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REVIEW 3 major objections 5 minor 58 references

NAVIS: A LAMMPS-Python framework for efficient computation of nanochannel velocity and thermal interfacial slip

T0 review · 3 major / 5 minor · reviewed 2026-08-03 · deepseek-v4-flash

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Pith's one-line read NAVIS extracts the intrinsic friction and Kapitza resistance at a solid-fluid interface from fluctuations in a single equilibrium molecular dynamics run, avoiding the Green-Kubo plateau problem by fitting a single-exponential memory kernel

desk verdict A useful but unverified software wrapper around validated EMD slip methods; Method-3's stability claim needs a kernel-model caveat. read the letter →

arxiv 2601.11391 v2 pith:YJDZH4QS submitted 2026-01-16 cond-mat.soft physics.comp-ph

Sleeba Varghese , Sobin Alosious , Jesper Schmidt Hansen , Billy Dean Todd This is my paper · ORCID
classification cond-mat.softphysics.comp-ph
keywords NavierfrictioncoefficientKapitzaresistanceequilibriummoleculardynamicsvelocityslipthermalinterfacialslabLaplace-domainanalysisnanochannel
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

The paper presents NAVIS, a Python toolkit that computes the intrinsic Navier friction coefficient and Kapitza resistance of a solid-fluid interface from fluctuations measured in an equilibrium molecular dynamics simulation. The key move is to restrict all correlation functions to a thin slab of fluid next to the wall, making the result a local interface property rather than a bulk property. To bypass the divergence of Green-Kubo integrals in confined systems, the toolkit assumes the friction and thermal memory kernels are single exponentials and extracts zero-frequency coefficients in the Laplace domain. The paper argues that its preferred Method-3 yields friction values that do not drift with correlation lag time and that its Kapitza method similarly stays flat while the standard Green-Kubo analogue diverges.

What carries the argument

The load-bearing object is the one-term Maxwellian memory kernel, ζ(t)=B1 e^{-λ1 t} for momentum and G_k(t)=k1 e^{-μ1 t} for heat, together with the Laplace-domain relation C̃_{u_x F_x}(s)=-(B1/(s+λ1)) C̃_{u_x u_x}(s) and its thermal analogue. The extraction compares Laplace transforms of measured slab-velocity/force and temperature/heat-flux correlations. Method-3 integrates these transforms over two intervals in s-space to solve for B1 and λ1 algebraically, removing the need to truncate the correlation function at long times. The dynamic fluid slab, defined as the layer within a few molecular diameters of the wall, is what makes the computed coefficients intrinsic to the interface rather t

What would settle it

Run the same equilibrium simulation with several slab thicknesses Δ (e.g. 2, 3, 4, and 5 Å) and check whether ξ0 and Rk remain constant; or fit the ratio of Laplace-transformed correlation functions at many s values and look for systematic deviation from the single-exponential form. A visibly non-exponential ratio, or a drift of ξ0 with Δ, would falsify the central extraction claim.

Watch

Extended reading notes

Core claim

The central claim is that interfacial hydrodynamic and thermal resistance can be obtained from a single equilibrium simulation by correlating fluctuations in a molecularly thin slab adjacent to the wall, with the extraction stabilized by modelling the friction memory kernel as ζ(t)=B1 e^{-λ1 t} and the thermal kernel as G_k(t)=k1 e^{-μ1 t}. The zero-frequency friction coefficient ξ0=B1/(Aλ1) and the Kapitza resistance Rk=μ1/k1 follow from Laplace-transforming the measured correlation functions and fitting or algebraically inverting the single-exponential form. For a water-graphene channel, Method-3 gives friction coefficients that stay constant as the correlation lag time is varied, unlike t

Load-bearing premise

The derivation assumes the friction memory kernel is exactly a single decaying exponential, ζ(t)=B1 e^{-λ1 t}, and the thermal kernel analogously a single exponential; if the true kernel has a second timescale or a different shape, the extracted zero-frequency coefficient is biased, and the paper gives no test of this shape assumption.

