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REVIEW 3 major objections 7 minor 91 references

Simulating dynamic bonding in soft materials

T0 review · 3 major / 7 minor · reviewed 2026-08-04 · deepseek-v4-flash

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Pith's one-line read A review of soft-matter simulation argues that dynamic, reversible bonding is now modeled by coherent MD, Monte Carlo, and hybrid methods, with remaining obstacles specific to multivalent systems, MC parallelization, and machine-learning fo

desk verdict A useful, explicitly framed review of dynamic-bonding simulation methods; the two transcription slips (Eq. 4, Fig. 3 cross-reference) are correctable and do not undermine the survey's core value. read the letter →

arxiv 2510.08879 v1 pith:VNQOL7NZ submitted 2025-10-10 cond-mat.soft cond-mat.stat-mechphysics.bio-phphysics.comp-ph

Tyla R. Holoman , B. P. Prajwal , Glen M. Hocky , Thomas M. Truskett This is my paper · ORCID
classification cond-mat.softcond-mat.stat-mechphysics.bio-phphysics.comp-ph
keywords dynamicbondingsoftmatterdissociativeassociativemoleculardynamicsMonteCarlokineticcytoskeletalassembly
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This review sets out to show that dynamic—reversible—bonding in soft materials, long treated as a collection of bespoke modeling tricks, has matured into a coherent set of simulation strategies organized around one mechanistic distinction: bonds either break and reform (dissociative) or swap partners while conserving bond number (associative). The authors survey molecular dynamics, Monte Carlo, hybrid MD/MC, and kinetic Monte Carlo approaches and argue that these now primarily address the central tension of the field—bonding kinetics operate at molecular scales while the resulting network structures evolve at mesoscales—so that both can be captured in a single simulation. A sympathetic reader would care because dynamic bonding controls self-healing, reprocessability, and cellular mechanics, and the review's map tells a materials scientist which method to reach for and what each method can be trusted to deliver. If the map is right, the remaining hard problems are specific and solvable: designing balanced Monte Carlo moves for multivalent and multi-species networks, parallelizing MC moves, and building machine-learning force fields and inverse design methods to reach experimentally relevant time and length scales.

What carries the argument

The load-bearing object is the dissociative/associative classification of bond rearrangement, which determines what an algorithm must conserve and how its moves must be biased. Around that distinction the review organizes a set of named computational mechanisms: a three-body potential (a repulsive term added to a short-range bond potential) that lets associative bond swaps proceed continuously in MD with a single tunable barrier parameter; reaction templates that map pre-reaction to post-reaction topologies so bonds can be created and broken on the fly; a bond-boost restraint energy that stretches or compresses a nearly-formed bond to accelerate rare cross-linking events; an explicit-bonding

What would settle it

Run every surveyed method on the same two canonical systems—a vitrimer undergoing bond exchange and an actomyosin network with active crosslinkers—and compare bond lifetimes, network topology, and stress relaxation to reference data. A more immediate check on the text itself: evaluate the bond-boost expression as printed to see whether the distance-variable conflation changes the predicted barrier, and read the cited figure's caption to see which panel actually presents the valence-bias swap algorithm; either discrepancy settling as an error would undermine the review's reliability as a map.

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Extended reading notes

Core claim

The paper's central claim is that the conflict between molecular-scale bonding kinetics and mesoscale network reorganization is now primarily addressed by three families of simulation—molecular dynamics (MD), Monte Carlo (MC), and hybrid MD/MC—each of which can represent associative or dissociative bond rearrangements with tunable kinetics. Within MD, the paper identifies a three-body associative bond-swap potential that creates a user-controlled swap barrier while preserving a one-to-one bonding scheme, reaction-template methods that treat chemical reactions as topology changes and can be parallelized, and a bond-boost acceleration of reactive force fields for atomistic cross-linking with r

Load-bearing premise

The review's value depends on the fidelity of its transcriptions and characterizations of the roughly ninety cited sources, and the text itself shows two local slips—a distance-variable mismatch in the bond-boost energy expression and a figure-panel mismatch for the multivalent bond-swap acceptance rule—so if broader mischaracterizations exist in parts of the survey that cannot be cross-checked here, the map would mislead rather than orient.

Editorial extensions

If this is right

  • Researchers can select a method by mechanism: a three-body potential for associative swaps, reaction-template or bond-boost methods for covalent cross-linking, explicit-bond MC for dissociative equilibria, hybrid MD/MC for decoupling kinetics from thermodynamics, and kinetic Monte Carlo for cytoskeletal networks.
  • For associative bonding, a single parameter in the three-body swap potential controls the swap barrier, so material response, self-healing, and network reconfiguration can be studied as functions of bond-exchange kinetics.
  • For dissociative bonding, hybrid MD/MC schemes let the binding energy and the forward/reverse reaction rates be set independently, enabling direct comparison with experiments on viscoelasticity and gelation.
  • The survey says the field's remaining bottlenecks are concrete: balanced and efficient Monte Carlo moves for multivalent, multi-species networks; parallelization of MC; and machine-learning force fields plus inverse design for longer timescales.
  • If correct, the review implies that a researcher modeling a new dynamically bonded system can choose an existing open-source implementation rather than deriving a new algorithm from scratch.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the map holds, the natural next step is a cross-method benchmark: running the surveyed algorithms on identical vitrimer and actin test systems would turn the qualitative capacity claims into quantitative, comparable numbers.
  • The review's emphasis on detailed balance for multivalent systems suggests that the key algorithmic advance to watch is coupled multi-bond moves with explicit proposal ratios; dense, concentrated networks—where collisions make single-bond swaps unphysical—are the stress test.
  • The bond-boost and machine-learning-force-field directions point to a shift: as time-scale sampling improves, the accuracy bottleneck may move to whether the underlying potentials reproduce both bond-breaking barriers and long-time network reorganization from the same parameters.
  • Because the review itself performs no verification of the methods it describes, a community-maintained reproducibility suite would make the survey self-correcting and would be the fastest way to test whether the claimed capacities are real.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 7 minor

