Reuse & Permissions

It is not necessary to obtain permission to reuse this article or its components as it is available under the terms of the Creative Commons Attribution 4.0 International license. This license permits unrestricted use, distribution, and reproduction in any medium, provided attribution to the author(s) and the published article's title, journal citation, and DOI are maintained. Please note that some figures may have been included with permission from other third parties. It is your responsibility to obtain the proper permission from the rights holder directly for these figures.

Export citation

Export citation

Choose format for download:

Download Citation
  • Open Access

Reactive path ensembles within nonequilibrium steady states

Aditya N. Singh1,2 and David T. Limmer1,2,3,4,*

  • *Contact author: dlimmer@berkeley.edu

Phys. Rev. Research 7, 043222 – Published 26 November, 2025

DOI: https://doi.org/10.1103/rngb-46dt

Abstract

The modern theory of rare events is grounded in near-equilibrium ideas, however, many systems of modern interest are sufficiently far from equilibrium that traditional approaches do not apply. Using the recently developed variational path sampling methodology, we study systems evolving within nonequilibrium steady states to elucidate how reactive processes are altered away from equilibrium. Variational path sampling provides access to ensembles of reactive events and a means of quantifying the relative importance of each dynamical degree of freedom in such processes. With it, we have studied the conformational change of a solute in an active bath. We illustrate how energy injection generically enhances the rates of rare events, even when energy is not directed into specific reactive modes. By studying the folding and unfolding transitions of a grafted polymer under shear, we illustrate how nonequilibrium reactive processes do not follow gradient paths due to the emergence of persistent currents. The breaking of detailed balance allows for the mechanisms of forward and backward reactions to be distinct, enabling unconventional pathways to be explored and designed, and states unstable in equilibrium to become stabilized kinetically away from it. The analysis presented in this work establishes some basic principles for understanding the mechanism through which rates are perturbed by nonconservative forces, and how probability currents can stabilize thermodynamically unfavorable states, and is made possible by the use of a numerical method that does not invoke proximity to equilibrium.

View figure in article

Physics Subject Headings (PhySH)

Article Text

References (105)

