1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Physical Chemistry
  4. Volume 75, 2024
  5. Article

Annual Review of Physical Chemistry

Volume 75, 2024

Dynamical Reweighting for Biased Rare Event Simulations

Abstract

Dynamical reweighting techniques aim to recover the correct molecular dynamics from a simulation at a modified potential energy surface. They are important for unbiasing enhanced sampling simulations of molecular rare events. Here, we review the theoretical frameworks of dynamical reweighting for modified potentials. Based on an overview of kinetic models with increasing level of detail, we discuss techniques to reweight two-state dynamics, multistate dynamics, and path integrals. We explore the natural link to transition path sampling and how the effect of nonequilibrium forces can be reweighted. We end by providing an outlook on how dynamical reweighting integrates with techniques for optimizing collective variables and with modern potential energy surfaces.

Keyword(s): dynamical reweighting, enhanced sampling, molecular simulation, path reweighting, potential energy surface, rare events
Loading

Article metrics loading...

/content/journals/10.1146/annurev-physchem-083122-124538
2024-06-28
2024-10-08
Loading full text...

Full text loading...

/deliver/fulltext/physchem/75/1/annurev-physchem-083122-124538.html?itemId=/content/journals/10.1146/annurev-physchem-083122-124538&mimeType=html&fmt=ahah

