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arxiv: 2512.21490 · v1 · submitted 2025-12-25 · ⚛️ physics.chem-ph

Thermal conductivities of monolayer graphene oxide from machine learning molecular dynamics simulations

Bohan Zhang , Biyuan Liu , Penghua Ying , Zherui Chen , Yanzhou Wang , Yonglin Zhang , Haikuan Dong , Jinglei Yang
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Zheyong Fan
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Pith reviewed 2026-05-16 20:04 UTC · model grok-4.3

classification ⚛️ physics.chem-ph
keywords graphene oxidethermal conductivitymachine learningmolecular dynamicsneuroevolution potentialthermal reductionheat transport2D materials
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The pith

Reduced graphene oxide shows thermal conductivities of only a few to tens of W m^{-1} K^{-1}, far below pristine graphene.

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

This paper uses a machine-learned neuroevolution potential to simulate the thermal properties of monolayer graphene oxide at various reduction states. The simulations reveal that reduced GO has strongly suppressed thermal conductivities compared to defect-free GO and graphene. Conductivity increases moderately with higher OH/O ratios but decreases with higher O/C ratios, except at the highest oxidation level. These trends link to the recovery of graphene-like structures in the material. The work offers a computational way to connect chemical structure to heat transport in these heterogeneous carbon materials.

Core claim

Through large-scale molecular dynamics simulations enabled by a neuroevolution potential trained on density functional theory data, the study establishes that thermally reduced monolayer graphene oxide exhibits thermal conductivities ranging from a few to tens of Wm^{-1}K^{-1}. This is substantially lower than pristine GO without defects and far below graphene. The thermal conductivity increases moderately with increasing OH/O ratio, except at O/C=0.5 where the trend inverts, and decreases significantly with increasing O/C ratio, a trend strongly correlated with the fraction of recovered graphene-like structures.

What carries the argument

A neuroevolution potential (NEP) trained on an existing DFT dataset, used with homogeneous nonequilibrium MD and quantum-statistical corrections to compute thermal conductivities across oxidation levels.

Load-bearing premise

The neuroevolution potential trained on the DFT dataset accurately reproduces the thermal transport properties for all oxidation and reduction states examined, including the quantum correction.

What would settle it

Experimental measurements of thermal conductivity on reduced graphene oxide samples with controlled O/C ratios from 0.1 to 0.5 and varying OH/O ratios would directly test the predicted values and trends.

Figures

Figures reproduced from arXiv: 2512.21490 by Biyuan Liu, Bohan Zhang, Haikuan Dong, Jinglei Yang, Penghua Ying, Yanzhou Wang, Yonglin Zhang, Zherui Chen, Zheyong Fan.

Figure 2
Figure 2. Figure 2: FIG. 2 [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIG. 3 [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: FIG. 4 [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: FIG. 5 [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: FIG. 6 [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: FIG. 8 [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗
read the original abstract

Graphene oxide (GO) exhibits rich chemical heterogeneity that strongly influences its structural, thermal, and mechanical properties, yet quantitatively linking reduction chemistry to heat transport remains challenging. In this work, we develop a machine-learned neuroevolution potential (NEP) trained on an existing density functional theory dataset (\textit{Angew.\ Chem.\ Int.\ Ed.}, \textbf{63} , e202410088 (2024)), achieving reasonable accuracy at a computational cost much lower than the existing machine-learned and empirical potentials. Leveraging this potential, we perform large-scale molecular dynamics (MD) simulations to model the thermal reduction of GO across realistic structural domains. Using the homogeneous nonequilibrium MD method with a proper quantum-statistical correction scheme, we find that reduced GO exhibits strongly suppressed thermal conductivities, ranging from a few to tens of Wm$^{-1}$K$^{-1}$, substantially lower than pristine GO without defects and far below graphene. Moreover, the thermal conductivity of reduced GO increases moderately with increasing OH/O ratio, except at the highest oxidation level (O/C=0.5) where this trend inverts, while decreasing significantly with increasing O/C ratio, a trend strongly correlated with the fraction of recovered graphene-like structures. Our work provides a computationally tractable and predictive atomistic machine learning framework for exploring how chemical structure governs heat transport in heterogeneous carbon materials.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript develops a neuroevolution potential (NEP) trained on an existing DFT dataset for graphene oxide (GO). Using this potential in large-scale molecular dynamics simulations with the homogeneous nonequilibrium MD (HNEMD) method and a quantum-statistical correction, the authors compute thermal conductivities for reduced GO across various oxidation levels. They report that reduced GO has strongly suppressed thermal conductivities (a few to tens of W m^{-1} K^{-1}), much lower than pristine graphene or defect-free GO, with conductivity increasing moderately with OH/O ratio (except inverting at O/C=0.5) and decreasing with O/C ratio, correlated with recovered graphene-like structures.

Significance. If the NEP is shown to accurately predict thermal transport, the work supplies a computationally efficient framework for connecting reduction chemistry to heat conduction in chemically heterogeneous 2D carbon materials. The reported trends versus OH/O and O/C ratios, together with the correlation to graphene-like domain recovery, would be of interest for materials design where direct experiment is difficult.

major comments (2)
  1. Abstract: The central quantitative claims (kappa reduced to a few–tens of W m^{-1} K^{-1} and the specific trends with OH/O and O/C) rest on the NEP reproducing phonon spectra, anharmonic scattering rates, and defect suppression across oxidation states. No direct validation of predicted thermal conductivities against experimental GO data or independent ab initio MD/HNEMD results for the same compositions is provided, leaving the accuracy of the reported values and trends unconfirmed.
  2. Methods (NEP training and HNEMD section): The quantum-statistical correction is applied to classical HNEMD trajectories under the assumption that the NEP force field already yields correct mode-specific heat capacities and lifetimes. Without benchmarks demonstrating that the NEP matches DFT-level thermal transport properties over the full range of O/C and OH/O ratios studied, systematic errors in the potential propagate directly into the reported trends and the inversion at O/C=0.5.
minor comments (2)
  1. Abstract: The baseline comparison to 'pristine GO without defects' should be defined more precisely (e.g., specific defect density or structure) when first introduced in the results.
  2. Abstract: The phrase 'a proper quantum-statistical correction scheme' would benefit from a short parenthetical description or citation to improve immediate clarity for readers.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the careful and constructive review of our manuscript. We address the major comments point by point below, providing the strongest honest defense of our work while acknowledging limitations. Revisions have been made to improve clarity and discussion of assumptions where feasible.

read point-by-point responses

  1. Referee: Abstract: The central quantitative claims (kappa reduced to a few–tens of W m^{-1} K^{-1} and the specific trends with OH/O and O/C) rest on the NEP reproducing phonon spectra, anharmonic scattering rates, and defect suppression across oxidation states. No direct validation of predicted thermal conductivities against experimental GO data or independent ab initio MD/HNEMD results for the same compositions is provided, leaving the accuracy of the reported values and trends unconfirmed.

    Authors: We acknowledge the absence of direct experimental or independent ab initio thermal conductivity benchmarks for the exact compositions studied, which is a genuine limitation given the scarcity of such data for well-characterized reduced GO samples and the prohibitive cost of ab initio MD for large systems. The NEP was trained on a comprehensive DFT dataset and validated for energies, forces, and structural properties, with additional phonon spectra comparisons now added to the SI for representative structures. The reported trends are robustly correlated with the fraction of recovered graphene-like domains, which emerges directly from the simulations and aligns with known physics of defect scattering in 2D carbons. In revision, we have modified the abstract and added a dedicated paragraph in the discussion section to explicitly state the predictive nature of the results, cite analogous MLMD thermal transport studies, and note the lack of direct benchmarks as a caveat. revision: partial

  2. Referee: Methods (NEP training and HNEMD section): The quantum-statistical correction is applied to classical HNEMD trajectories under the assumption that the NEP force field already yields correct mode-specific heat capacities and lifetimes. Without benchmarks demonstrating that the NEP matches DFT-level thermal transport properties over the full range of O/C and OH/O ratios studied, systematic errors in the potential propagate directly into the reported trends and the inversion at O/C=0.5.

    Authors: The quantum-statistical correction follows established protocols in the literature for classical MD thermal conductivity calculations. We have validated the NEP against DFT phonon dispersions for multiple GO compositions, supporting the underlying vibrational properties. Full ab initio HNEMD benchmarks across all O/C and OH/O ratios are not feasible with current resources for the system sizes required for convergence. We agree this leaves room for potential systematic bias and have revised the methods section to expand on the correction scheme, include error discussion, and provide additional justification for the inversion at O/C=0.5 based on structural metrics (graphene-like domain fraction) that are directly computed and less sensitive to the potential details. revision: partial

standing simulated objections not resolved
  • Direct ab initio MD or HNEMD benchmarks of thermal conductivities for the full range of studied O/C and OH/O ratios, due to prohibitive computational expense for converged large-scale simulations

Circularity Check

0 steps flagged

No significant circularity; derivation chain is self-contained

full rationale

The paper trains an NEP on an external, pre-existing DFT dataset cited from Angew. Chem. Int. Ed. (2024), then runs forward MD simulations with HNEMD plus a quantum-statistical correction to obtain thermal conductivities and their trends versus OH/O and O/C ratios. No equation or step reduces the reported kappa values to fitted inputs by construction; the simulation outputs are generated from the potential rather than presupposed. The central claims about suppression to a few–tens of W m^{-1} K^{-1} and the specific monotonic trends emerge from the large-scale MD trajectories, not from re-labeling of training data. Any self-citations are peripheral and non-load-bearing for the conductivity results.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the transferability of the NEP to thermal transport and on the validity of the quantum correction; no free parameters are explicitly fitted to the conductivity data itself.

axioms (1)
  • domain assumption The neuroevolution potential trained on the 2024 DFT dataset reproduces phonon transport properties with sufficient accuracy for the oxidation levels studied.
    Invoked when using the potential for large-scale MD heat transport calculations.

pith-pipeline@v0.9.0 · 5574 in / 1167 out tokens · 21100 ms · 2026-05-16T20:04:21.046486+00:00 · methodology

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Reference graph

Works this paper leans on

64 extracted references · 64 canonical work pages

  1. [1]

    D. R. Dreyer, S. Park, C. W. Bielawski, and R. S. Ruoff, The chemistry of graphene oxide, Chem. Soc. Rev. 39, 228 (2010)

    work page 2010

  2. [2]

    C. K. Chua and M. Pumera, Chemical reduction of graphene oxide: a synthetic chemistry viewpoint, Chem. Soc. Rev. 43, 291 (2014)

    work page 2014

  3. [3]

    Y. Y. Khine, X. Wen, X. Jin, T. Foller, and R. Joshi, Functional groups in graphene oxide, Phys. Chem. Chem. Phys. 24, 26337 (2022)

    work page 2022

  4. [4]

    J. D. Renteria, S. Ramirez, H. Malekpour, B. Alonso, A. Centeno, A. Zurutuza, A. I. Cocemasov, D. L. Nika, and A. A. Balandin, Strongly anisotropic thermal con- ductivity of free-standing reduced graphene oxide films annealed at high temperature, Adv. Funct. Mater. 25, 4664 (2015)

    work page 2015

  5. [5]

    S. Jin, Q. Gao, X. Zeng, R. Zhang, K. Liu, X. Shao, and M. Jin, Effects of reduction methods on the structure and thermal conductivity of free-standing reduced graphene oxide films, Diam. Relat. Mater. 58, 54 (2015)

    work page 2015

  6. [6]

    A. C. Van Duin, S. Dasgupta, F. Lorant, and W. A. God- dard, ReaxFF: a reactive force field for hydrocarbons, The Journal of Physical Chemistry A 105, 9396 (2001)

    work page 2001

  7. [7]

    Zou, Z.-Q

    J.-H. Zou, Z.-Q. Ye, and B.-Y. Cao, Phonon thermal properties of graphene from molecular dynamics using different potentials, J. Chem. Phys. 145, 134705 (2016)

    work page 2016

  8. [8]

    C. Diao, Y. Dong, and J. Lin, Reactive force field simula- tion on thermal conductivities of carbon nanotubes and graphene, Int. J. Heat Mass Transfer 112, 903 (2017)

    work page 2017

  9. [9]

    Behler and M

    J. Behler and M. Parrinello, Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces, Phys. Rev. Lett. 98, 146401 (2007)

    work page 2007

  10. [10]

    A. P. Bartók, M. C. Payne, R. Kondor, and G. Csányi, Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons, Phys. Rev. Lett. 104, 136403 (2010)

    work page 2010

  11. [11]

    Batatia, D

    I. Batatia, D. P. Kovacs, G. Simm, C. Ortner, and G. Csányi, Mace: Higher order equivariant message pass- ing neural networks for fast and accurate force fields, Advances in neural information processing systems 35, 11423 (2022)

    work page 2022

  12. [12]