  1. T. Vicsek, A. Czirók, E. Ben-Jacob, I. Cohen, and O. Shochet, Novel type of phase transition in a system of self-driven particles, Phys. Rev. Lett. 75, 1226 (1995).
  2. A. K. Omar, K. Klymko, T. GrandPre, and P. L. Geissler, Phase diagram of active Brownian spheres: Crystallization and the metastability of motility-induced phase separation, Phys. Rev. Lett. 126, 188002 (2021).
  3. M. E. Cates and J. Tailleur, Motility-induced phase separation, Annu. Rev. Condens. Matter Phys. 6, 219 (2015).
  4. J. Palacci, S. Sacanna, A. P. Steinberg, D. J. Pine, and P. M. Chaikin, Living crystals of light-activated colloidal surfers, Science 339, 936 (2013).
  5. T. H. Tan, A. Mietke, J. Li, Y. Chen, H. Higinbotham, P. J. Foster, S. Gokhale, J. Dunkel, and N. Fakhri, Odd dynamics of living chiral crystals, Nature (London) 607, 287 (2022).
  6. M. C. Marchetti, J.-F. Joanny, S. Ramaswamy, T. B. Liverpool, J. Prost, M. Rao, and R. A. Simha, Hydrodynamics of soft active matter, Rev. Mod. Phys. 85, 1143 (2013).
  7. K. Kinosita, R. Yasuda, H. Noji, and K. Adachi, A rotary molecular motor that can work at near 100% efficiency, Philos. Trans. R. Soc. Lond. Ser. B 355, 473 (2000).
  8. A. Lau, D. Lacoste, and K. Mallick, Nonequilibrium fluctuations and mechanochemical couplings of a molecular motor, Phys. Rev. Lett. 99, 158102 (2007).
  9. R. Zandi, D. Reguera, J. Rudnick, and W. M. Gelbart, What drives the translocation of stiff chains? Proc. Natl. Acad. Sci. USA 100, 8649 (2003).
  10. A. K. Sharma and E. P. O'Brien, Non-equilibrium coupling of protein structure and function to translation–elongation kinetics, Curr. Opin. Struct. Biol. 49, 94 (2018).
  11. A. Alexander-Katz, M. Schneider, S. Schneider, A. Wixforth, and R. Netz, Shear-flow-induced unfolding of polymeric globules, Phys. Rev. Lett. 97, 138101 (2006).
  12. S. Schneider, S. Nuschele, A. Wixforth, C. Gorzelanny, A. Alexander-Katz, R. Netz, and M. F. Schneider, Shear-induced unfolding triggers adhesion of von Willebrand factor fibers, Proc. Natl. Acad. Sci. USA 104, 7899 (2007).
  13. S. A. Mallory, C. Valeriani, and A. Cacciuto, An active approach to colloidal self-assembly, Annu. Rev. Phys. Chem. 69, 59 (2018).
  14. Y. Cho and W. M. Jacobs, Tuning nucleation kinetics via nonequilibrium chemical reactions, Phys. Rev. Lett. 130, 128203 (2023).
  15. A. Walther and A. H. Müller, Janus particles, Soft Matter 4, 663 (2008).
  16. B. Kuznets-Speck and D. T. Limmer, Dissipation bounds the amplification of transition rates far from equilibrium, Proc. Natl. Acad. Sci. USA 118, e2020863118 (2021).
  17. A. Das and D. T. Limmer, Nonequilibrium design strategies for functional colloidal assemblies, Proc. Natl. Acad. Sci. USA 120, e2217242120 (2023).
  18. S. Osat, J. Metson, M. Kardar, and R. Golestanian, Escaping kinetic traps using nonreciprocal interactions, Phys. Rev. Lett. 133, 028301 (2024).
  19. C. H. Batton and G. M. Rotskoff, Microscopic origin of tunable assembly forces in chiral active environments, Soft Matter 20, 4111 (2024).
  20. G. S. Redner, C. G. Wagner, A. Baskaran, and M. F. Hagan, Classical nucleation theory description of active colloid assembly, Phys. Rev. Lett. 117, 148002 (2016).
  21. A. K. Omar, H. Row, S. A. Mallory, and J. F. Brady, Mechanical theory of nonequilibrium coexistence and motility-induced phase separation, Proc. Natl. Acad. Sci. USA 120, e2219900120 (2023).
  22. A. N. Singh and D. T. Limmer, Variational deep learning of equilibrium transition path ensembles, J. Chem. Phys. 159, 024124 (2023).
  23. A. N. Singh and D. T. Limmer, Splitting probabilities as optimal controllers of rare reactive events, J. Chem. Phys. 161, 054113 (2024).
  24. D. Chandler, Statistical mechanics of isomerization dynamics in liquids and the transition state approximation, J. Chem. Phys. 68, 2959 (1978).
  25. P. Hänggi, P. Talkner, and M. Borkovec, Reaction-rate theory: Fifty years after Kramers, Rev. Mod. Phys. 62, 251 (1990).
  26. H. A. Kramers, Brownian motion in a field of force and the diffusion model of chemical reactions, Physica 7, 284 (1940).