Editorial extensions

If this is right

  • Users can obtain velocity-slip and thermal-slip parameters from one equilibrium run with wall thermostatting, without imposing a flow or a temperature gradient.
  • Because the correlation functions are restricted to a thin slab, the method can deliver separate intrinsic friction coefficients for two different walls in the same simulation.
  • The Kapitza-resistance version works in cylindrical confinement without needing an auxiliary inner cylinder to generate a temperature gradient.
  • Method-3's stability across correlation lag times removes the need to visually pick a plateau region, a standard bottleneck in Green-Kubo analysis.
  • The packaged Python algorithms for correlation and Laplace transformation are reusable for arbitrary solid-fluid interfaces, not just the two water-carbon examples demonstrated.

Reading between the lines

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

  • If the single-exponential ansatz is exact, the same Laplace-domain algebraic inversion could be extended to multi-exponential kernels by adding more integration intervals and solving for additional relaxation times, providing a built-in shape test.
  • The slab thickness Δ is a free parameter; a natural convergence check is to verify that ξ0 and Rk are independent of Δ while Δ lies within the first density peak, which the current paper does not report.
  • Method-3 uses four quadrature points in Laplace space; shifting the integration intervals s1..s4 would reveal how sensitive the result is to the shape of the memory kernel and to the numerical Laplace transform.
  • The nonequilibrium agreement is shown at one field strength; comparing at several field strengths and temperatures would confirm the linear-response regime and sharpen the validity of the exponential ansatz.
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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 / 5 minor

Summary. The paper presents NAVIS, a LAMMPS-Python toolkit that implements the authors' previously developed equilibrium-MD methods for computing the Navier friction coefficient and the Kapitza resistance at solid-fluid interfaces. The manuscript gives a condensed account of the linear-response theory, provides LAMMPS input snippets and Python post-processing algorithms, and illustrates the workflow on a water-graphene system (hydrodynamic slip) and a water-CNT system (thermal slip). The central advertised claim is that the 'Method-3' variant, together with the analogous thermal method, yields interfacial transport coefficients that are statistically stable across correlation lag times and avoids the Green-Kubo plateau/divergence problem. The figures are explicitly stated to be reproduced from the authors' earlier papers.

Significance. If the robustness claims hold, NAVIS would be a useful community resource: it packages a linear-response EMD protocol for extracting intrinsic interfacial friction and thermal resistance from a single equilibrium simulation with wall thermostatting, avoiding NEMD extrapolation issues. The open-source GPL release, the explicit LAMMPS snippets, and the step-by-step algorithms are strengths. However, the evidence presented in this manuscript is weaker than the advertised central claim: the figures are reprints from prior studies, no test is shown that the released code reproduces those figures, and no test is provided for the single-exponential memory-kernel ansatz on which Methods 2 and 3 rely. The claimed robustness of Method-3 may be an artifact of that assumed functional form rather than a generic property of the estimator. With sensitivity tests and a reproducible demo, the paper could become a solid software contribution.