Summary. This paper is a review of simulation methods for dynamic (reversible) bonding in soft materials, organized by method category: molecular dynamics (coarse-grained RevCross three-body potential, REACTER, accelerated ReaxFF), Monte Carlo (eb-RxMC for dissociative bonding, multivalent bond-swap MC for associative bonding), hybrid MD/MC (Hoy-Fredrickson, dybond, TILD), and kinetic Monte Carlo/biopolymer-specific packages (AFINES, Cytosim, aLENS, MEDYAN). The stated purpose is to survey recent advances and highlight outstanding challenges and future directions, with the main outlook items being MC move design for multivalent/multi-species systems, MC parallelization/efficiency, and machine-learning force fields and inverse design. The review contains no new derivations or simulations; its contribution is a curated map of methods with implementation availability summarized in Table 1.

Significance. If the survey is accurate, it provides a useful, reasonably current orientation for researchers entering the dynamic-bonding simulation area, particularly in drawing together synthetic polymer networks (vitrimers, CANs) and cytoskeletal biopolymer networks under a common dissociative/associative framing. The paper is strongest where it is concrete: Table 1 lists algorithms, applications, mechanisms (associative vs. dissociative), and implementation availability, and the text's characterizations of several key methods (RevCross swap barrier, REACTER expansion, AFINES Metropolis factor, Hoy-Fredrickson h parameter) spot-check correctly against the cited primary literature. The review also has practical value in flagging the open problem of balanced MC moves for multivalent systems and the single-processor bottleneck of MC. However, because the paper is exclusively a transcription of ~90 external sources and performs no verification, its value is directly proportional to transcription fidelity, and two visible fidelity failures (Eq. (4) notation, Fig. 3A/B callout) weaken confidence in the map as a whole.

major comments (3)
  1. [§Atomistic force fields, Eq. (4)] Equation (4) is the central quantitative statement for the accelerated ReaxFF capacity claim, but as typeset it conflates the reference bond distance r12 with the instantaneous distance rij: the text says E_rest depends on the interatomic distance r12 and two parameters, then gives E_rest = F1{1 - exp[-F2(rij - r12)^2]} and states that rij is the actual distance. With the bond-boost method (Refs. 57,58), r12 is a fixed reference/equilibrium bond length and rij is the instantaneous separation; as printed the roles are not distinguishable. Because the entire treatment of accelerated ReaxFF rests on this formula, the notation must be corrected and the intended definitions stated explicitly. This is a local correction, but it is exactly the kind of error that undermines a review's orienting function.
  2. [§Monte Carlo, discussion of Eq. (5)] In the description of the Rao et al. multivalent bond-swap algorithm, the text directs the reader to "Fig. 3A" for the unoccupied-valence notation (v_u and v'_u), but Fig. 3's caption places the associative algorithm in panel b (dissociative eb-RxMC is panel a). The cross-reference should be Fig. 3b. This is a minor typographical error in itself, but in a review that asks readers to trust its transcription of ~90 sources, wrong figure callouts reduce reliability; it should be fixed.
  3. [General (transcription-fidelity risk)] The paper's stated purpose is to provide a trustworthy map of the field, yet it performs no independent verification of the methods it describes. Two local failures are visible in the text (Eq. (4) and the Fig. 3A callout). These are individually correctable, and I do not find evidence of broader systemic mischaracterization in the sections I could check against the primary literature. However, since the review's value is precisely its fidelity to external sources, the authors should carefully re-verify all equations, figure references, and implementation claims (especially in Table 1) before publication. I do not treat this as a load-bearing error requiring rejection, but it should be addressed as part of a careful revision.
minor comments (7)
  1. [§Introduction, Fig. 1] Fig. 1 is not referenced in the text at the point where dissociative vs. associative mechanisms are introduced; adding an explicit parenthetical reference would help the reader map terminology to the figure panels.
  2. [§Monte Carlo, Eq. (5)] The notation in Eq. (5) uses p'/v'_u and p/v_u, but the text defines v_u and v'_u as the unoccupied valences for the attacking and leaving residues. The order (forward vs. reverse) should be stated more explicitly to avoid ambiguity about which valence corresponds to which proposal probability.
  3. [§Hybrid MD/MC, Eq. (6)] Equation (6) defines U_sb(r,h) = U_FENE(r) - U_FENE(r0) - h; it would be helpful to state explicitly that U_FENE(r0) is a constant shift so that the well depth is controlled by h, especially since h=0 is described as allowing spontaneous formation.
  4. [§AFINES, Eq. (8)] The text says the motor velocity is v(F_m) = v0 max{1 + F_m · r_hat / F_s, 0}, but it would be worth clarifying the sign convention for the motor force and the direction of motion relative to the filament tangent, as this affects the physical interpretation of force-dependent unbinding/walking.
  5. [General] Some sentences are missing spaces between words (e.g., "Incontrast", "Theconcentration", "Actinisabiopolymer", "AlthoughtheMC"), indicating a spacing/compression artifact in the manuscript text. These should be corrected in the final version.
  6. [§Modeling challenges and outlook] The paragraph on detailed balance cites Ref. [83] for the weak-balance condition; the wording "moves should be chosen such that they leave the equilibrium Boltzmann distribution invariant" could be sharpened to distinguish detailed balance from weak balance, and to note that weak balance alone is sufficient for ergodic sampling.
  7. [Table 1] Table 1 is useful but the 'Implementation' column would be more helpful if it indicated the license or explicit version, and if the GitHub/GitLab URLs were given in a footnote rather than only the primary reference. This is a minor usability suggestion.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the review is a survey that attributes all methods and equations to external, code-backed literature; the authors' self-citations are descriptive, not load-bearing.