  27. H. Eyring, The activated complex in chemical reactions, J. Chem. Phys. 3, 107 (1935).
  28. T. S. Van Erp, D. Moroni, and P. G. Bolhuis, A novel path sampling method for the calculation of rate constants, J. Chem. Phys. 118, 7762 (2003).
  29. P. G. Bolhuis, D. Chandler, C. Dellago, and P. L. Geissler, Transition path sampling: Throwing ropes over rough mountain passes, in the dark, Annu. Rev. Phys. Chem. 53, 291 (2002).
  30. A. Barducci, M. Bonomi, and M. Parrinello, Metadynamics, Wiley Interdiscip. Rev. Comput. Mol. Sci. 1, 826 (2011).
  31. G. M. Torrie and J. P. Valleau, Nonphysical sampling distributions in Monte Carlo free-energy estimation: Umbrella sampling, J. Comput. Phys. 23, 187 (1977).
  32. Weinan E and E. Vanden-Eijnden, Transition-path theory and path-finding algorithms for the study of rare events, Annu. Rev. Phys. Chem. 61, 391 (2010).
  33. P. Metzner, C. Schütte, and E. Vanden-Eijnden, Transition path theory for Markov jump processes, Multiscale Model. Simul. 7, 1192 (2009).
  34. E. H. Thiede, D. Giannakis, A. R. Dinner, and J. Weare, Galerkin approximation of dynamical quantities using trajectory data, J. Chem. Phys. 150, 244111 (2019).
  35. R. R. Coifman, I. G. Kevrekidis, S. Lafon, M. Maggioni, and B. Nadler, Diffusion maps, reduction coordinates, and low dimensional representation of stochastic systems, Multiscale Model. Simul. 7, 842 (2008).
  36. J. Strahan, A. Antoszewski, C. Lorpaiboon, B. P. Vani, J. Weare, and A. R. Dinner, Long-time-scale predictions from short-trajectory data: A benchmark analysis of the trp-cage miniprotein, J. Chem. Theory Comput. 17, 2948 (2021).
  37. J.-H. Prinz, H. Wu, M. Sarich, B. Keller, M. Senne, M. Held, J. D. Chodera, C. Schütte, and F. Noé, Markov models of molecular kinetics: Generation and validation, J. Chem. Phys. 134, 174105 (2011).
  38. M. I. Freidlin, A. D. Wentzell, M. Freidlin, and A. Wentzell, Random Perturbations (Springer, New York, 1998).
  39. R. Zakine and E. Vanden-Eijnden, Minimum action method for nonequilibrium phase transitions, Phys. Rev. X 13, 041044 (2023).
  40. E. R. Heller and D. T. Limmer, Evaluation of transition rates from nonequilibrium instantons, Phys. Rev. Res. 6, 043110 (2024).
  41. A. Warmflash, P. Bhimalapuram, and A. R. Dinner, Umbrella sampling for nonequilibrium processes, J. Chem. Phys. 127, 154112 (2007).
  42. R. J. Allen, C. Valeriani, and P. R. Ten Wolde, Forward flux sampling for rare event simulations, J. Phys.: Condens. Matter 21, 463102 (2009).
  43. A. Das, B. Kuznets-Speck, and D. T. Limmer, Direct evaluation of rare events in active matter from variational path sampling, Phys. Rev. Lett. 128, 028005 (2022).
  44. A. Das and D. T. Limmer, Variational control forces for enhanced sampling of nonequilibrium molecular dynamics simulations, J. Chem. Phys. 151, 244123 (2019).
  45. F. Knoch and T. Speck, Nonequilibrium Markov state modeling of the Globule-Stretch transition, Phys. Rev. E 95, 012503 (2017).
  46. L. Helfmann, E. Ribera Borrell, C. Schütte, and P. Koltai, Extending transition path theory: Periodically driven and finite-time dynamics, J. Nonlinear Sci. 30, 3321 (2020).
  47. J. Strahan, S. C. Guo, C. Lorpaiboon, A. R. Dinner, and J. Weare, Inexact iterative numerical linear algebra for neural network-based spectral estimation and rare-event prediction, J. Chem. Phys. 159, 014110 (2023).
  48. J. Strahan, J. Finkel, A. R. Dinner, and J. Weare, Predicting rare events using neural networks and short-trajectory data, J. Comput. Phys. 488, 112152 (2023).
  49. R. J. Allen, P. B. Warren, and P. R. Ten Wolde, Sampling rare switching events in biochemical networks, Phys. Rev. Lett. 94, 018104 (2005).
  50. A. Dickson, M. Maienschein-Cline, A. Tovo-Dwyer, J. R. Hammond, and A. R. Dinner, Flow-dependent unfolding and refolding of an RNA by nonequilibrium umbrella sampling, J. Chem. Theory Comput. 7, 2710 (2011).
  51. X. Fang, K. Kruse, T. Lu, and J. Wang, Nonequilibrium physics in biology, Rev. Mod. Phys. 91, 045004 (2019).