major comments (3)
  1. [Secs. 2.1, 2.2, 4.1] Eqs. (4) and (10) assume the friction and thermal memory kernels are exactly one-term Maxwellians, ζ(t)=B1 e^{-λ1 t} and G_k(t)=k1 e^{-μ1 t}. Methods 2 and 3, and the analog for the Kapitza resistance, all depend on this form: Q(s) ≡ -C~uxux(s)/C~uxFx(s) is then exactly linear in s, and Method-3 extrapolates Q(s) to s=0 using finite-s intervals (chosen in Sec. 4.1 as s1=0, s2=s3=0.5, s4=1). If the true memory kernel has additional relaxation modes, Q(s) is nonlinear and the extracted ξ0 = B1/(A λ1) and Rk = μ1/k1 are biased. The manuscript provides no diagnostic for the ansatz, no convergence check, no sensitivity study of the slab thickness Δ, and no model-free comparison within this work. The advertised robustness of Method-3 in Fig. 3(b,c) could therefore reflect the rigid fitting form rather than the underlying physics. Please add a quantitative test, e.g., fit a two-exponential kern
  2. [Secs. 4, Algorithms 2-5] The paper is a software contribution, but the validation does not exercise the released code. Section 4 states that the figures 'have been reproduced using data from our previous studies,' and Figs. 3(d) and 4 are reprinted. No output of the NAVIS code is shown, no sample input/output files or unit tests are provided, and no check that the Python implementation reproduces the printed correlation functions is reported. A reader cannot verify that the GitHub implementation corresponds to the described Algorithms 2-5. I request a reproducible test case (input files, run commands, expected numeric values for ξ0 and Rk) and a demonstration that the code reproduces at least one of the reprinted figures.
  3. [Secs. 4.1, 4.2, Algorithm 2] The generality claims rest on a single system per property — water-graphene for friction and water-CNT for Kapitza — and several free parameters are chosen without sensitivity analysis. The interfacial slab thickness Δ is set to 3.165 Å in both cases but its influence on the extracted coefficients is not investigated, despite Δ defining the interfacial region over which the correlation functions are computed. Likewise, Algorithm 2 introduces no_of_sets (the number of independent correlation blocks) as a free parameter, and the paper gives no guidance on how the results depend on it. Since these parameters directly affect the correlation data entering Eqs. (3) and (11), the claim that the method yields an intrinsic interfacial property is not fully established. Please add a sensitivity study for Δ and no_of_sets, and state the criterion used to select them.
minor comments (5)
  1. [Throughout] There are several typos and spelling inconsistencies: 'Kaptiza' in the Section 2.2 heading, 'noticable' and 'nanometeric' in the Introduction, 'analagous' in Section 2.2, and the title is rendered 'NA VIS' in the abstract but 'NAVIS' elsewhere.
  2. [References] Refs. 31 and 49 are the same Barrat-Chiaruttini article; Ref. 48 (Puech et al.) is not the source of the Green-Kubo method attributed to Barrat and Chiaruttini in Sec. 4.2. The reference list should be cleaned up.
  3. [Program Summary] The GitHub link is broken across lines ('https://github.com/sleebaslv/ NAVIS') and no version or commit hash is given. Please provide a single clickable URL and a version identifier so that the deposited code is reproducible.
  4. [Listing 2 / Sec. 3.2] The relationship between the LAMMPS quantities computed in Listing 2 (vz times the z-force tally) and the heat flux J_q used in Eq. (9) is not explained. Please give the explicit definition of J_q used, or point to the exact equation in the referenced prior work, so that users can verify they are computing the intended correlation function.
  5. [Sec. 4] The word 'efficient' in the title is not quantified. No runtime or performance benchmark is reported; a brief wall-clock time for the two example systems would help users gauge the cost of the EMD approach.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: EMD estimators are standard correlation-function estimators; the one-term Maxwellian is an explicit model assumption (a correctness risk), and inherited validation is externally benchmarked by NEMD.

full rationale

The derivation starts from explicitly stated linear-response relations (Eq. 5 and Eq. 9) and extracts zero-frequency coefficients by fitting two memory-kernel parameters (B1 and lambda1, or k1 and mu1) to equilibrium time-correlation data; xi0 is then formed as B1/(A*lambda1), not as a returned fit parameter itself. No target quantity is inserted into the estimator, so this is not a fitted input called a prediction. The one-term Maxwellian forms in Eq. 4 and Eq. 10 are openly assumed rather than implied by the target result; if the true memory kernel has additional relaxation modes, Methods 2 and 3 will be biased, but that is a model-validity concern rather than circularity. The manuscript is admittedly an implementation of the authors' prior methods and reprints validation figures from Varghese et al. 2021 and Alosious et al. 2021. Those self-citations are load-bearing for the toolkit's validation, but the cited works contain independent NEMD comparisons, so the support is external rather than reducible to the present paper's assumptions. The Laplace-estimator steps in the algorithms are standard numerical transforms and algebraic manipulations, not the reintroduction of the quantity being computed. Overall, no step exhibits a quantity that is equivalent by construction to an input; the score of 2 reflects the substantial but externally grounded self-citation. The open concerns about the one-term Maxwellian ansatz and slab-thickness sensitivity belong to correctness risk, not circularity.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The paper's computation rests on previously established linear-response formulas, a Maxwellian kernel ansatz, and several user-chosen numerical parameters (slab thickness, Laplace integration limits, block count). The theory is adopted from the authors' earlier papers rather than re-derived here, so the ledger reflects what is assumed rather than newly demonstrated.