    El-Machachi, D

    Z. El-Machachi, D. Frantzov, A. Nijamudheen, T. Zarrouk, M. A. Caro, and V. L. Deringer, Ac- celerated first-principles exploration of structure and reactivity in graphene oxide, Angewandte Chemie International Edition 63, e202410088 (2024)

    work page 2024

  13. [13]

    H. Dong, Y. Shi, P. Ying, K. Xu, T. Liang, Y. Wang, Z. Zeng, X. Wu, W. Zhou, S. Xiong, S. Chen, and Z. Fan, Molecular dynamics simulations of heat transport using machine-learned potentials: A mini-review and tutorial on GPUMD with neuroevolution potentials, Journal of Applied Physics 135, 161101 (2024)

    work page 2024

  14. [14]

    Z. Fan, Z. Zeng, C. Zhang, Y. Wang, K. Song, H. Dong, Y. Chen, and T. Ala-Nissila, Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport, Phys. Rev. B 104, 104309 (2021)

    work page 2021

  15. [15]

    Z. Fan, Y. Wang, P. Ying, K. Song, J. Wang, Y. Wang, Z. Zeng, K. Xu, E. Lindgren, J. M. Rahm, A. J. Gabourie, J. Liu, H. Dong, J. Wu, Y. Chen, Z. Zhong, J. Sun, P. Erhart, Y. Su, and T. Ala-Nissila, GPUMD: A package for constructing accurate machine-learned po- tentials and performing highly efficient atomistic simu- lations, The Journal of Chemical Physic...

    work page 2022

  16. [16]

    K. Song, R. Zhao, J. Liu, Y. Wang, E. Lindgren, Y. Wang, S. Chen, K. Xu, T. Liang, P. Ying, N. Xu, Z. Zhao, J. Shi, J. Wang, S. Lyu, Z. Zeng, S. Liang, H. Dong, L. Sun, Y. Chen, Z. Zhang, W. Guo, P. Qian, J. Sun, P. Erhart, T. Ala-Nissila, Y. Su, and Z. Fan, General-purpose machine-learned potential for 16 ele- mental metals and their alloys, Nature Commu...

    work page 2024

  17. [17]

    P. Ying, C. Qian, R. Zhao, Y. Wang, K. Xu, F. Ding, S. Chen, and Z. Fan, Advances in modeling complex ma- terials: The rise of neuroevolution potentials, Chemical Physics Reviews 6, 011310 (2025)

    work page 2025

  18. [18]

    K. Xu, H. Bu, S. Pan, E. Lindgren, Y. Wu, Y. Wang, J. Liu, K. Song, B. Xu, Y. Li, T. Hainer, L. Svens- son, J. Wiktor, R. Zhao, H. Huang, C. Qian, S. Zhang, Z. Zeng, B. Zhang, B. Tang, Y. Xiao, Z. Yan, J. Shi, Z. Liang, J. Wang, T. Liang, S. Cao, Y. Wang, P. Ying, N. Xu, C. Chen, Y. Zhang, Z. Chen, X. Wu, W. Jiang, E. Berger, Y. Li, S. Chen, A. J. Gabouri...

    work page 2025

  19. [19]

    Schaul, T

    T. Schaul, T. Glasmachers, and J. Schmidhuber, High di- mensions and heavy tails for natural evolution strategies (Association for Computing Machinery, New York, NY, USA, 2011)

    work page 2011

  20. [20]

    Bussi and M

    G. Bussi and M. Parrinello, Accurate sampling using langevin dynamics, Phys. Rev. E 75, 056707 (2007)

    work page 2007

  21. [21]

    Z. Fan, H. Dong, A. Harju, and T. Ala-Nissila, Homo- geneous nonequilibrium molecular dynamics method for heat transport and spectral decomposition with many- body potentials, Phys. Rev. B 99, 064308 (2019)

    work page 2019

  22. [22]

    Bussi, D

    G. Bussi, D. Donadio, and M. Parrinello, Canonical sam- pling through velocity rescaling, The Journal of Chemical Physics 126, 014101 (2007)

    work page 2007

  23. [23]

    D. J. Evans, Homogeneous NEMD algorithm for ther- mal conductivity—Application of non-canonical linear response theory, Physics Letters A 91, 457 (1982)

    work page 1982

  24. [24]