full rationale

This paper is a review, not an original derivation. It contains no fitted parameters that are relabeled as predictions, no uniqueness argument imported from the authors' prior work, and no ansatz smuggled in via self-citation. The equations presented (RevCross three-body potential, Erest bond boost, eb-RxMC/Rao acceptance ratios, Hoy-Fredrickson sticky-bond potential, AFINES binding rates, motor velocity law) are transcriptions or summaries of specific external references, and the survey's organizational claims are supported by citations to independent, published, and in several cases open-source code-backed methods. The self-citations that do appear (e.g., AFINES [27,78], dybond [69], and some Truskett-group references) are used to describe actual published methods and results, not to justify the review's central classification scheme. The notation issue in Eq. (4) and the Fig. 3A/3b callout are transcription-fidelity concerns and would be correctness risks if uncorrected, but they are not instances of circular reasoning. No step in the paper reduces to its own inputs by construction, so the appropriate circularity score is 0.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The paper is a review and introduces no new free parameters, fitted quantities, or invented entities. All parameters appearing in quoted equations (lambda, F1, F2, h, kon, koff, v0, Fs, DeltaG0) belong to the cited primary methods and are not estimated here. The ledger therefore contains only the framing assumptions a survey of this kind must make: the organizing dissociative/associative taxonomy, the premise that coarse-grained simulations yield experimentally meaningful insight, the genre-level reliance on the fidelity of transcription, and standard balance requirements for Monte Carlo moves.

assumptions (4)
  • domain assumption Reversible bonding in soft matter is exhaustively partitioned into dissociative and associative mechanisms; this partition organizes the survey.
    Organizing taxonomy stated in the Introduction and used throughout (Fig. 1; MD/MC/hybrid sections). If real networks mix mechanisms or the classification omits a mechanism, the survey's map is incomplete.
  • domain assumption Coarse-grained MD/MC simulations with explicit reaction rules capture bonding kinetics and thermodynamics well enough to yield insights experiments cannot provide.
    The premise that justifies surveying these methods (Abstract: 'providing insights... that cannot be obtained by experiments alone'); not tested by this paper.
  • domain assumption The quoted equations and capability statements faithfully reproduce the cited primary sources.
    The review's validity depends on this fidelity; it is not independently verified here, and two local inconsistencies exist: Eq. (4) conflates r12 and rij, and the Eq. (5) discussion points to 'Fig. 3A' for an algorithm the caption places in panel b.
  • standard math Monte Carlo bond moves should preserve the equilibrium distribution via detailed balance or weaker balance.
    Invoked in 'Modeling challenges and outlook' (citing [83, 84]) as the criterion for correct MC bonding moves; standard statistical mechanics.

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Cite this review

Pith. "Pith review of Simulating dynamic bonding in soft materials." pith.science (2026). https://pith.science/paper/VNQOL7NZ

@misc{pith2026251008879,
  author       = {Pith},
  title        = {Pith review of: Simulating dynamic bonding in soft materials},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VNQOL7NZ}},
  note         = {Machine review of arXiv:2510.08879}
}
read the original abstract

Dynamic bonding is an essential feature of many soft materials. Molecular simulations have proven to be a powerful tool for modeling bonding kinetics and thermodynamics in these materials, providing insights into their properties that cannot be obtained by experiments alone. Here, we review recent advances in modeling dynamic bonding in soft matter via molecular dynamics, Monte Carlo, and hybrid simulation methods, highlighting outstanding challenges and future directions.

Figures

Figures reproduced from arXiv: 2510.08879 by the authors.

Figure 1
Figure 1. Mechanisms associated with network formation and reversible rearrangement [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Examples of simulated dynamic soft matter. a) Self-healing vitrimers (repro [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Comparison of Monte Carlo algorithms for modeling a) dissociative (reproduced [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗

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Reference graph

Works this paper leans on

91 extracted references · 45 canonical work pages

  1. [1]

    J., Bowman, C

    Kloxin, C. J., Bowman, C. N., Covalent adaptable networks: Smart, reconfigurable and responsive network systems, Chem. Soc. Rev. 2013, 42 7161–7173. doi:10.1039/c3cs60046g

    work page doi:10.1039/c3cs60046g 2013

  2. [2]

    J., Tibbitt, M

    Webber, M. J., Tibbitt, M. W., Dynamic and reconfigurable materials from reversible network interactions, Nat. Rev. Mater. 2022, 7 541–556. doi:10.1038/s41578-021-00412-x

    work page doi:10.1038/s41578-021-00412-x 2022

  3. [3]

    V., Couture, O., Hesse, C., Schmidt, D

    Karatrantos, A. V., Couture, O., Hesse, C., Schmidt, D. F., Molecu- lar simulation of covalent adaptable networks and vitrimers: A review, Polymers 2024, 16. doi:10.3390/polym16101373

    work page doi:10.3390/polym16101373 2024

  4. [4]

    V., Kalow, J

    Zhang, V., Kang, B., Accardo, J. V., Kalow, J. A., Struc- ture–reactivity–property relationships in covalent adaptable networks, J. Am. Chem. Soc. 2022, 144 22358–22377. doi:10.1021/jacs.2c08104

    work page doi:10.1021/jacs.2c08104 2022

  5. [5]

    N., Howard, M

    Dominguez, M. N., Howard, M. P., Maier, J. M., Valenzuela, S. A., Sherman, Z. M., Reuther, J. F., Reimnitz, L. C., Kang, J., Cho, S. H., Gibbs, S. L., et al., Assembly of linked nanocrystal colloids by reversible covalent bonds, Chem. Mater. 2020, 32 10235–10245

    2020

  6. [6]

    A., Lin, E

    Kang, J., Valenzuela, S. A., Lin, E. Y., Dominguez, M. N., Sherman, Z. M., Truskett, T. M., Anslyn, E. V., Milliron, D. J., Colorimetric quantification of linking in thermoreversible nanocrystal gel assemblies, Sci. Adv. 2022, 8 eabm7364

    2022

  7. [7]

    W., Liu, J., Anslyn, E., Truskett, T., Milliron, D., Colloidal phase con- trol in plasmonic metal oxide nanocrystals via competitive metal–ligand equilibria, ChemRxiv

    Kang, J., Qian, D., Lee, J., Conrad, D., Oberlander, J., Berry, M. W., Liu, J., Anslyn, E., Truskett, T., Milliron, D., Colloidal phase con- trol in plasmonic metal oxide nanocrystals via competitive metal–ligand equilibria, ChemRxiv. 2025; doi:10.26434/chemrxiv-2025-42gc2 2025. 16

    work page doi:10.26434/chemrxiv-2025-42gc2 2025

  8. [8]

    M., Rowan, S

    Kim, H., Livermore, S. M., Rowan, S. J., Jaeger, H. M., Dense suspen- sions as trainable rheological metafluids, Proc. Natl. Acad. Sci. USA 2025, 122 e2509525122

    2025

Show all 91 references
  1. [9]

    J., Leeuwenburgh, S

    Bertsch, P., Diba, M., Mooney, D. J., Leeuwenburgh, S. C. G., Self- healing injectable hydrogels for tissue regeneration, Chem. Rev. 2023, 123 834–873. doi:10.1021/acs.chemrev.2c00179

    2023 doi

  2. [10]

    W., Self-healing polymers, Nat

    Wang, S., Urban, M. W., Self-healing polymers, Nat. Rev. Mater. 2020, 5 562–583

    2020

  3. [11]

    M., Oentoro, F., Shanbhag, T

    FitzSimons, T. M., Oentoro, F., Shanbhag, T. V., Anslyn, E. V., Ros- ales, A. M., Preferential control of forward reaction kinetics in hydrogels crosslinked with reversible conjugate additions, Macromolecules 2020, 53 3738–3746. doi:10.1021/acs.macromol.0c00335

    2020 doi

  4. [12]

    M., Appel, E

    Yesilyurt, V., Ayoob, A. M., Appel, E. A., Borenstein, J. T., Langer, R., Anderson, D. G., Mixed Reversible Covalent Crosslink Kinetics Enable Precise, Hierarchical Mechanical Tuning of Hydrogel Networks, Adv. Mat. 2017, 29. doi:10.1002/adma.201605947

    2017 doi

  5. [13]

    D., FitzSimons, T

    Crowell, A. D., FitzSimons, T. M., Anslyn, E. V., Schultz, K. M., Ros- ales, A. M., Shear thickening behavior in injectable tetra-peg hydrogels cross-linked via dynamic thia-michael addition bonds, Macromolecules 2023, 56 7795–7807

    2023

  6. [14]

    M., Anseth, K

    Tang, S., Richardson, B. M., Anseth, K. S., Dynamic covalent hydrogels as biomaterials to mimic the viscoelasticity of soft tissues, Prog. Mater. Sci. 2021, 120 100738

    2021

  7. [15]

    L., Divergent shear thinning and shear thickening behavior of supramolecular polymer networks in semidilute entangled polymer solutions, Macromolecules 2011, 44 2343–2353

    Xu, D., Liu, C.-Y., Craig, S. L., Divergent shear thinning and shear thickening behavior of supramolecular polymer networks in semidilute entangled polymer solutions, Macromolecules 2011, 44 2343–2353

    2011

  8. [16]

    E., Dolinski, N

    Crolais, A. E., Dolinski, N. D., Boynton, N. R., Radhakrishnan, J. M., Snyder, S. A., Rowan, S. J., Enhancing the equilibrium of dynamic thia-Michael reactions through heterocyclic design, J. Am. Chem. Soc. 2023, 145 14427–14434. 17

    2023

  9. [17]

    H., Schwarz, W., Pollard, T., Cross-linker dynamics determine the mechanical properties of actin gels, Biophys

    Wachsstock, D. H., Schwarz, W., Pollard, T., Cross-linker dynamics determine the mechanical properties of actin gels, Biophys. J. 1994, 66 801–809. doi:10.1016/s0006-3495(94)80856-2

    1994 doi

  10. [18]

    M., Bausch, A

    Lieleg, O., Claessens, M. M., Bausch, A. R., Structure and dy- namics of cross-linked actin networks, Soft Matter 2010, 6 218–225. doi:10.1039/B912163N

    2010 doi

  11. [19]

    M., Anseth, K

    Rosales, A. M., Anseth, K. S., The design of reversible hydrogels to capture extracellular matrix dynamics, Nat. Rev. Mater. 2016, 1 1–15

    2016

  12. [20]

    B., Chen, M

    Loebel, C., Rodell, C. B., Chen, M. H., Burdick, J. A., Shear-thinning and self-healing hydrogels as injectable therapeutics and for 3d-printing, Nat. Protoc. 2017, 12 1521–1541

    2017

  13. [21]

    K., Schmidt, F

    Brandt, J., Oehlenschlaeger, K. K., Schmidt, F. G., Barner-Kowollik, C., Lederer, A., State-of-the-art analytical methods for assessing dy- namic bonding soft matter materials, Adv. Mat. 2014, 26 5758–5785. doi:https://doi.org/10.1002/adma.201400521

    2014 doi

  14. [22]

    doi:10.1021/ma201847v

    Lange, F., Schwenke, K., Kurakazu, M., Akagi, Y., Chung, U.-i., Lang, M., Sommer, J.-U., Sakai, T., Saalwächter, K., Connectivity and Struc- tural Defects in Model Hydrogels: A Combined Proton NMR and Monte Carlo Simulation Study, Macromolecules 2011, 44 9666–9674. doi:10.1021...