free parameters (3)
  • Interfacial slab thickness Δ = 3.165 Å (water layer adjacent to wall; both planar and CNT geometries)
    The correlations defining friction and Kapitza resistance are computed only over this slab; the reported 'intrinsic' values depend on this user-specified width. Section 4.2 states the slab thickness is 3.165 Å, similar to the graphene-water system.
  • Laplace integration limits for Method-3 (s1, s2, s3, s4) = (0, 0.5, 0.5, 1) in reduced units
    Used in Eqs (6)-(8) to determine λ1 and B1. Chosen without optimality or sensitivity analysis in Section 4.1; on finite data the estimated friction coefficient is sensitive to this choice.
  • Number of independent correlation blocks (no_of_sets) = not specified in text
    Algorithm 2 splits the time series into no_of_sets blocks; the reported standard errors depend on this user choice. The paper recommends no value and shows no sensitivity.
assumptions (5)
  • ad hoc to paper The friction memory kernel has a one-term Maxwellian form ζ(t)=B1 e^{-λ1 t}.
    Adopted in Section 2.1 Eq. (4); no justification or check that the real kernel is exponential. Method-2/Method-3 rely on this functional form.
  • ad hoc to paper The thermal conductance kernel has a one-term Maxwellian form G_k(t)=k1 e^{-μ1 t}.
    Section 2.2 Eq. (10); borrowed from Ref [33]. Same issue as the friction kernel: no test of exponentiality.
  • domain assumption Friction and Kapitza resistance are local interfacial properties captured by a thin slab adjacent to the wall; contributions from bulk fluid can be neglected.
    Central to the method; invoked in Sections 2 and 3.2. No convergence test with varying slab thickness is provided.
  • domain assumption Wall thermostatting maintains the appropriate equilibrium ensemble for confined fluids.
    Section 3.2, citing Refs [44-46]. If the thermostatting strategy affects interfacial fluctuations, computed coefficients could be altered.
  • standard math Linear response (Green-Kubo) theory connects equilibrium correlation functions to transport coefficients.
    Background used throughout Section 2; from standard statistical mechanics and the authors' prior derivations.

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Cite this review

Pith. "Pith review of NAVIS: A LAMMPS-Python framework for efficient computation of nanochannel velocity and thermal interfacial slip." pith.science (2026). https://pith.science/paper/YJDZH4QS

@misc{pith2026260111391,
  author       = {Pith},
  title        = {Pith review of: NAVIS: A LAMMPS-Python framework for efficient computation of nanochannel velocity and thermal interfacial slip},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YJDZH4QS}},
  note         = {Machine review of arXiv:2601.11391}
}
read the original abstract

We present NAVIS (NAnochannel Velocity and thermal Interfacial Slip), a LAMMPS-Python scripted toolkit for computing the Navier (hydrodynamic) friction coefficient and Kapitza (thermal) resistance at arbitrary solid-fluid interfaces. NAVIS is based on equilibrium molecular dynamics (EMD) methods for calculating the linear response friction and thermal resistance at the interface, as well as the corresponding velocity and temperature slips. The methodology is based on our previous studies (Hansen, et al., Phys. Rev. E 84, 016313 (2011); Varghese et al., J. Chem. Phys. 154, 184707 (2021); Alosious, et al., J. Chem. Phys. 151, 194502 (2019); Alosious, et al., Langmuir 37, 2355-2361 (2021)), and in this work we provide a pedagogical framework for the implementation of this toolkit on two systems: (i) a water-graphene system (for hydrodynamic slip) and (ii) a water-CNT system (for thermal slip). We provide detailed instructions for performing the EMD simulations using the LAMMPS package and processing the simulation outputs using Python modules to obtain the desired quantities of interest. We expect the toolkit to be useful for computational researchers studying interfacial friction and thermal transport, key factors for efficient and practical applications of nanofluidic systems.

Figures

Figures reproduced from arXiv: 2601.11391 by the authors.

Figure 1
Figure 1. A 2-D schematic diagram of the fluid molecules (depicted as solid [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Flowchart outlining the steps involved in the computation of the [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. For a water-graphene system: (a) Normalized time correlation func [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: (a) Comparison of the Kapitza resistance obtained from the NEMD [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

58 extracted references

  1. [16]

    Varghese, J

    S. Varghese, J. S. Hansen, B. D. Todd, Improved method- ology to compute the intrinsic friction coefficient at solid– liquid interfaces, J. Chem. Phys. 154 (18) (2021) 184707

    2021

  2. [28]

    Theory of simple liquids, Academic Press, 2013

    2013

  3. [5]

    A. Sam, R. Hartkamp, S. Kumar Kannam, J. S. Babu, S. P. Sathian, P. J. Daivis, B. Todd, Fast transport of water in carbon nanotubes: a review of current accomplishments and challenges, Mol. Simul. 47 (10-11) (2021) 905–924

    2021

  4. [1]

    Holmberg, A

    K. Holmberg, A. Erdemir, The impact of tribology on en- ergy use and co2 emission globally and in combustion en- gine and electric cars, Tribol. Int. 135 (2019) 389–396

    2019

  5. [3]

    Bocquet, E

    L. Bocquet, E. Charlaix, Nanofluidics, from bulk to inter- faces, Chem. Soc. Rev. 39 (3) (2010) 1073–1095

    2010

  6. [4]

    Ajdari, L

    A. Ajdari, L. Bocquet, Giant amplification of interfacially driven transport by hydrodynamic slip: Diffusio-osmosis and beyond, Phys. Rev. Lett. 96 (18) (2006) 186102

    2006

  7. [6]

    G. L. Pollack, Kapitza resistance, Rev. Mod. Phys. 41 (1) (1969) 48

    1969

  8. [7]

    J. Chen, X. Xu, J. Zhou, B. Li, Interfacial thermal resis- tance: Past, present, and future, Rev. Mod. Phys. 94 (2) (2022) 025002

    2022

Show all 58 references
  1. [8]

    B. D. Todd, P. J. Daivis, Nonequilibrium Molecular Dy- namics: Theory, Algorithms and Applications, Cam- bridge University Press, 2017

    2017

  2. [9]

    Kumar Kannam, B

    S. Kumar Kannam, B. D. Todd, J. S. Hansen, P. J. Daivis, Slip length of water on graphene: Limitations of non- equilibrium molecular dynamics simulations, J. Chem. Phys. 136 (2) (2012) 024705

    2012

  3. [10]

    S. K. Kannam, B. D. Todd, J. S. Hansen, P. J. Daivis, How fast does water flow in carbon nanotubes?, J. Chem. Phys. 138 (9) (2013) 094701

    2013

  4. [11]

    Bernardi, S

    S. Bernardi, S. J. Brookes, D. J. Searles, D. J. Evans, Response theory for confined systems, J. Chem. Phys. 137 (7) (2012) 074114. 9

    2012

  5. [12]

    Bernardi, D

    S. Bernardi, D. J. Searles, Local response in nanopores, Mol. Simul. 42 (6-7) (2016) 463–473

    2016

  6. [13]

    Maffioli, E

    L. Maffioli, E. R. Smith, J. P. Ewen, P. J. Daivis, D. Dini, B. D. Todd, Slip and stress from low shear rate nonequi- librium molecular dynamics: The transient-time correla- tion function technique, J. Chem. Phys. 156 (18) (2022) 184111

    2022

  7. [14]

    Maffioli, J

    L. Maffioli, J. P. Ewen, E. R. Smith, S. Varghese, P. J. Daivis, D. Dini, B. Todd, Ttcf4lammps: A toolkit for simulation of the non-equilibrium behaviour of molecular fluids at experimentally accessible shear rates, Comput. Phys. Commun. (2024) 109205

    2024

  8. [15]

    Bocquet, J.-L

    L. Bocquet, J.-L. Barrat, Hydrodynamic boundary condi- tions, correlation functions, and kubo relations for con- fined fluids, Phys. Rev. E 49 (1994) 3079–3092

    1994

  9. [17]

    Espanol, I

    P. Espanol, I. Zuniga, Force autocorrelation function in brownian motion theory, J. Chem. Phys. 98 (1) (1993) 574–580

    1993

  10. [18]

    D. J. Evans, G. P. Morriss, Statistical mechanics of nonequilibrium liquids, Academic Press, 1990

    1990

  11. [19]

    Petravic, P

    J. Petravic, P. Harrowell, On the equilibrium calculation of the friction coefficient for liquid slip against a wall, J. Chem. Phys. 127 (17) (2007) 174706

    2007

  12. [20]

    J. Brey, J. G. Ordónez, Computer studies of brownian mo- tion in a lennard-jones fluid: The stokes law, J. Chem. Phys. 76 (6) (1982) 3260–3263

    1982

  13. [21]

    Bocquet, J.-L

    L. Bocquet, J.-L. Barrat, On the green-kubo relationship for the liquid-solid friction coefficient, J. Chem. Phys. 139 (4) (2013)

    2013

  14. [22]

    Huang, I

    K. Huang, I. Szlufarska, Green-kubo relation for friction at liquid-solid interfaces, Phys. Rev. E 89 (2014) 032119

    2014

  15. [23]

    H. Oga, Y . Yamaguchi, T. Omori, S. Merabia, L. Joly, Green-kubo measurement of liquid-solid friction in finite- size systems, J. Chem. Phys. 151 (5) (2019) 054502

    2019

  16. [24]

    Español, J

    P. Español, J. A. de la Torre, D. Duque-Zumajo, Solution to the plateau problem in the green-kubo formula, Phys. Rev. E 99 (2019) 022126

    2019

  17. [25]

    Nakano, S.-i

    H. Nakano, S.-i. Sasa, Statistical mechanical expressions of slip length, J. Stat. Phys. 176 (2019) 312–357

    2019

  18. [26]

    Nakano, S.-i

    H. Nakano, S.-i. Sasa, Equilibrium measurement method of slip length based on fluctuating hydrodynamics, Phys. Rev. E 101 (2020) 033109

    2020

  19. [27]

    H. Oga, T. Omori, L. Joly, Y . Yamaguchi, Equilibrium molecular dynamics evaluation of the solid–liquid friction coefficient: Role of timescales, J. Chem. Phys. 159 (2) (2023) 024701

    2023

  20. [29]

    On the equilibrium calculation of the friction coefficient for liquid slip against a wall

    J. Petravic, P. Harrowell, Erratum: “On the equilibrium calculation of the friction coefficient for liquid slip against a wall” [J. Chem. Phys. 127, 174706 (2007)], J. Chem. Phys. 128 (20) (2008) 209901

    2007

  21. [30]

    J. S. Hansen, B. D. Todd, P. J. Daivis, Prediction of fluid velocity slip at solid surfaces, Phys. Rev. E 84 (2011) 016313

    2011

  22. [31]

    Barrat, F

    J.-L. Barrat, F. Chiaruttini, Kapitza resistance at the liquid–solid interface, Mol. Phys. 101 (11) (2003) 1605– 1610

    2003

  23. [32]

    Alosious, S

    S. Alosious, S. K. Kannam, S. P. Sathian, B. D. Todd, Prediction of kapitza resistance at fluid-solid interfaces., J. Chem. Phys. 151 (19) (2019) 194502

    2019

  24. [33]

    Alosious, S

    S. Alosious, S. K. Kannam, S. P. Sathian, B. D. Todd, Nanoconfinement effects on the kapitza resistance at water-cnt interfaces., Langmuir 37 (7) (2021) 2355–2361

    2021

  25. [34]

    Python 3.12.2 documentation

  26. [35]

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

    2022

  27. [36]

    Alosious, S

    S. Alosious, S. K. Kannam, S. P. Sathian, B. D. Todd, Kapitza resistance at water–graphene interfaces, J. Chem. Phys. 152 (22) (2020) 224703

    2020

  28. [37]

    Alosious, S

    S. Alosious, S. K. Kannam, S. P. Sathian, B. D. Todd, Effects of electrostatic interactions on kapitza resistance in hexagonal boron nitride–water interfaces, Langmuir 38 (29) (2022) 8783–8793

    2022

  29. [38]

    S. K. Kannam, B. D. Todd, J. S. Hansen, P. J. Daivis, Slip flow in graphene nanochannels, J. Chem. Phys. 135 (14) (2011) 144701

    2011

  30. [39]

    Varghese, B

    S. Varghese, B. D. Todd, J. S. Hansen, Existence of a max- imum flow rate in electro-osmotic systems, J. Chem. Phys. 161 (19) (2024)

    2024

  31. [40]

    S. K. Kannam, B. Todd, J. S. Hansen, P. J. Daivis, Inter- facial slip friction at a fluid-solid cylindrical boundary, J. Chem. Phys. 136 (24) (2012)

    2012

  32. [41]

    Frenkel, B

    D. Frenkel, B. Smit, Understanding molecular simulation, Elsevier, 1957. 10

    1957

  33. [42]

    M. P. Allen, D. J. Tildesley, Computer simulation of liq- uids, Oxford university press, 2017

    2017

  34. [43]

    A. P. Thompson, H. M. Aktulga, R. Berger, D. S. Bolin- tineanu, W. M. Brown, P. S. Crozier, P. J. In’t Veld, A. Kohlmeyer, S. G. Moore, T. D. Nguyen, et al., Lammps-a flexible simulation tool for particle-based ma- terials modeling at the atomic, meso, and continuum scales, Co...