  52. É. Fodor, C. Nardini, M. E. Cates, J. Tailleur, P. Visco, and F. Van Wijland, How far from equilibrium is active matter? Phys. Rev. Lett. 117, 038103 (2016).
  53. A. Bovier and F. Den Hollander, Metastability: A potential-theoretic approach (Springer, Cham, 2016), Vol. 351.
  54. S. N. Majumdar and H. Orland, Effective Langevin equations for constrained stochastic processes, J. Stat. Mech. (2015) P06039.
  55. R. Chetrite and H. Touchette, Nonequilibrium Markov processes conditioned on large deviations, Ann. Henri Poincaré, 16, 2005 (2015).
  56. R. Chetrite and H. Touchette, Variational and optimal control representations of conditioned and driven processes, J. Stat. Mech. (2015) P12001.
  57. C. Bustamante, Y. R. Chemla, N. R. Forde, and D. Izhaky, Mechanical processes in biochemistry, Annu. Rev. Biochem. 73, 705 (2004).
  58. A. Kolchinsky, N. Ohga, and S. Ito, Thermodynamic bound on spectral perturbations, with applications to oscillations and relaxation dynamics, Phys. Rev. Res. 6, 013082 (2024).
  59. F. Ghimenti, L. Berthier, G. Szamel, and F. van Wijland, Sampling efficiency of transverse forces in dense liquids, Phys. Rev. Lett. 131, 257101 (2023).
  60. T. R. Gingrich and J. M. Horowitz, Fundamental bounds on first passage time fluctuations for currents, Phys. Rev. Lett. 119, 170601 (2017).
  61. Weinan E and E. Vanden-Eijnden, Towards a theory of transition paths, J. Stat. Phys. 123, 503 (2006).
  62. G. M. Rotskoff, A. R. Mitchell, and E. Vanden-Eijnden, Active importance sampling for variational objectives dominated by rare events: Consequences for optimization and generalization, in Mathematical and Scientific Machine Learning edited by J. Bruna, J. Hesthaven, and L. Zdeborova (PMLR/ML Research Press, Cambridge, MA, 2022), Vol. 145, pp. 757–780.
  63. M. R. Hasyim, C. H. Batton, and K. K. Mandadapu, Supervised learning and the finite-temperature string method for computing committor functions and reaction rates, J. Chem. Phys. 157, 184111 (2022).
  64. Y. Khoo, J. Lu, and L. Ying, Solving for high-dimensional committor functions using artificial neural networks, Res. Math. Sci. 6, 1 (2019).
  65. S. Liang, A. N. Singh, Y. Zhu, D. T. Limmer, and C. Yang, Probing reaction channels via reinforcement learning, Mach. Learn.: Sci. Technol. 4, 045003 (2023).
  66. P. Koehl and H. Orland, Sampling constrained stochastic trajectories using Brownian bridges, J. Chem. Phys. 157, 054105 (2022).
  67. E. Woillez, Y. Zhao, Y. Kafri, V. Lecomte, and J. Tailleur, Activated escape of a self-propelled particle from a metastable state, Phys. Rev. Lett. 122, 258001 (2019).
  68. L. Caprini, F. Cecconi, and U. Marini Bettolo Marconi, Correlated escape of active particles across a potential barrier, J. Chem. Phys. 155, 234902 (2021).
  69. J. S. Langer, Statistical theory of the decay of metastable states, Ann. Phys. 54, 258 (1969).
  70. A. Berezhkovskii and A. Szabo, One-dimensional reaction coordinates for diffusive activated rate processes in many dimensions, J. Chem. Phys. 122, 014503 (2005).
  71. B. Peters, Reaction Rate Theory and Rare Events (Elsevier, Amsterdam, 2017).
  72. S. Mallory, M. Bowers, and A. Cacciuto, Universal reshaping of arrested colloidal gels via active doping, J. Chem. Phys. 153, 084901 (2020).
  73. A. Sokolov, M. M. Apodaca, B. A. Grzybowski, and I. S. Aranson, Swimming bacteria power microscopic gears, Proc. Natl. Acad. Sci. USA 107, 969 (2010).
  74. J. D. Weeks, D. Chandler, and H. C. Andersen, Role of repulsive forces in determining the equilibrium structure of simple liquids, J. Chem. Phys. 54, 5237 (1971).
  75. S. Saha, J. Agudo-Canalejo, and R. Golestanian, Scalar active mixtures: The nonreciprocal Cahn-Hilliard model, Phys. Rev. X 10, 041009 (2020).
  76. N. M. Boffi and E. Vanden-Eijnden, Deep learning probability flows and entropy production rates in active matter, Proc. Natl. Acad. Sci. USA 121, e2318106121 (2024).
  77. D. E. Smith, H. P. Babcock, and S. Chu, Single-polymer dynamics in steady shear flow, Science 283, 1724 (1999).
  78. T. T. Perkins, D. E. Smith, R. G. Larson, and S. Chu, Stretching of a single tethered polymer in a uniform flow, Science 268, 83 (1995).