    2011 doi

  15. [23]

    D., Johnson, J

    Zhong, M., Wang, R., Kawamoto, K., Olsen, B. D., Johnson, J. A., Quantifying the impact of molecular defects on polymer network elas- ticity, Science 2016, 353 1264–1268. doi:10.1126/science.aag0184

    2016 doi

  16. [24]

    A., Olsen, B

    Lin, T.-S., Wang, R., Johnson, J. A., Olsen, B. D., Revisiting the Elas- ticity Theory for Real Gaussian Phantom Networks, Macromolecules 2019, 52 1685–1694. doi:10.1021/acs.macromol.8b01676

    2019 doi

  17. [25]

    doi:10.3390/coatings9020114

    Ciarella, S., Ellenbroek, W., Swap-Driven Self-Adhesion and Healing of Vitrimers, Coatings 2019, 9 114. doi:10.3390/coatings9020114

    2019 doi

  18. [26]

    doi:10.1021/acs.macromol.4c01849, *Bond exchange reac- tions modeled with REACTER give insight into the topological impact of material properties in polymer-grafted nanoparticles

    Chen, Q., Huang, W., Zhang, L., Ganesan, V., Liu, J., Topological Design and Mechanical Manipulation of Matrix-Free Polymer Grafted Nanoparticles Driven by Bond Exchanging, Macromolecules 2024, 57 18 10474–10486. doi:10.1021/acs.macromol.4c01849, *Bond exchange reac- tions mod...

    2024 doi

  19. [27]

    L., Hocky, G

    Freedman, S. L., Hocky, G. M., Banerjee, S., Dinner, A. R., Nonequi- librium phase diagrams for actomyosin networks, Soft Matter 2018, 14 7740–7747. doi:10.1039/C8SM00741A

    2018 doi

  20. [28]

    A., Blackwell, R., Better- ton, M

    Yan, W., Ansari, S., Lamson, A., Glaser, M. A., Blackwell, R., Better- ton, M. D., Shelley, M., Toward the cellular-scale simulation of motor- driven cytoskeletal assemblies, eLife 2022, 11. doi:10.7554/eLife.74160

    2022 doi

  21. [29]

    Bianchi, E., Largo, J., Tartaglia, P., Zaccarelli, E., Sciortino, F., Phase diagram of patchy colloids: Towards empty liquids, Phys. Rev. Lett. 2006, 97 168301

    2006

  22. [30]

    P., Jadrich, R

    Howard, M. P., Jadrich, R. B., Lindquist, B. A., Khabaz, F., Bonnecaze, R. T., Milliron, D. J., Truskett, T. M., Structure and phase behavior of polymer-linked colloidal gels, J. Chem. Phys. 2019, 151

    2019

  23. [31]

    G., Associative bond swaps in molecular dy- namics, SciPost Phys

    Ciarella, S., Ellenbroek, W. G., Associative bond swaps in molecular dy- namics, SciPost Phys. 2022, 12. doi:10.21468/SCIPOSTPHYS.12.4.128

    2022 doi

  24. [32]

    doi:10.1021/acs.langmuir.4c00699

    Li, Z., Zhao, H., Duan, P., Zhang, L., Liu, J., Manipulat- ing the Properties of Polymer Vitrimer Nanocomposites by Design- ing Dual Dynamic Covalent Bonds, Langmuir 2024, 40 7769–7780. doi:10.1021/acs.langmuir.4c00699

    2024 doi

  25. [33]

    S., Sing, C

    Lin, T.-W., Mei, B., Dutta, S., Schweizer, K. S., Sing, C. E., Molecular dynamics simulation and theoretical analysis of structural relaxation, bond exchange dynamics, and glass transition in vitrimers, Macro- molecules 2025, 58 1481–1497

    2025

  26. [34]

    Zhao, H., Wei, X., Fang, Y., Gao, K., Yue, T., Zhang, L., Ganesan, V., Meng, F., Liu, J., Molecular dynamics simulation of the structural, mechanical, and reprocessing properties of vitrimers based on a dynamic covalent polymer network, Macromolecules 2022, 55 1091–1103

    2022

  27. [35]

    K., Combinatorial- entropy-driven aggregation in DNA-grafted nanoparticles, ACS Nano 2020, 14 5628–5635

    Sciortino, F., Zhang, Y., Gang, O., Kumar, S. K., Combinatorial- entropy-driven aggregation in DNA-grafted nanoparticles, ACS Nano 2020, 14 5628–5635. 19

    2020

  28. [36]

    Rovigatti, L., Sciortino, F., Designing enhanced entropy binding in single-chain nanoparticles, Phys. Rev. Lett. 2022, 129 047801

    2022

  29. [37]

    2023, 15 163

    Rovigatti, L., Sciortino, F., Entropy-driven phase behavior of associa- tive polymer networks, SciPost Phys. 2023, 15 163. *This work uses molecular dynamics simulations to establish the role of entropy in de- termining the interaction between polymer networks comprising stick...