Literature Cited

  1. 1.
    Eyring H. 1935.. The activated complex in chemical reactions. . J. Chem. Phys. 3:(2):107–15
     [Crossref] [Google Scholar]
  2. 2.
    Kramers HA. 1940.. Brownian motion in a field of force and the diffusion model of chemical reactions. . Physica 7:(4):284–304
     [Crossref] [Google Scholar]
  3. 3.
    Bedrov D, Piquemal JP, Borodin O, MacKerell AD Jr., Roux B, Schröder C. 2019.. Molecular dynamics simulations of ionic liquids and electrolytes using polarizable force fields. . Chem. Rev. 119:(13):7940–95
     [Crossref] [Google Scholar]
  4. 4.
    Bannwarth C, Caldeweyher E, Ehlert S, Hansen A, Pracht P, et al. 2021.. Extended tight-binding quantum chemistry methods. . Wiley Interdiscip. Rev. Comput. Mol. Sci. 11:(2):e1493
     [Crossref] [Google Scholar]
  5. 5.
    Kocer E, Ko TW, Behler J. 2022.. Neural network potentials: a concise overview of methods. . Annu. Rev. Phys. Chem. 73::163–86
     [Crossref] [Google Scholar]
  6. 6.
    Noé F, Tkatchenko A, Müller KR, Clementi C. 2020.. Machine learning for molecular simulation. . Annu. Rev. Phys. Chem. 71::361–90
     [Crossref] [Google Scholar]
  7. 7.
    Hénin J, Lelièvre T, Shirts MR, Valsson O, Delemotte L. 2022.. Enhanced sampling methods for molecular dynamics simulations [article v1.0]. . Living J. Comput. Mol. Sci. 4:1583
     [Google Scholar]
  8. 8.
    Peters B. 2016.. Reaction coordinates and mechanistic hypothesis tests. . Annu. Rev. Phys. Chem. 67::669–90
     [Crossref] [Google Scholar]
  9. 9.
    Peters B. 2017.. Reaction Rate Theory and Rare Events. Amsterdam:: Elsevier
     [Google Scholar]
  10. 10.
    Kamenik AS, Linker SM, Riniker S. 2022.. Enhanced sampling without borders: on global biasing functions and how to reweight them. . Phys. Chem. Chem. Phys. 24:(3):1225–36
     [Crossref] [Google Scholar]
  11. 11.
    Chen H, Chipot C. 2022.. Enhancing sampling with free-energy calculations. . Curr. Opin. Struct. Biol. 77::102497
     [Crossref] [Google Scholar]
  12. 12.
    Camilloni C, Pietrucci F. 2018.. Advanced simulation techniques for the thermodynamic and kinetic characterization of biological systems. . Adv. Phys. X 3:(1):1477531
     [Google Scholar]
  13. 13.
    Linker SM, Weiß RG, Riniker S. 2020.. Connecting dynamic reweighting algorithms: derivation of the dynamic reweighting family tree. . J. Chem. Phys. 153::234106
     [Crossref] [Google Scholar]
  14. 14.
    Kieninger S, Donati L, Keller BG. 2020.. Dynamical reweighting methods for Markov models. . Curr. Opin. Struct. Biol. 61::124–31
     [Crossref] [Google Scholar]
  15. 15.
    Huber G, Kim S. 1996.. Weighted-ensemble Brownian dynamics simulations for protein association reactions. . Biophys. J. 70:(1):97–110
     [Crossref] [Google Scholar]
  16. 16.
    Zhang BW, Jasnow D, Zuckerman DM. 2010.. The ``weighted ensemble'' path sampling method is statistically exact for a broad class of stochastic processes and binning procedures. . J. Chem. Phys. 132::054107
     [Crossref] [Google Scholar]
  17. 17.
    Allen RJ, Warren PB, ten Wolde PR. 2005.. Sampling rare switching events in biochemical networks. . Phys. Rev. Lett. 94::018104
     [Crossref] [Google Scholar]
  18. 18.
    Cerou F, Guyader A, Lelievre T, Pommier D. 2011.. A multiple replica approach to simulate reactive trajectories. . J. Chem. Phys. 134::054108
     [Crossref] [Google Scholar]
  19. 19.
    Dellago C, Bolhuis PG, Csajka FS, Chandler D. 1998.. Transition path sampling and the calculation of rate constants. . J. Chem. Phys. 108:(5):1964–77
     [Crossref] [Google Scholar]
  20. 20.
    Bolhuis PG, Chandler D, Dellago C, Geissler PL. 2002.. Transition path sampling: throwing ropes. . Annu. Rev. Phys. Chem. 53::291–318
     [Crossref] [Google Scholar]
  21. 21.
    Dellago C, Bolhuis PG. 2009.. Transition path sampling and other advanced simulation techniques for rare events. . In Advanced Computer Simulation Approaches for Soft Matter Sciences III, ed. C Holm, K Kremer , pp. 167–233. Berlin:: Springer
     [Google Scholar]
  22. 22.
    Cabriolu R, Refsnes KMS, Bolhuis PG, van Erp TS. 2017.. Foundations and latest advances in replica exchange transition interface sampling. . J. Chem. Phys. 147::152722
     [Crossref] [Google Scholar]
  23. 23.
    E W, Vanden-Eijnden E. 2010.. Transition-path theory and path-finding algorithms for the study of rare events. . Annu. Rev. Phys. Chem. 61::391–420
     [Crossref] [Google Scholar]
  24. 24.
    Bowman GR, Pande VS, Noé F, eds. 2013.. An Introduction to Markov State Models and Their Application to Long-Timescale Molecular Simulation. Berlin:: Springer
     [Google Scholar]
  25. 25.
    Zwanzig R. 2001.. Nonequilibrium Statistical Mechanics. Oxford, UK:: Oxford Univ. Press
     [Google Scholar]
  26. 26.
    Prinz JH, Wu H, Sarich M, Keller B, Senne M, et al. 2011.. Markov models of molecular kinetics: generation and validation. . J. Chem. Phys. 134::174105
     [Crossref] [Google Scholar]
  27. 27.
    Swope WC, Pitera JW, Suits F. 2004.. Describing protein folding kinetics by molecular dynamics simulations. 1. Theory. . J. Phys. Chem. B 108:(21):6571–81
     [Crossref] [Google Scholar]
  28. 28.
    Buchete NV, Hummer G. 2008.. Coarse master equations for peptide folding dynamics. . J. Phys. Chem. B 112:(19):6057–69
     [Crossref] [Google Scholar]
  29. 29.
    Bicout D, Szabo A. 1998.. Electron transfer reaction dynamics in non-Debye solvents. . J. Chem. Phys. 109:(6):2325–38
     [Crossref] [Google Scholar]
  30. 30.
    Berneche S, Roux B. 2003.. A microscopic view of ion conduction through the K+ channel. . PNAS 100:(15):8644–48
     [Crossref] [Google Scholar]
  31. 31.
    Krämer A, Ghysels A, Wang E, Venable RM, Klauda JB, et al. 2020.. Membrane permeability of small molecules from unbiased molecular dynamics simulations. . J. Chem. Phys. 153::124107
     [Crossref] [Google Scholar]
  32. 32.
    Lie HC, Fackeldey K, Weber M. 2013.. A square root approximation of transition rates for a Markov state model. . SIAM J. Matrix Anal. Appl. 34:(2):738–56
     [Crossref] [Google Scholar]
  33. 33.
    Donati L, Heida M, Keller BG, Weber M. 2018.. Estimation of the infinitesimal generator by square-root approximation. . J. Condens. Matter Phys. 30::425201
     [Crossref] [Google Scholar]
  34. 34.
    Donati L, Weber M, Keller BG. 2021.. Markov models from the square root approximation of the Fokker–Planck equation: calculating the grid-dependent flux. . J. Condens. Matter Phys. 33::115902
     [Crossref] [Google Scholar]
  35. 35.
    Dixit PD, Jain A, Stock G, Dill KA. 2015.. Inferring transition rates of networks from populations in continuous-time Markov processes. . J. Chem. Theory Comput. 11::5464–72
     [Crossref] [Google Scholar]
  36. 36.
    van Erp TS, Moroni D, Bolhuis PG. 2003.. A novel path sampling method for the calculation of rate constants. . J. Chem. Phys. 118:(17):7762–74
     [Crossref] [Google Scholar]
  37. 37.
    Kumar S, Rosenberg JM, Bouzida D, Swendsen RH, Kollman PA. 1992.. The weighted histogram analysis method for free-energy calculations on biomolecules. I. The method. . J. Comput. Chem. 13:(8):1011–21
     [Crossref] [Google Scholar]
  38. 38.
    Kokh DB, Amaral M, Bomke J, Grädler U, Musil D, et al. 2018.. Estimation of drug-target residence times by τ-random acceleration molecular dynamics simulations. . J. Chem. Theory Comput. 14:(7):3859–69
     [Crossref] [Google Scholar]
  39. 39.
    Kokh DB, Doser B, Richter S, Ormersbach F, Cheng X, Wade RC. 2020.. A workflow for exploring ligand dissociation from a macromolecule: efficient random acceleration molecular dynamics simulation and interaction fingerprint analysis of ligand trajectories. . J. Chem. Phys. 153::125102
     [Crossref] [Google Scholar]
  40. 40.
    Wolf S, Stock G. 2018.. Targeted molecular dynamics calculations of free energy profiles using a nonequilibrium friction correction. . J. Chem. Theory Comput. 14:(12):6175–82
     [Crossref] [Google Scholar]
  41. 41.
    Wolf S, Lickert B, Bray S, Stock G. 2020.. Multisecond ligand dissociation dynamics from atomistic simulations. . Nat. Commun. 11::2918
     [Crossref] [Google Scholar]
  42. 42.
    Huber T, Torda A, van Gunsteren W. 1994.. Local elevation: a method for improving the searching properties of molecular dynamics simulation. . J. Comput.-Aided Mol. Des. 8::695–708
     [Crossref] [Google Scholar]
  43. 43.
    Grubmüller H. 1995.. Predicting slow structural transitions in macromolecular systems: conformational flooding. . Phys. Rev. E 52::2893–906
     [Crossref] [Google Scholar]
  44. 44.
    Voter AF. 1997.. Hyperdynamics: accelerated molecular dynamics of infrequent events. . Phys. Rev. Lett. 78::3908–11
     [Crossref] [Google Scholar]
  45. 45.
    Laio A, Parrinello M. 2002.. Escaping free-energy minima. . PNAS 99:(20):12562–66
     [Crossref] [Google Scholar]
  46. 46.
    Tiwary P, Parrinello M. 2013.. From metadynamics to dynamics. . Phys. Rev. Lett. 111::230602
     [Crossref] [Google Scholar]
  47. 47.
    Salvalaglio M, Tiwary P, Parrinello M. 2014.. Assessing the reliability of the dynamics reconstructed from metadynamics. . J. Chem. Theory Comput. 10:(4):1420–25
     [Crossref] [Google Scholar]
  48. 48.
    Ray D, Ansari N, Rizzi V, Invernizzi M, Parrinello M. 2022.. Rare event kinetics from adaptive bias enhanced sampling. . J. Chem. Theory Comput. 18:(11):6500–9
     [Crossref] [Google Scholar]
  49. 49.
    Palacio-Rodriguez K, Vroylandt H, Stelzl LS, Pietrucci F, Hummer G, Cossio P. 2022.. Transition rates and efficiency of collective variables from time-dependent biased simulations. . J. Phys. Chem. Lett. 13:(32):7490–96
     [Crossref] [Google Scholar]
  50. 50.
    Hummer G, Szabo A. 2003.. Kinetics from nonequilibrium single-molecule pulling experiments. . Biophys. J. 85:(1):5–15
     [Crossref] [Google Scholar]
  51. 51.
    Dickson BM. 2018.. Erroneous rates and false statistical confirmations from infrequent metadynamics and other equivalent violations of the hyperdynamics paradigm. . J. Chem. Theory Comput. 15:(1):78–83
     [Crossref] [Google Scholar]
  52. 52.
    Khan SA, Dickson BM, Peters B. 2020.. How fluxional reactants limit the accuracy/efficiency of infrequent metadynamics. . J. Chem. Phys. 153::054125
     [Crossref] [Google Scholar]
  53. 53.
    Mahinthichaichan P, Liu R, Vo QN, Ellis CR, Stavitskaya L, Shen J. 2023.. Structure–kinetics relationships of opioids from metadynamics and machine learning analysis. . J. Chem. Inf. Model. 63:(7):2196–206
     [Crossref] [Google Scholar]
  54. 54.
    Ludwig T, Singh AR, Nørskov JK. 2020.. Subsurface nitrogen dissociation kinetics in lithium metal from metadynamics. . J. Phys. Chem. C 124:(48):26368–78
     [Crossref] [Google Scholar]
  55. 55.
    Fu CD, He Y, Pfaendtner J. 2019.. Diagnosing the impact of external electric fields chemical kinetics: application to toluene oxidation and pyrolysis. . J. Phys. Chem. A 123:(14):3080–89