  79. T. T. Perkins, D. E. Smith, and S. Chu, Single polymer dynamics in an elongational flow, Science 276, 2016 (1997).
  80. J. Jaspe and S. J. Hagen, Do protein molecules unfold in a simple shear flow? Biophys. J. 91, 3415 (2006).
  81. C.-C. Huang, R. G. Winkler, G. Sutmann, and G. Gompper, Semidilute polymer solutions at equilibrium and under shear flow, Macromolecules 43, 10107 (2010).
  82. P. S. Doyle, B. Ladoux, and J.-L. Viovy, Dynamics of a tethered polymer in shear flow, Phys. Rev. Lett. 84, 4769 (2000).
  83. R. G. Winkler, Semiflexible polymers in shear flow, Phys. Rev. Lett. 97, 128301 (2006).
  84. C. A. Siedlecki, B. J. Lestini, K. Kottke-Marchant, S. J. Eppell, D. L. Wilson, and R. E. Marchant, Shear-dependent changes in the three-dimensional structure of human von Willebrand factor, Blood 88, 2939 (1996).
  85. A. Buguin and F. Brochard-Wyart, Unwinding of globular polymers under strong flows, Macromolecules 29, 4937 (1996).
  86. P. Eastman, J. Swails, J. D. Chodera, R. T. McGibbon, Y. Zhao, K. A. Beauchamp, L.-P. Wang, A. C. Simmonett, M. P. Harrigan, C. D. Stern et al., OpenMM 7: Rapid development of high performance algorithms for molecular dynamics, PLoS Comput. Biol. 13, e1005659 (2017).
  87. C. R. Sweet, P. Petrone, V. S. Pande, and J. A. Izaguirre, Normal mode partitioning of Langevin dynamics for biomolecules, J. Chem. Phys. 128, 145101 (2008).
  88. W.-C. Yueh, Eigenvalues of several tridiagonal matrices, Appl. Math. E-Notes 5, 66 (2005).
  89. M. Rubinstein and R. Colby, Polymer Physics (Oxford University Press, Oxford, 2003).
  90. C. Van den Broeck and M. Esposito, Three faces of the second law. II. Fokker-Planck formulation, Phys. Rev. E 82, 011144 (2010).
  91. J. L. Rosa-Raíces and D. T. Limmer, Variational time reversal for free-energy estimation in nonequilibrium steady states, Phys. Rev. E 110, 024120 (2024).
  92. P. De Gennes, Coil-stretch transition of dilute flexible polymers under ultrahigh velocity gradients, J. Chem. Phys. 60, 5030 (1974).
  93. R. Delgado-Buscalioni, Cyclic motion of a grafted polymer under shear flow, Phys. Rev. Lett. 96, 088303 (2006).
  94. Y. Zhang, A. Donev, T. Weisgraber, B. J. Alder, M. D. Graham, and J. J. De Pablo, Tethered DNA dynamics in shear flow, J. Chem. Phys. 130, 234902 (2009).
  95. J. L. Lumley, Drag reduction by additives, Annu. Rev. Fluid Mech. 1, 367 (1969).
  96. C. M. Schroeder, R. E. Teixeira, E. S. Shaqfeh, and S. Chu, Characteristic periodic motion of polymers in shear flow, Phys. Rev. Lett. 95, 018301 (2005).
  97. S. Gerashchenko and V. Steinberg, Statistics of tumbling of a single polymer molecule in shear flow, Phys. Rev. Lett. 96, 038304 (2006).
  98. N. Nikola, A. P. Solon, Y. Kafri, M. Kardar, J. Tailleur, and R. Voituriez, Active particles with soft and curved walls: Equation of state, ratchets, and instabilities, Phys. Rev. Lett. 117, 098001 (2016).
  99. Y. Fily, A. Baskaran, and M. F. Hagan, Dynamics of self-propelled particles under strong confinement, Soft Matter 10, 5609 (2014).
  100. Y. Fily, A. Baskaran, and M. F. Hagan, Dynamics and density distribution of strongly confined noninteracting nonaligning self-propelled particles in a nonconvex boundary, Phys. Rev. E 91, 012125 (2015).
  101. F. Smallenburg and H. Löwen, Swim pressure on walls with curves and corners, Phys. Rev. E 92, 032304 (2015).
  102. S. A. Mallory, A. Šarić, C. Valeriani, and A. Cacciuto, Anomalous thermomechanical properties of a self-propelled colloidal fluid, Phys. Rev. E 89, 052303 (2014).
  103. https://github.com/ansingh1214/Noneq-reactive.
  104. D. T. Limmer, Statistical Mechanics and Stochastic Thermodynamics: A Textbook on Modern Approaches in and Out of Equilibrium (Oxford University Press, Oxford, 2024).
  105. I. Loshchilov and F. Hutter, SGDR: Stochastic gradient descent with warm restarts, in International Conference on Learning Representations (ICLR) (ICLR, 2017).

Outline

Information

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation