Journal of Chemical Physics · doi:10.1063/1.457480 · Cited by 1,517
Wang, F., & Landau, D. P. (2001) · Physical Review E · doi:10.1103/physreve.64.056101 · Cited by 1,224
Calculating free energies using average force
Darve, E., & Pohorille, A. (2001) · The Journal of Chemical Physics · doi:10.1063/1.1410978 · Cited by 1,097
Self-citationConstrained reaction coordinate dynamics for the simulation of rare events
Carter, E. A., Ciccotti, G., Hynes, J. T., & Kapral, R. (1989) · Chemical Physics Letters · doi:10.1016/s0009-2614(89)87314-2 · Cited by 1,020
Predicting Crystal Structures: The Parrinello-Rahman Method Revisited
Martoňák, R., Laio, A., & Parrinello, M. (2003) · Physical Review Letters · doi:10.1103/physrevlett.90.075503 · Cited by 894
Multidimensional free‐energy calculations using the weighted histogram analysis method
Kumar, S., Rosenberg, J. M., Bouzida, D., Swendsen, R. H., & Kollman, P. A. (1995) · Journal of Computational Chemistry · doi:10.1002/jcc.540161104 · Cited by 866
Goldstein, H., Poole, C. P., Safko, J. L., & Addison, S. R. (2002) · American Journal of Physics · doi:10.1119/1.1484149 · Cited by 823
Efficient Exploration of Reactive Potential Energy Surfaces Using Car-Parrinello Molecular Dynamics
Iannuzzi, M., Laio, A., & Parrinello, M. (2003) · Physical Review Letters · doi:10.1103/physrevlett.90.238302 · Cited by 821
Overcoming free energy barriers using unconstrained molecular dynamics simulations
Hénin, J., & Chipot, C. (2004) · The Journal of Chemical Physics · doi:10.1063/1.1773132 · Cited by 464
Equilibrium Free Energies from Nonequilibrium Metadynamics
Bussi, G., Laio, A., & Parrinello, M. (2006) · Physical Review Letters · doi:10.1103/physrevlett.96.090601 · Cited by 433
Bartels, C., & Karplus, M. (1997) · Journal of Computational Chemistry · doi:10.1002/(sici)1096-987x(199709)18:12<1450::aid-jcc3>3.0.co;2-i · Cited by 325
Free energy calculations : theory and applications in chemistry and biology
Chipot, C. J., & Pohorille, A. (2007) · Cited by 295
Self-citationReaction path study of conformational transitions in flexible systems: Applications to peptides
Czermiński, R., & Elber, R. (1990) · The Journal of Chemical Physics · doi:10.1063/1.458491 · Cited by 254
Hummer, G., & Kevrekidis, I. G. (2003) · The Journal of Chemical Physics · doi:10.1063/1.1574777 · Cited by 235
Generalization of the Wang-Landau method for off-lattice simulations
Shell, M. S., Debenedetti, P. G., & Panagiotopoulos, A. Z. (2002) · Physical Review E · doi:10.1103/physreve.66.056703 · Cited by 227
Apostolakis, J., Ferrara, P., & Caflisch, A. (1999) · The Journal of Chemical Physics · doi:10.1063/1.477819 · Cited by 216
Assessing the efficiency of free energy calculation methods
Rodriguez-Gomez, D., Darve, E., & Pohorille, A. (2004) · The Journal of Chemical Physics · doi:10.1063/1.1642607 · Cited by 211
Self-citationSimulations of peptide conformational dynamics and thermodynamics
Brooks, C., & Case, D. A. (1993) · Chemical Reviews · doi:10.1021/cr00023a008 · Cited by 209
Tobias, D. J., & Brooks, C. L. (1992) · The Journal of Physical Chemistry · doi:10.1021/j100188a054 · Cited by 205
Transition Matrix Monte Carlo Method
Wang, J., & Swendsen, R. H. (2002) · Journal of Statistical Physics · doi:10.1023/a:1013180330892 · Cited by 191
Blue Moon Sampling, Vectorial Reaction Coordinates, and Unbiased Constrained Dynamics
Ciccotti, G., Kapral, R., & Vanden‐Eijnden, E. (2005) · ChemPhysChem · doi:10.1002/cphc.200400669 · Cited by 188
The calculation of free-energy differences by constrained molecular-dynamics simulations
den Otter, W. K., & Briels, W. J. (1998) · The Journal of Chemical Physics · doi:10.1063/1.477019 · Cited by 183
Reconstructing the Density of States by History-Dependent Metadynamics
Micheletti, C., Laio, A., & Parrinello, M. (2004) · Physical Review Letters · doi:10.1103/physrevlett.92.170601 · Cited by 162
Density-of-states Monte Carlo method for simulation of fluids