    Z. Fan, L. F. C. Pereira, H.-Q. Wang, J.-C. Zheng, D. Donadio, and A. Harju, Force and heat current formu- las for many-body potentials in molecular dynamics sim- ulations with applications to thermal conductivity calcu- lations, Phys. Rev. B 92, 094301 (2015)

    work page 2015

  25. [25]

    Kowalik, C

    M. Kowalik, C. Ashraf, B. Damirchi, D. Akbarian, S. Ra- jabpour, and A. C. Van Duin, Atomistic scale analysis of the carbonization process for C/H/O/N-based poly- mers with the ReaxFF reactive force field, The Journal of Physical Chemistry B 123, 5357 (2019) . 11

    work page 2019

  26. [26]

    Golze, M

    D. Golze, M. Hirvensalo, P. Hernández-León, A. Aarva, J. Etula, T. Susi, P. Rinke, T. Laurila, and M. A. Caro, Accurate computational prediction of core-electron binding energies in carbon-based materials: A machine- learning model combining density-functional theory and gw, Chemistry of Materials 34, 6240 (2022)

    work page 2022

  27. [27]

    Valentini, V

    C. Valentini, V. Montes-García, P. A. Livio, T. Chudziak, J. Raya, A. Ciesielski, and P. Samorì, Tuning the electrical properties of graphene oxide through low-temperature thermal annealing, Nanoscale 15, 5743 (2023)

    work page 2023

  28. [28]

    D. P. Kovács, I. Batatia, E. S. Arany, and G. Csányi, Evaluation of the MACE force field architecture: From medicinal chemistry to materials science, The Journal of Chemical Physics 159, 044118 (2023)

    work page 2023

  29. [29]

    Hjorth Larsen, J

    A. Hjorth Larsen, J. Jørgen Mortensen, J. Blomqvist, I. E. Castelli, R. Christensen, M. Dułak, J. Friis, M. N. Groves, B. Hammer, C. Hargus, E. D. Hermes, P. C. Jennings, P. Bjerre Jensen, J. Kermode, J. R. Kitchin, E. Leonhard Kolsbjerg, J. Kubal, K. Kaasb- jerg, S. Lysgaard, J. Bergmann Maronsson, T. Max- son, T. Olsen, L. Pastewka, A. Peterson, C. Rost...

    work page 2017

  30. [30]

    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, and S. J. Plimpton, LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales, Comp. Phys. Comm. 271...

    work page 2022

  31. [31]

    P. M. Larsen, S. Schmidt, and J. Schiøtz, Robust struc- tural identification via polyhedral template matching, Modelling and Simulation in Materials Science and En- gineering 24, 055007 (2016)

    work page 2016

  32. [32]

    A. Stukowski, Visualization and analysis of atomistic simulation data with OVITO–the Open Visualization Tool, Modelling and simulation in materials science and engineering 18, 015012 (2009)

    work page 2009

  33. [33]

    El-Machachi, B

    Z. El-Machachi, B. Cheng, and V. L. Deringer, Mechan- ical properties of graphene oxide from machine-learning- driven simulations, Chemical Communications 61, 11405 (2025)

    work page 2025

  34. [34]

    P. Ying, W. Zhou, L. Svensson, E. Berger, E. Frans- son, F. Eriksson, K. Xu, T. Liang, J. Xu, B. Song, et al. , Highly efficient path-integral molecular dynamics simulations with GPUMD using neuroevolution poten- tials: Case studies on thermal properties of materials, The Journal of Chemical Physics 162, 064109 (2025)

    work page 2025

  35. [35]

    Z. Zeng, Z. Fan, M. Simoncelli, C. Chen, T. Liang, Y. Chen, G. Thornton, and B. Cheng, Lattice distor- tion leads to glassy thermal transport in crystalline cs3bi2i6cl3, Proceedings of the National Academy of Sci- ences 122, e2415664122 (2025)

    work page 2025

  36. [36]

    Y. Wang, Z. Fan, P. Qian, M. A. Caro, and T. Ala- Nissila, Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine learning molecular dynamics simulations, Physical Re- view B 107, 054303 (2023)

    work page 2023

  37. [37]

    Liang, P

    T. Liang, P. Ying, K. Xu, Z. Ye, C. Ling, Z. Fan, and J. Xu, Mechanisms of temperature-dependent ther- mal transport in amorphous silica from machine-learning molecular dynamics, Phys. Rev. B 108, 184203 (2023)

    work page 2023

  38. [38]

    Y. Wang, Z. Fan, P. Qian, M. A. Caro, and T. Ala- Nissila, Density dependence of thermal conductivity in nanoporous and amorphous carbon with machine-learned molecular dynamics, Phys. Rev. B 111, 094205 (2025)

    work page 2025

  39. [39]

    K. Xu, Y. Hao, T. Liang, P. Ying, J. Xu, J. Wu, and Z. Fan, Accurate prediction of heat conductivity of water by a neuroevolution potential, The Journal of Chemical Physics 158 (2023)

    work page 2023

  40. [40]

    K. Xu, T. Liang, N. Xu, P. Ying, S. Chen, N. Wei, J. Xu, and Z. Fan, NEP-MB-pol: A unified machine-learned framework for fast and accurate prediction of water’s thermodynamic and transport properties, npj Computa- tional Materials 11, 279 (2025)

    work page 2025

  41. [41]

    Z. Fan, P. Hirvonen, L. F. C. Pereira, M. M. Ervasti, K. R. Elder, D. Donadio, A. Harju, and T. Ala-Nissila, Bimodal Grain-Size Scaling of Thermal Transport in Polycrystalline Graphene from Large-Scale Molecular Dynamics Simulations, Nano Letters 17, 5919 (2017)

    work page 2017

  42. [42]

    Sääskilahti, J

    K. Sääskilahti, J. Oksanen, J. Tulkki, A. J. H. Mc- Gaughey, and S. Volz, Vibrational mean free paths and thermal conductivity of amorphous silicon from non- equilibrium molecular dynamics simulations, AIP Ad- vances 6, 121904 (2016)

    work page 2016

  43. [43]

    Lv and A

    W. Lv and A. Henry, Direct calculation of modal contri- butions to thermal conductivity via green–kubo modal analysis, New Journal of Physics 18, 013028 (2016)

    work page 2016

  44. [44]

    Schwamb, B

    T. Schwamb, B. R. Burg, N. C. Schirmer, and D. Poulikakos, An electrical method for the measure- ment of the thermal and electrical conductivity ofre- duced graphene oxide nanostructures, Nanotechnology 20, 405704 (2009)

    work page 2009

  45. [45]

    N. K. Mahanta and A. R. Abramson, Thermal conduc- tivity of graphene and graphene oxide nanoplatelets, in 13th intersociety conference on thermal and thermome- chanical phenomena in electronic systems (IEEE, 2012) pp. 1–6

    work page 2012

  46. [46]

    Zhang, A

    H. Zhang, A. F. Fonseca, and K. Cho, Tailoring thermal transport property of graphene through oxygen function- alization, The Journal of Physical Chemistry C 118, 1436 (2014)

    work page 2014

  47. [47]

    Lin and M

    S. Lin and M. J. Buehler, Thermal transport in mono- layer graphene oxide: Atomistic insights into phonon engineering through surface chemistry, Carbon 77, 351 (2014)

    work page 2014

  48. [48]

    X. Mu, X. Wu, T. Zhang, D. B. Go, and T. Luo, Thermal transport in graphene oxide – from ballistic extreme to amorphous limit, Scientific Reports 4, 3909 (2014)

    work page 2014

  49. [49]

    X. Xu, L. F. C. Pereira, Y. Wang, J. Wu, K. Zhang, X. Zhao, S. Bae, C. Tinh Bui, R. Xie, J. T. L. Thong, B. H. Hong, K. P. Loh, D. Donadio, B. Li, and B. Özy- ilmaz, Length-dependent thermal conductivity in sus- pended single-layer graphene, Nature Communications 5, 3689 (2014)

    work page 2014

  50. [50]

    X. Wu, W. Zhou, H. Dong, P. Ying, Y. Wang, B. Song, Z. Fan, and S. Xiong, Correcting force error-induced un- derestimation of lattice thermal conductivity in machine learning molecular dynamics, The Journal of Chemical Physics 161, 014103 (2024)

    work page 2024

  51. [51]

    Z. Fan, L. F. C. Pereira, P. Hirvonen, M. M. Ervasti, K. R. Elder, D. Donadio, T. Ala-Nissila, and A. Harju, Thermal conductivity decomposition in two-dimensional materials: Application to graphene, Phys. Rev. B 95, 12 144309 (2017)

    work page 2017

  52. [52]

    P. Ying, T. Liang, Y. Du, J. Zhang, X. Zeng, and Z. Zhong, Thermal transport in planar sp2-hybridized carbon allotropes: A comparative study of biphenylene network, pentaheptite and graphene, International Jour- nal of Heat and Mass Transfer 183, 122060 (2022)

    work page 2022

  53. [53]

    P. Ying, T. Liang, K. Xu, J. Xu, Z. Fan, T. Ala-Nissila, and Z. Zhong, Variable thermal transport in black, blue, and violet phosphorene from extensive atomistic simula- tions with a neuroevolution potential, International Jour- nal of Heat and Mass Transfer 202, 123681 (2023)

    work page 2023

  54. [54]

    Jiang, H

    W. Jiang, H. Bu, T. Liang, P. Ying, Z. Fan, J. Xu, and W. Ouyang, Accurate modeling of interfacial ther- mal transport in van der waals heterostructures via hy- brid machine learning and registry-dependent potentials (2025), arXiv:2505.00376 [physics.comp-ph]

    work page arXiv 2025

  55. [55]

    X. Yang, X. Zheng, Q. Liu, T. Zhang, Y. Bai, Z. Yang, H. Chen, and M. Liu, Experimental study on thermal conductivity and rectification in suspended monolayer mos2, ACS Applied Materials & Interfaces 12, 28306 (2020)

    work page 2020

  56. [56]

    Q. Li, H. Dong, P. Ying, and Z. Fan, Anisotropic and isotropic elasticity and thermal transport in monolayer c24 networks from machine-learning molecular dynamics (2025), arXiv:2512.00362 [cond-mat.mtrl-sci]

    work page arXiv 2025

  57. [57]

    H. Dong, C. Cao, P. Ying, Z. Fan, P. Qian, and Y. Su, Anisotropic and high thermal conductivity in monolayer quasi-hexagonal fullerene: A comparative study against bulk phase fullerene, International Journal of Heat and Mass Transfer 206, 123943 (2023)

    work page 2023

  58. [58]

    Z. Tan, S. Wang, Y. Liu, Y. Xiao, X. Zhou, S. Zhou, X. Xiu, and H. Dong, Coherent and incoherent phonon transport in graphene/h-bn superlattice: A machine learning potential, Physica E: Low-dimensional Systems and Nanostructures 172, 116259 (2025)

    work page 2025

  59. [59]

    Q. Cai, D. Scullion, W. Gan, A. Falin, S. Zhang, K. Watanabe, T. Taniguchi, Y. Chen, E. J. G. Santos, and L. H. Li, High thermal conductivity of high-quality monolayer boron nitride and its thermal expansion, Sci- ence Advances 5, eaav0129 (2019)

    work page 2019

  60. [60]

    X. Yang, Z. Dai, Y. Zhao, and S. Meng, Phonon ther- mal transport in a class of graphene allotropes from first principles, Phys. Chem. Chem. Phys. 20, 15980 (2018)

    work page 2018

  61. [61]

    H. P. Veeravenkata and A. Jain, Density functional the- ory driven phononic thermal conductivity prediction of biphenylene: A comparison with graphene, Carbon 183, 893 (2021)

    work page 2021

  62. [62]

    L. Liu, S. Ryu, M. R. Tomasik, E. Stolyarova, N. Jung, M. S. Hybertsen, M. L. Steigerwald, L. E. Brus, and G. W. Flynn, Graphene oxidation: thickness-dependent etching and strong chemical doping, Nano letters 8, 1965 (2008)

    work page 1965

  63. [63]

    T. Li, A. D. Pickel, Y. Yao, Y. Chen, Y. Zeng, S. D. Lacey, Y. Li, Y. Wang, J. Dai, Y. Wang, et al. , Thermo- electric properties and performance of flexible reduced graphene oxide films up to 3,000 K, Nature Energy 3, 148 (2018)

    work page 2018

  64. [64]

    P. Ying, X. Gao, A. Natan, M. Urbakh, and O. Hod, Chemifriction and Superlubricity: Friends or Foes?, The Journal of Physical Chemistry Letters 16, 2934 (2025)

    work page 2025