     [Crossref] [Google Scholar]
  56. 56.
    Polino D, Parrinello M. 2019.. Kinetics of aqueous media reactions via ab initio enhanced molecular dynamics: the case of urea decomposition. . J. Phys. Chem. B 123:(31):6851–56
     [Crossref] [Google Scholar]
  57. 57.
    Doshi U, Hamelberg D. 2011.. Extracting realistic kinetics of rare activated processes from accelerated molecular dynamics using Kramers' theory. . J. Chem. Theory Comput. 7:(3):575–81
     [Crossref] [Google Scholar]
  58. 58.
    Frank AT, Andricioaei I. 2016.. Reaction coordinate-free approach to recovering kinetics from potential-scaled simulations: application of Kramers' rate theory. . J. Phys. Chem. B 120:(33):8600–5
     [Crossref] [Google Scholar]
  59. 59.
    Miao Y, Bhattarai A, Wang J. 2020.. Ligand Gaussian accelerated molecular dynamics (LiGaMD): characterization of ligand binding thermodynamics and kinetics. . J. Chem. Theory Comput. 16:(9):5526–47
     [Crossref] [Google Scholar]
  60. 60.
    Ferrenberg AM, Swendsen RH. 1989.. Optimized Monte Carlo data analysis. . Comput. Phys. 3:(5):101–4
     [Crossref] [Google Scholar]
  61. 61.
    Shirts MR, Chodera JD. 2008.. Statistically optimal analysis of samples from multiple equilibrium states. . J. Chem. Phys. 129::124105
     [Crossref] [Google Scholar]
  62. 62.
    Hummer G. 2005.. Position-dependent diffusion coefficients and free energies from Bayesian analysis of equilibrium and replica molecular dynamics simulations. . New J. Phys. 7:(1):34
     [Crossref] [Google Scholar]
  63. 63.
    Comer J, Chipot C, González-Nilo FD. 2013.. Calculating position-dependent diffusivity in biased molecular dynamics simulations. . J. Chem. Theory Comput. 9:(2):876–82
     [Crossref] [Google Scholar]
  64. 64.
    Sicard F, Koskin V, Annibale A, Rosta E. 2021.. Position-dependent diffusion from biased simulations and Markov state model analysis. . J. Chem. Theory Comput. 17:(4):2022–33
     [Crossref] [Google Scholar]
  65. 65.
    Donati L, Weber M, Keller BG. 2022.. A review of Girsanov reweighting and of square root approximation for building molecular Markov state models. . J. Math. Phys. 63::123306
     [Crossref] [Google Scholar]
  66. 66.
    Rosta E, Hummer G. 2015.. Free energies from dynamic weighted histogram analysis using unbiased Markov state model. . J. Chem. Theory Comput. 11:(1):276–85
     [Crossref] [Google Scholar]
  67. 67.
    Badaoui M, Kells A, Molteni C, Dickson CJ, Hornak V, Rosta E. 2018.. Calculating kinetic rates and membrane permeability from biased simulations. . J. Phys. Chem. B 122:(49):11571–78
     [Crossref] [Google Scholar]
  68. 68.
    Tiwary P, Berne B. 2016.. Spectral gap optimization of order parameters for sampling complex molecular systems. . PNAS 113:(11):2839–44
     [Crossref] [Google Scholar]
  69. 69.
    Stelzl LS, Kells A, Rosta E, Hummer G. 2017.. Dynamic histogram analysis to determine free energies and rates from biased simulations. . J. Chem. Theory Comput. 13:(12):6328–42
     [Crossref] [Google Scholar]
  70. 70.
    Stelzl LS, Hummer G. 2017.. Kinetics from replica exchange molecular dynamics simulations. . J. Chem. Theory Comput. 13:(8):3927–35
     [Crossref] [Google Scholar]
  71. 71.
    Wu H, Paul F, Wehmeyer C, Noé F. 2016.. Multiensemble Markov models of molecular thermodynamics and kinetics. . PNAS 113:(23):E3221–30
     [Crossref] [Google Scholar]
  72. 72.
    Wu H, Mey ASJS, Rosta E, Noé F. 2014.. Statistically optimal analysis of state-discretized trajectory data from multiple thermodynamic states. . J. Chem. Phys. 141::214106
     [Crossref] [Google Scholar]
  73. 73.