    2023

  30. [38]

    Tosti Guerra, F., Marini, F., Sciortino, F., Rovigatti, L., Entropy-driven phase behavior of all-DNA associative polymers, J. Chem. Phys. 2025, 163

    2025

  31. [39]

    Sciortino, F., Three-bodypotentialforsimulatingbondswapsinmolecu- lar dynamics, Eur. Phys. J. E 2017, 40. doi:10.1140/epje/i2017-11496-5

    2017 doi

  32. [40]

    A., Glaser, J., Glotzer, S

    Anderson, J. A., Glaser, J., Glotzer, S. C., HOOMD-blue: A Python package for high-performance molecular dynamics and hard parti- cle Monte Carlo simulations, Comp. Mat. Sci. 2020, 173 109363. doi:10.1016/j.commatsci.2019.109363

    2020

  33. [41]

    R., Jensen, B

    Gissinger, J. R., Jensen, B. D., Wise, K. E., Modeling chemical reactions in classical molecular dynamics simulations, Polymer 2017, 128 211–217. doi:10.1016/j.polymer.2017.09.038

    2017 doi

  34. [42]

    P., Aktulga, H

    Thompson, A. P., Aktulga, H. M., Berger, R., Bolintineanu, D. S., Brown, W. M., Crozier, P. S., in ’t Veld, P. J., Kohlmeyer, A., Moore, S. G., Nguyen, T. D., Shan, R., Stevens, M. J., Tranchida, J., Trott, C., Plimpton, S. J., LAMMPS - a flexible simulation tool for particle-...

    2022

  35. [43]

    doi:10.1021/acs.macromol.1c02034

    Zhao, H., Wei, X., Fang, Y., Gao, K., Yue, T., Zhang, L., Ganesan, V., Meng, F., Liu, J., Molecular Dynamics Simulation of the Structural, Mechanical, and Reprocessing Properties of Vitrimers Based on a Dy- namic Covalent Polymer Network, Macromolecules 2022, 55 1091–1103. doi...

    2022 doi

  36. [44]

    doi:10.1016/j.polymer.2024.127667, *All-atom simulations that use REACTER to model bond swaps in vitrimers to understand their behavior under creep loading

    Singh, G., Varshney, V., Sundararaghavan, V., Understanding creep in vitrimers: Insights from molecular dynamics simulations, Polymer 2024, 20 313 127667. doi:10.1016/j.polymer.2024.127667, *All-atom simulations that use REACTER to model bond swaps in vitrimers to understand t...

    2024

  37. [45]

    Singh, G., Sundararaghavan, V., Modeling self-healing behavior of vit- rimers using molecular dynamics with dynamic cross-linking capability, Chem. Phys. Lett. 2020, 760 137966. doi:10.1016/j.cplett.2020.137966

    2020

  38. [46]

    R., Jensen, B

    Gissinger, J. R., Jensen, B. D., Wise, K. E., REACTER: A heuristic methodforreactivemoleculardynamics, Macromolecules2020, 539953–

  39. [47]

    R., Jensen, B

    Gissinger, J. R., Jensen, B. D., Wise, K. E., Molecular modeling of reactive systems with REACTER, Comput. Phys. Commun. 2024, 304 109287. doi:10.1016/j.cpc.2024.109287, **REACTER uses reaction tem- plates to treat reversible reactions as topological changes within the system

    2024

  40. [48]

    Warshel, A., Levitt, M., Theoretical studies of enzymic reactions: di- electric, electrostatic and steric stabilization of the carbonium ion in the reaction of lysozyme, J. Mol. Biol. 1976, 103 227–249

    1976

  41. [49]

    Brunk, E., Rothlisberger, U., Mixed quantum mechanical/molecular mechanical molecular dynamics simulations of biological systems in ground and electronically excited states, Chem. Rev. 2015, 115 6217– 6263

    2015

  42. [50]

    M., Capece, L., Martí, M

    Clemente, C. M., Capece, L., Martí, M. A., Best practices on QM/MM simulations of biological systems, J. Chem. Inf. Model. 2023, 63 2609– 2627

    2023

  43. [51]

    Ho, J., Yu, H., Shao, Y., Taylor, M., Chen, J., How accurate are QM/MM models?, J. Phys. Chem. A 2024, 129 1517–1528

    2024

  44. [52]

    E., Bringing quantum mechanics to coarse- grained soft materials modeling, Chem

    Wang, C.-I., Jackson, N. E., Bringing quantum mechanics to coarse- grained soft materials modeling, Chem. Mater. 2023, 35 1470–1486

    2023

  45. [53]

    van Duin, A. C. T., Dasgupta, S., Lorant, F., Goddard, W. A., ReaxFF: A Reactive Force Field for Hydrocarbons, J. Phys. Chem. A 2001, 105 9396–9409. doi:10.1021/jp004368u. 21

    2001 doi

  46. [54]

    Chenoweth, K., van Duin, A. C. T., Goddard, W. A., ReaxFF Reactive Force Field for Molecular Dynamics Simulations of Hydrocarbon Oxi- dation, J. Phys. Chem. A 2008, 112 1040–1053. doi:10.1021/jp709896w

    2008 doi

  47. [55]

    A., Akbarian, D., Evangelisti, B., Hossain, M

    Leven, I., Hao, H., Tan, S., Guan, X., Penrod, K. A., Akbarian, D., Evangelisti, B., Hossain, M. J., Islam, M. M., Koski, J. P., et al., Recent advances for improving the accuracy, transferability, and efficiency of reactive force fields, J. Chem. Inf. Model. 2021, 17 3237–3251

    2021

  48. [56]

    P., Hong, S., Islam, M

    Senftle, T. P., Hong, S., Islam, M. M., Kylasa, S. B., Zheng, Y., Shin, Y. K., Junkermeier, C., Engel-Herbert, R., Janik, M. J., Aktulga, H. M., Verstraelen, T., Grama, A., van Duin, A. C. T., The ReaxFF reactive force-field: development, applications and future directions, np...