    2022

  35. [44]

    Bernardi, B

    S. Bernardi, B. Todd, D. J. Searles, Thermostating highly confined fluids, J. Chem. Phys. 132 (24) (2010)

    2010

  36. [45]

    De Luca, B

    S. De Luca, B. Todd, J. S. Hansen, P. J. Daivis, A new and effective method for thermostatting confined fluids, J. Chem. Phys. 140 (5) (2014)

    2014

  37. [46]

    A. Sam, S. K. Kannam, R. Hartkamp, S. P. Sathian, Water flow in carbon nanotubes: The effect of tube flexibility and thermostat, J. Chem. Phys. 146 (23) (2017)

    2017

  38. [47]

    Español, J

    P. Español, J. de la Torre, D. Duque-Zumajo, Solution to the plateau problem in the green-kubo formula, Phys. Rev. E 99 (2) (2019) 022126

    2019

  39. [48]

    Puech, G

    R. Puech, G. Bonfait, B. Castaing, Mobility of the 3he solid-liquid interface: Experiment and theory, J. Low Temp. Phys. 62 (1986) 315

    1986

  40. [49]

    Barrat, F

    J.-L. Barrat, F. Chiaruttini, Kapitza resistance at the liquid-solid interface, Mol. Phys. 101 (11) (2003) 1605– 1610

    2003

  41. [50]

    J. S. Hansen, Nanoscale Hydrodynamics of Simple Sys- tems, Cambridge University Press, Cambridge, 2022

    2022

  42. [51]

    Tersoff, Modeling solid-state chemistry: Interatomic potentials for multicomponent systems, Phys

    J. Tersoff, Modeling solid-state chemistry: Interatomic potentials for multicomponent systems, Phys. Rev. B 39 (8) (1989) 5566

    1989

  43. [52]

    Lindsay, D

    L. Lindsay, D. Broido, Optimized tersoffand bren- ner empirical potential parameters for lattice dynamics and phonon thermal transport in carbon nanotubes and graphene, Phys. Rev. B 81 (20) (2010) 205441

    2010

  44. [53]

    Berendsen, J

    H. Berendsen, J. Grigera, T. Straatsma, The missing term in effective pair potentials, J. Phys. Chem. 91 (24) (1987) 6269–6271

    1987

  45. [54]

    Y . Wu, H. L. Tepper, G. A. V oth, Flexible simple point- charge water model with improved liquid-state properties, J. Chem. Phys. 124 (2) (2006) 024503

    2006

  46. [55]

    Ryckaert, G

    J.-P. Ryckaert, G. Ciccotti, H. J. Berendsen, Numerical integration of the cartesian equations of motion of a sys- tem with constraints: molecular dynamics of n-alkanes, J. Comput. Phys. 23 (3) (1977) 327–341

    1977

  47. [56]

    Werder, J

    T. Werder, J. H. Walther, R. Jaffe, T. Halicioglu, P. Koumoutsakos, On the water- carbon interaction for use in molecular dynamics simulations of graphite and carbon nanotubes, J. Phys. Chem. B 107 (6) (2003) 1345–1352

    2003

  48. [57]

    R. W. Hockney, J. W. Eastwood, Computer simulation us- ing particles, crc Press, 2021

    2021

  49. [58]

    I.-C. Yeh, M. L. Berkowitz, Ewald summation for systems with slab geometry, J. Chem. Phys. 111 (7) (1999) 3155– 3162

    1999

  50. [59]

    W. C. Swope, H. C. Andersen, P. H. Berens, K. R. Wilson, A computer simulation method for the calculation of equi- librium constants for the formation of physical clusters of molecules: Application to small water clusters, J. Chem. Phys. 76 (1) (1982) 637–649. 11

    1982

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