    Mey AS, Wu H, Noé F. 2014.. xTRAM: estimating equilibrium expectations from time-correlated simulation data at multiple thermodynamic states. . Phys. Rev. X 4::041018
     [Google Scholar]
  74. 74.
    Chodera JD, Swope WC, Noé F, Prinz JH, Shirts MR, Pande VS. 2011.. Dynamical reweighting: improved estimates of dynamical properties from simulations at multiple temperatures. . J. Chem. Phys. 134::244107
     [Crossref] [Google Scholar]
  75. 75.
    Prinz JH, Chodera JD, Pande VS, Swope WC, Smith JC, Noé F. 2011.. Optimal use of data in parallel tempering simulations for the construction of discrete-state Markov models of biomolecular dynamics. . J. Chem. Phys. 134::244108
     [Crossref] [Google Scholar]
  76. 76.
    Galama MM, Wu H, Krämer A, Sadeghi M, Noé F. 2023.. Stochastic approximation to MBAR and TRAM: batchwise free energy estimation. . J. Chem. Theory Comput. 19:(3):758–66
     [Crossref] [Google Scholar]
  77. 77.
    Wan H, Zhou G, Voelz VA. 2016.. A maximum-caliber approach to predicting perturbed folding kinetics due to mutations. . J. Chem. Theory Comput. 12:(12):5768–76
     [Crossref] [Google Scholar]
  78. 78.
    Ge Y, Voelz VA. 2022.. Estimation of binding rates and affinities from multiensemble Markov models and ligand decoupling. . J. Chem. Phys. 156::134115
     [Crossref] [Google Scholar]
  79. 79.
    Knoch F, Speck T. 2018.. Unfolding dynamics of small peptides biased by constant mechanical forces. . Mol. Syst. Des. Eng. 3:(1):204–13
     [Crossref] [Google Scholar]
  80. 80.
    Dodd T, Botto M, Paul F, Fernandez-Leiro R, Lamers MH, Ivanov I. 2020.. Polymerization and editing modes of a high-fidelity DNA polymerase are linked by a well-defined path. . Nat. Commun. 11::5379
     [Crossref] [Google Scholar]
  81. 81.
    Hu X, Wang Y, Hunkele A, Provasi D, Pasternak GW, Filizola M. 2019.. Kinetic and thermodynamic insights into sodium ion translocation through the μ-opioid receptor from molecular dynamics and machine learning analysis. . PLOS Comput. Biol. 15:(1):e1006689
     [Crossref] [Google Scholar]
  82. 82.
    He Z, Paul F, Roux B. 2021.. A critical perspective on Markov state model treatments of protein–protein association using coarse-grained simulations. . J. Chem. Phys. 154::084101
     [Crossref] [Google Scholar]
  83. 83.
    Jaynes ET. 1980.. The minimum entropy production principle. . Annu. Rev. Phys. Chem. 31::579–601
     [Crossref] [Google Scholar]
  84. 84.
    Dixit PD, Wagoner J, Weistuch C, Pressé S, Ghosh K, Dill KA. 2018.. Perspective: Maximum caliber is a general variational principle for dynamical systems. . J. Chem. Phys. 148::010901
     [Crossref] [Google Scholar]
  85. 85.
    Pressé S, Ghosh K, Lee J, Dill KA. 2013.. Principles of maximum entropy and maximum caliber in statistical physics. . Rev. Mod. Phys. 85:(3):1115–41
     [Crossref] [Google Scholar]
  86. 86.
    Ghosh K, Dixit PD, Agozzino L, Dill KA. 2020.. The maximum caliber variational principle for nonequilibria. . Annu. Rev. Phys. Chem. 71::213–38
     [Crossref] [Google Scholar]
  87. 87.
    Bause M, Wittenstein T, Kremer K, Bereau T. 2019.. Microscopic reweighting for nonequilibrium steady-state dynamics. . Phys. Rev. E 100::060103
     [Crossref] [Google Scholar]
  88. 88.
    Bause M, Bereau T. 2021.. Reweighting non-equilibrium steady-state dynamics along collective variables. . J. Chem. Phys. 154::134105
     [Crossref] [Google Scholar]
  89. 89.
    Bolhuis PG, Brotzakis ZF, Keller BG. 2023.. Optimizing molecular potential models by imposing kinetic constraints with path reweighting. . J. Chem. Phys. 159::074102
     [Crossref] [Google Scholar]
  90. 90.
    Onsager L, Machlup S. 1953.. Fluctuations and irreversible processes. . Phys. Rev. 91::1505–12
     [Crossref] [Google Scholar]
  91. 91.
    Girsanov IV. 1960.. On transforming a certain class of stochastic processes by absolutely continuous substitution of measures. . Theory Probab. Appl. 5::285–301
     [Crossref] [Google Scholar]
  92. 92.
    Øksendal B. 2003.. Stochastic Differential Equations: An Introduction with Applications. Berlin:: Springer. , 6th ed..
     [Google Scholar]
  93. 93.
    Adib AB. 2008.. Stochastic actions for diffusive dynamics: reweighting, sampling, and minimization. . J. Phys. Chem. B 112:(19):5910–16
     [Crossref] [Google Scholar]
  94. 94.
    Kieninger S, Keller BG. 2021.. Path probability ratios for Langevin dynamics—exact and approximate. . J. Chem. Phys. 154::094102
     [Crossref] [Google Scholar]
  95. 95.
    Zuckerman DM, Woolf TB. 1999.. Dynamic reaction paths and rates through importance-sampled stochastic dynamics. . J. Chem. Phys. 111:(21):9475–84
     [Crossref] [Google Scholar]
  96. 96.
    Zuckerman DM, Woolf TB. 2000.. Efficient dynamic importance sampling of rare events in one dimension. . Phys. Rev. E 63::016702
     [Crossref] [Google Scholar]
  97. 97.
    Athenes M. 2004.. A path-sampling scheme for computing thermodynamic properties of a many-body system in a generalized ensemble. . Eur. Phys. J. B 38::651–63
     [Crossref] [Google Scholar]
  98. 98.
    Xing C, Andricioaei I. 2006.. On the calculation of time correlation functions by potential scaling. . J. Chem. Phys. 124::034110
     [Crossref] [Google Scholar]
  99. 99.
    Schütte C, Nielsen A, Weber M. 2015.. Markov state models and molecular alchemy. . Mol. Phys. 113:(1):69–78
     [Crossref] [Google Scholar]
  100. 100.
    Hünenberger PH. 2005.. Thermostat algorithms for molecular dynamics simulations. . In Advanced Computer Simulation Approaches for Soft Matter Sciences I, ed. C Holm, K Kremer , pp. 105–49. Berlin:: Springer
     [Google Scholar]
  101. 101.
    Bussi G, Parrinello M. 2007.. Accurate sampling using Langevin dynamics. . Phys. Rev. E 75::056707
     [Crossref] [Google Scholar]
  102. 102.
    Leimkuhler B, Matthews C. 2013.. Robust and efficient configurational molecular sampling via Langevin dynamics. . J. Chem. Phys. 138::174102
     [Crossref] [Google Scholar]
  103. 103.
    Sivak DA, Chodera JD, Crooks GE. 2013.. Using nonequilibrium fluctuation theorems to understand and correct errors in equilibrium and nonequilibrium simulations of discrete Langevin dynamics. . Phys. Rev. X 3::011007
     [Google Scholar]
  104. 104.
    Kieninger S, Keller BG. 2022.. GROMACS stochastic dynamics and BAOAB are equivalent configurational sampling algorithms. . J. Chem. Theory Comput. 18:(10):5792–98
     [Crossref] [Google Scholar]
  105. 105.
    Kieninger S, Ghysbrecht S, Keller BG. 2023.. Girsanov reweighting for simulations of underdamped Langevin dynamics. Theory. . arXiv:2303.14696 [cond-mat.stat-mech]
  106. 106.
    Donati L, Hartmann C, Keller BG. 2017.. Girsanov reweighting for path ensembles and Markov state models. . J. Chem. Phys. 146::244112
     [Crossref] [Google Scholar]
  107. 107.
    Donati L, Keller BG. 2018.. Girsanov reweighting for metadynamics simulations. . J. Chem. Phys. 149::072335
     [Crossref] [Google Scholar]
  108. 108.
    Shmilovich K, Ferguson AL. 2023.. Girsanov reweighting enhanced sampling technique (GREST): on-the-fly data-driven discovery of and enhanced sampling in slow collective variables. . Phys. Chem. A 127:(15):3497–517
     [Crossref] [Google Scholar]
  109. 109.
    Brotzakis ZF, Vendruscolo M, Bolhuis PG. 2020.. A method of incorporating rate constants as kinetic constraints in molecular dynamics simulations. . PNAS 119:(2):e2012423118
     [Crossref] [Google Scholar]
  110. 110.
    Bolhuis PG, Brotzakis ZF, Vendruscolo M. 2021.. A maximum caliber approach for continuum path ensembles. . Eur. Phys. J. B 94:(9):188
     [Crossref] [Google Scholar]
  111. 111.
    Peter EK, Manstein DJ, Shea JE, Schug A. 2021.. Core-MD II: a fast, adaptive, and accurate enhanced sampling method. . J. Chem. Phys. 155::104114
     [Crossref] [Google Scholar]
  112. 112.
    Mandelli D, Hirshberg B, Parrinello M. 2020.. Metadynamics of paths. . Phys. Rev. Lett. 125::026001
     [Crossref] [Google Scholar]
  113. 113.
    Ketkaew R, Luber S. 2022.. DeepCV: a deep learning framework for blind search of collective variables in expanded configurational space. . J. Chem. Inf. Model. 62:(24):6352–64
     [Crossref] [Google Scholar]
  114. 114.
    Sidky H, Chen W, Ferguson AL. 2020.. Machine learning for collective variable discovery and enhanced sampling in biomolecular simulation. . Mol. Phys. 118:(5):e1737742
     [Crossref] [Google Scholar]
  115. 115.
    Chen M. 2021.. Collective variable–based enhanced sampling and machine learning. . Eur. Phys. J. B 94:(10):211
     [Crossref] [Google Scholar]
  116. 116.
    Hooft F, de Alba Ortíz AP, Ensing B. 2021.. Discovering collective variables of molecular transitions via genetic algorithms and neural networks. . J. Chem. Theory Comput. 17:(4):2294–306
     [Crossref] [Google Scholar]
  117. 117.
    Ma A, Dinner AR. 2005.. Automatic method for identifying reaction coordinates in complex systems. . J. Phys. Chem. B 109:(14):6769–79
     [Crossref] [Google Scholar]
  118. 118.
    Frassek M, Arjun A, Bolhuis PG. 2021.. An extended autoencoder model for reaction coordinate discovery in rare event molecular dynamics datasets. . J. Chem. Phys. 155::064103
     [Crossref] [Google Scholar]
  119. 119.
    Jung H, Covino R, Arjun A, Leitold C, Dellago C, et al. 2023.. Machine-guided path sampling to discover mechanisms of molecular self-organization. . Nat. Comput. Sci. 3:(4):334–45
     [Crossref] [Google Scholar]
  120. 120.
    Das A, Kuznets-Speck B, Limmer DT. 2022.. Direct evaluation of rare events in active matter from variational path sampling. . Phys. Rev. Lett. 128::028005
     [Crossref] [Google Scholar]
  121. 121.
    Lamim Ribeiro JM, Provasi D, Filizola M. 2020.. A combination of machine learning and infrequent metadynamics to efficiently predict kinetic rates, transition states, and molecular determinants of drug dissociation from G protein-coupled receptors. . J. Chem. Phys. 153::124105
     [Crossref] [Google Scholar]
  122. 122.
    Paul TK, Taraphder S. 2022.. Nonlinear reaction coordinate of an enzyme catalyzed proton transfer reaction. . J. Phys. Chem. B 126:(7):1413–25
     [Crossref] [Google Scholar]
  123. 123.
    Zeng Z, Wodaczek F, Liu K, Stein F, Hutter J, et al. 2023.. Mechanistic insight on water dissociation on pristine low-index TiO2 surfaces from machine learning molecular dynamics simulations. . Nat. Commun. 14::6131
     [Crossref] [Google Scholar]
/content/journals/10.1146/annurev-physchem-083122-124538
Loading
Dynamical Reweighting for Biased Rare Event Simulations
Annual Review of Physical Chemistry 75, 137 (2024); https://doi.org/10.1146/annurev-physchem-083122-124538
/content/journals/10.1146/annurev-physchem-083122-124538
/content/journals/10.1146/annurev-physchem-083122-124538
Loading

Data & Media loading...

Supplemental Materials

  • Supplemental Materials

file format pdf download PDF
  • Article Type: Review Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error