    2016 doi

  49. [57]

    E., van Duin, A

    Vashisth, A., Ashraf, C., Zhang, W., Bakis, C. E., van Duin, A. C. T., Accelerated ReaxFF Simulations for Describing the Reactive Cross-Linking of Polymers, J. Phys. Chem. A 2018, 122 6633–6642. doi:10.1021/ACS.JPCA.8B03826

    2018 doi

  50. [58]

    A., Fichthorn, K

    Miron, R. A., Fichthorn, K. A., Accelerated molecular dynamics with the bond-boost method, J. Chem. Phys. 2003, 119 6210–6216. doi:10.1063/1.1603722

    2003 doi

  51. [59]

    Zheng, Y., Varshney, V., Vashisth, A., Accelerated ReaxFF Simula- tions of Vitrimers with Dynamic Covalent Adaptive Networks, Macro- molecules2025, 584948–4958.doi:10.1021/acs.macromol.5c00501, *This work introduces an accelerated ReaxFF method to perform atomistic molecular d...

    doi

  52. [60]

    Vermeersch, L., Wang, T., Van den Brande, N., De Vleeschouwer, F., van Duin, A. C. T., Computational Insights into Tunable Reversible Network Materials: Accelerated ReaxFF Kinetics of Furan-Maleimide Diels–AlderReactionsforSelf-HealingandRecyclability, J.Phys.Chem. A 2024, 128...

    2024 doi

  53. [61]

    K., Panagiotopoulos, A

    Johnson, J. K., Panagiotopoulos, A. Z., Gubbins, K. E., Re- active canonical Monte Carlo, Mol. Phys. 1994, 81 717–733. doi:10.1080/00268979400100481. 22

    1994 doi

  54. [62]

    R., Triska, B., The reaction ensemble method for the com- puter simulation of chemical and phase equilibria

    Smith, W. R., Triska, B., The reaction ensemble method for the com- puter simulation of chemical and phase equilibria. I. Theory and basic examples, J. Chem. Phys. 1994, 100 3019–3027. doi:10.1063/1.466443

    1994 doi

  55. [63]

    H., Brennan, J

    Turner, C. H., Brennan, J. K., Lisal, M., Smith, W. R., Karl Johnson, J., Gubbins, K. E., Simulation of chemical reaction equilibria by the reaction ensemble Monte Carlo method: A review, Molecular Simulation 2008, 34 119–146. doi:10.1080/08927020801986564

    2008 doi

  56. [64]

    M., Košovan, P., The explicit bonding reaction ensemble Monte Carlo method, J

    Blanco, P. M., Košovan, P., The explicit bonding reaction ensemble Monte Carlo method, J. Chem. Phys. 2024, 161. doi:10.1063/5.0226122, *The explicit bonding reaction ensemble Monte Carlo method forms and breaks explicit bonds using Monte Carlo moves. This method can be couple...

    2024 doi

  57. [65]

    Rao, P., Xia, X., Ni, R., A bond swap algorithm for simulat- ing dynamically crosslinked polymers, J. Chem. Phys. 2024, 160. doi:10.1063/5.0186553, **This work introduces a Monte Carlo-based bond-swap algorithm that enables associative reactions between mul- tivalent species

    2024 doi

  58. [66]

    P., Ni, R., Entropy-Driven Thermo-gelling Vitrimer, JACS Au 2022 2359–2366

    Xia, X., Rao, P., Yang, J., Ciamarra, M. P., Ni, R., Entropy-Driven Thermo-gelling Vitrimer, JACS Au 2022 2359–2366

    2022

  59. [67]

    S., Fredrickson, G

    Hoy, R. S., Fredrickson, G. H., Thermoreversible associating polymer networks. I. Interplay of thermodynamics, chemical kinetics, and poly- mer physics, J. Chem. Phys. 2009, 131. doi:10.1063/1.3268777

    2009 doi

  60. [68]

    M., Estridge, C

    Thomas, S., Alberts, M., Henry, M. M., Estridge, C. E., Jankowski, E., Routine million-particle simulations of epoxy curing with dissipa- tive particle dynamics, J. Theor. Comput. Chem. 2018, 17 1840005. doi:10.1142/S0219633618400059

    2018 doi

  61. [69]

    M., A coarse-grained simulation model for colloidal self- assembly via explicit mobile binders, Soft Matter 2023, 19 4223–4236

    Mitra, G., Chang, C., McMullen, A., Puchall, D., Brujic, J., Hocky, G. M., A coarse-grained simulation model for colloidal self- assembly via explicit mobile binders, Soft Matter 2023, 19 4223–4236. doi:10.1039/D3SM00196B, **MD/MC method implementing dissocia- tive bonding in ...

    2023 doi

  62. [70]

    Jedlinska, Z.M., Riggleman, R.A., EffectsofAssociativeInteractionson the Phase Behavior of Complex Coacervates, Macromolecules 2024, 57 4323–4334. doi:10.1021/acs.macromol.4c00367, *MD/MC simulations using theoretically informed Langevin dynamics show that changing the energy ...

    2024 doi

  63. [71]

    M., Tabedzki, C., Gillespie, C., Hess, N., Yang, A., Riggle- man, R

    Jedlinska, Z. M., Tabedzki, C., Gillespie, C., Hess, N., Yang, A., Riggle- man, R. A., MATILDA.FT: A mesoscale simulation package for inhomo- geneous soft matter, J. Chem. Phys. 2023, 159. doi:10.1063/5.0145006

    2023 doi

  64. [72]

    T., Rudnicki, P

    Li, D. T., Rudnicki, P. E., Qin, J., Distribution cutoff for clusters near the gel point, ACS Polymers Au 2022, 2 361–370

    2022

  65. [73]

    K., Sastry, S., Kumar, S

    Karmakar, R., Venkatareddy, N., Valsecchi, M., Maiti, P. K., Sastry, S., Kumar, S. K., Patra, T. K., et al., Computer simulations of entropic cohesion in reversibly crosslinked polymers, Soft Matter 2025, 21 348– 355

    2025

  66. [74]

    doi:10.1021/acs.macromol.0c01423

    Perego, A., Khabaz, F., Volumetric and Rheological Proper- ties of Vitrimers: A Hybrid Molecular Dynamics and Monte Carlo Simulation Study, Macromolecules 2020, 53 8406–8416. doi:10.1021/acs.macromol.0c01423

    2020 doi

  67. [75]

    doi:10.1021/acs.macromol.2c00588

    Perego, A., Lazarenko, D., Cloitre, M., Khabaz, F., Microscopic Dynam- icsandViscoelasticityofVitrimers, Macromolecules2022, 557605–7613. doi:10.1021/acs.macromol.2c00588

    doi

  68. [76]

    B., Cai, L.-H., Kumar, N

    Stukalin, E. B., Cai, L.-H., Kumar, N. A., Leibler, L., Rubinstein, M., Self-Healing of Unentangled Polymer Networks with Reversible Bonds, Macromolecules 2013, 46 7525–7541. doi:10.1021/ma401111n

    2013 doi

  69. [77]

    Liu, J., Jin, J., Conka, R., Van Steenberge, P. H. M., D’hooge, D. R., Luo, Z.-H., Zhou, Y.-N., The dynamic interplay of cross- linking and exchange reaction probabilities during vitrimer synthe- sis: A Monte Carlo approach, Macromolecules 2024, 57 6927–6940. doi:10.1021/acs.m...

    2024 doi

  70. [78]

    L., Banerjee, S., Hocky, G

    Freedman, S. L., Banerjee, S., Hocky, G. M., Dinner, A. R., A Versatile Framework for Simulating the Dynamic Mechanical Struc- 24 ture of Cytoskeletal Networks, Biophys. J. 2017, 113 448–460. doi:10.1016/J.BPJ.2017.06.003

    2017 doi

  71. [79]

    Lugo, C.A., Saikia, E., Nedelec, F., ATypicalWorkflowtoSimulateCy- toskeletal Systems, J. Vis. Exp. 2023, 2023 e64125. doi:10.3791/64125

    2023 doi

  72. [80]

    J., Hagan, M

    Najma, B., Ghosh, S., Amey, C., Foster, P. J., Hagan, M. F., Baskaran, A., Duclos, G., Arrested coalescence, aging, and stability of asters composed of microtubules and kinesin motors, Phys. Rev. Res. 2025, 7 013247. doi:10.1103/PhysRevResearch.7.013247, *aLENS simulations cou...

    2025 doi

  73. [81]

    J., Computing collision stress in assem- blies of active spherocylinders: Applications of a fast and generic geo- metric method, J

    Yan, W., Zhang, H., Shelley, M. J., Computing collision stress in assem- blies of active spherocylinders: Applications of a fast and generic geo- metric method, J. Chem. Phys. 2019, 150 64109. doi:10.1063/1.5080433

    2019 doi

  74. [82]

    A., MEDYAN: Mechanochem- ical Simulations of Contraction and Polarity Alignment in Ac- tomyosin Networks, PLoS Comput

    Popov, K., Komianos, J., Papoian, G. A., MEDYAN: Mechanochem- ical Simulations of Contraction and Polarity Alignment in Ac- tomyosin Networks, PLoS Comput. Biol. 2016, 12 e1004877. doi:10.1371/journal.pcbi.1004877

    2016 doi

  75. [83]

    I., Deem, M

    Manousiouthakis, V. I., Deem, M. W., Strict detailed balance is unnec- essary in Monte Carlo simulation, J. Chem. Phys. 1999, 110 2753–2756. doi:10.1063/1.477973

    1999 doi

  76. [84]

    W., Corti, D

    Hatch, H. W., Corti, D. S., Kofke, D. A., Shen, V. K., Best practices for developing Monte Carlo methodologies in molecular simulations [article v1. 0], Living J. Comput. Mol. Sci. 2025, 6 3289–3289

    2025

  77. [85]

    Sun, Y., Wan, K., Shen, W., He, J., Zhou, T., Wang, H., Yang, H., Shi, X., Modeling exchange reactions in covalent adaptable networks with machine learning force fields, Macromolecules 2023, 56 9003–9013

    2023

  78. [86]

    Marín-Aguilar, S., Zaccarelli, E., Predicting structure and swelling of microgels with different crosslinker concentrations combining machine- learning with numerical simulations, arXiv preprint arXiv:2502.07482 2025. 25

    2025 arXiv

  79. [87]

    C., Castaneda-Priego, R., Machine learning for condensed matter physics, Journal of Physics: Condensed Matter 2020, 33 053001

    Bedolla, E., Padierna, L. C., Castaneda-Priego, R., Machine learning for condensed matter physics, Journal of Physics: Condensed Matter 2020, 33 053001

    2020

  80. [88]

    M., Ganesan, V., Truskett, T

    Kadulkar, S., Sherman, Z. M., Ganesan, V., Truskett, T. M., Ma- chine learning–assisted design of material properties, Ann. Rev. Chem. Biomol. Eng. 2022, 13 235–254

    2022

  81. [89]

    Yan, C., Feng, X., Konlan, J., Mensah, P., Li, G., Overcoming the barrier: designing novel thermally robust shape memory vitrimers by establishing a new machine learning framework, Phys. Chem. Chem. Phys. 2023, 25 30049–30065

    2023

  82. [90]

    K., Smith, J

    Zheng, Y., Thakolkaran, P., Biswal, A. K., Smith, J. A., Lu, Z., Zheng, S., Nguyen, B.H., Kumar, S., Vashisth, A., AI-guidedinversedesignand discovery of recyclable vitrimeric polymers, Adv. Sci. 2025, 12 2411385. 26

    2025

  83. [9961]

    doi:10.1021/acs.macromol.0c02012

    doi

Pith tools

Reviewed August 4, 2026 · model on record in the stance chip above.