Yan, Q., Faller, R., & de Pablo, J. J. (2002) · The Journal of Chemical Physics · doi:10.1063/1.1463055 · Cited by 156
Pettitt, B. M., & Karplus, M. (1985) · Chemical Physics Letters · doi:10.1016/0009-2614(85)85509-3 · Cited by 152
Calculating Free Energies Using a Scaled-Force Molecular Dynamics Algorithm
Darve, E. F., Wilson, M. A., & Pohorille, A. (2002) · Molecular Simulation · doi:10.1080/08927020211975 · Cited by 150
Self-citationHénin, J., Pohorille, A., & Chipot, C. J. (2005) · Journal of the American Chemical Society · doi:10.1021/ja050581y · Cited by 147
Self-citationExploring the free-energy landscape of a short peptide using an average force
Chipot, C. J., & Hénin, J. (2005) · The Journal of Chemical Physics · doi:10.1063/1.2138694 · Cited by 132
The alanine dipeptide free energy surface in solution
Smith, P. E. (1999) · The Journal of Chemical Physics · doi:10.1063/1.479860 · Cited by 130
Computation of free energy profiles with parallel adaptive dynamics
Tony Lelièvre, A., Rousset, M., & Stoltz, G. (2007) · The Journal of Chemical Physics · doi:10.1063/1.2711185 · Cited by 118
Thermodynamic integration of the free energy along a reaction coordinate in Cartesian coordinates
den Otter, W. K. (2000) · The Journal of Chemical Physics · doi:10.1063/1.481329 · Cited by 108
Molecular Dynamics Study of the Inclusion of Cholesterol into Cyclodextrins
Yu, Y., Chipot, C. J., Cai, W., & Shao, X. (2006) · The Journal of Physical Chemistry B · doi:10.1021/jp056751a · Cited by 97
Computation of free energy profiles with parallel adaptive dynamicsPreprint
Tony Lelièvre, A., Rousset, M., & Stoltz, G. (2007) · HAL · Cited by 85
Efficient schemes to compute diffusive barrier crossing rates
Ruiz-Montero, M. J., Frenkel, D., & Brey, J. J. (1997) · Molecular Physics · doi:10.1080/00268979709482677 · Cited by 82
Stirling, A., Iannuzzi, M., Laio, A., & Parrinello, M. (2004) · ChemPhysChem · doi:10.1002/cphc.200400063 · Cited by 80
Determining the density of states for classical statistical models by a flat-histogram random walk
Landau, D. P., & Wang, F. (2002) · Computer Physics Communications · doi:10.1016/s0010-4655(02)00374-0 · Cited by 56
Secondary and Tertiary Structure Elasticity of Titin Z1Z2 and a Titin Chain Model
Lee, E. H., Hsin, J., Mayans, O. M., & Schulten, K. J. (2007) · Biophysical Journal · doi:10.1529/biophysj.107.105528 · Cited by 54
Chipot, C. J., & Pohorille, A. (1998) · The Journal of Physical Chemistry B · doi:10.1021/jp970938n · Cited by 43
Self-citation
Cited by
Using collective variables to drive molecular dynamics simulations
Fiorin, G., Klein, M. L., & Hénin, J. (2013) · Molecular Physics · doi:10.1080/00268976.2013.813594 · Cited by 1,053
Improved Reweighting of Accelerated Molecular Dynamics Simulations for Free Energy Calculation
Miao, Y., Sinko, W., Pierce, L., Bucher, D., Walker, R. C., & McCammon, J. A. (2014) · Journal of Chemical Theory and Computation · doi:10.1021/ct500090q · Cited by 455
Spectral gap optimization of order parameters for sampling complex molecular systems
Tiwary, P., & Berne, B. J. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1600917113 · Cited by 259
CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed
Hwang, W., Austin, S. L., Blondel, A., Boittier, E. D., Boresch, S., Buck, M., Buckner, J., Caflisch, A., Chang, H.-T., Cheng, X., et al. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.4c04100 · Cited by 136
Free Energy Surface Reconstruction from Umbrella Samples Using Gaussian Process Regression
Stecher, T., Bernstein, N., & Csányi, G. (2014) · Journal of Chemical Theory and Computation · doi:10.1021/ct500438v · Cited by 56
Metadata
- Authors
- Darve, E., Rodríguez-Gómez, D., & Pohorille, A.
- Venue
- The Journal of Chemical Physics
- Publisher
- AIP Publishing
- Published
- 2008-04-11
- Volume
- 128
- Issue
- 14
- Pages
- 144120-144120
- ISSN
- 0021-9606 (ISSN-L)
- 1089-7690
- OpenAlex
- W2042821667
- Cited by
- 871
- References
- 53
- FWCI
- 5.09
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.