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REVIEW 3 major objections 3 minor 67 references

Disentangling morphology and conductance in amorphous graphene

T0 review · 3 major / 3 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read The paper claims that the standard two-descriptor morphology space does not uniquely determine conductance in amorphous graphene, and that gate voltage can switch conduction between crystallite-based and defect-based hopping networks.

desk verdict A transparent, reproducible computational study whose central non-uniqueness claim hinges on an ad hoc site-radius cutoff that needs a sensitivity sweep. read the letter →

arxiv 2411.18041 v2 pith:RENYHT7R submitted 2024-11-27 cond-mat.mes-hall cond-mat.dis-nnphysics.chem-ph

Nicolas Gastellu , Ata Madanchi , Lena Simine This is my paper · ORCID
classification cond-mat.mes-hallcond-mat.dis-nnphysics.chem-ph
keywords amorphousgraphenemonolayercarbonvariable-rangehoppingpercolationtheorymorphology-conductancerelationshiptight-bindingelectronicstructuregate-voltagetunabilitymesoscalesimulation
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

Amorphous monolayer carbon films can differ enormously in electrical conductance even when the usual structural descriptors make them look nearly identical. The paper generates three ensembles of roughly 40 nm amorphous graphene samples with a deep-learning structure generator, computes their variable-range hopping conductance from tight-binding electronic states, and compares the results with experimental AMC-300, AMC-400, and AMC-500 films. It reproduces the observed five-order-of-magnitude conductance gap between the most ordered and most disordered ensembles. The central claim is that the descriptor space $(\log \eta_{\mathrm{MRO}}, \rho_{\mathrm{sites}})$ does not determine conductance: a simulated ensemble sitting on top of the insulating AMC-400 in that space is predicted to conduct almost as well as the most ordered material. The paper further claims that hopping networks change character with gate voltage, being carried by crystallites at band edges and by defects near the Fermi energy.

What carries the argument

The load-bearing object is the variable-range hopping network. Each thermally accessible tight-binding molecular orbital is converted into one or more hopping sites with a position and radius, using density-weighted k-means clustering; sites whose effective radius exceeds the largest crystalline inclusion in the ensemble are deleted as numerical artifacts. The hopping rate between sites $i$ and $j$ is written as $\omega_{ij} = \omega_0 e^{-\xi_{ij}}$, where $\xi_{ij}$ combines the site separation, the Gaussian overlap of the two site wavefunctions, and the site energies. Percolating a cluster that connects left and right edges of each 40 nm sample defines a critical $\xi_c$, and the ensemble conductance is the integral of $e^{-\xi}$ over the distribution of $\xi_c$. The second diagnostic object is the crystallinity $\chi$ of a site, the fraction of its electronic density lying on crystalline hexagons, which reveals where in the energy spectrum the conduction network lives.

What would settle it

Measure the conductance of a real AMC film whose ring statistics match sAMC-q400 (crystalline hexagons more abundant than isolated hexagons) rather than AMC-400: if it is insulating, the claimed non-uniqueness of the descriptor map would fail. Alternatively, recompute sAMC-q400 Fermi-energy conductance after varying the k-means localization thresholds (grid resolution, density cutoff, 20 \AA{} pocket separation, and the $a_{\max}$ cutoff) and check whether the $\approx 2.5\times 10^{-5}$ S prediction survives.

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Extended reading notes

Core claim

The paper's central discovery is that a unique map between $(\log \eta_{\mathrm{MRO}}, \rho_{\mathrm{sites}})$ and conductance does not exist for amorphous monolayer carbon. Its evidence is the sAMC-q400 ensemble: those structures occupy nearly the same descriptor coordinates as the experimentally insulating AMC-400, yet the percolation calculation puts their ungated conductance at $(2.51 \pm 0.40)\times 10^{-5}$ S, comparable to the ordered sAMC-300 ensemble. A second claim is that conduction networks are not controlled by atomic structure alone: variable-range hopping sites near the band edges sit preferentially on crystalline inclusions, while sites near the Fermi energy sit preferentially on defects. Because a gate voltage shifts the chemical potential through these regimes, the morphology of the conducting network can be changed while leaving the film itself unchanged.

Load-bearing premise

The results stand on the assumption that the protocol for turning delocalized tight-binding orbitals into hopping sites, namely k-means partitioning into high-density pockets followed by deletion of sites larger than the largest crystalline inclusion, does not systematically bias which morphologies receive sites or how those sites conduct.

Editorial extensions

If this is right

  • The five-order-of-magnitude conductance gap between sAMC-300 and sAMC-500 reproduces the experimental gap, indicating that the percolation-based variable-range hopping model captures the dominant transport physics in these films.
  • Because the same $(\log \eta_{\mathrm{MRO}}, \rho_{\mathrm{sites}})$ point can host both insulating and conducting samples, experimental classification of AMCs should include ring statistics and crystallite-size distributions, not just the two standard order parameters.
  • Gate voltage can modulate conductance by selecting band-edge crystalline hopping networks or Fermi-energy defect networks, without any change to the atomic structure.
  • The Fermi-energy preference for defects implies that ungated conductance is controlled by the defect network's density and connectivity, so defect engineering should be as effective as global crystallinity in designing conductive AMCs.
  • The combination of gate-tunable electronic transport with low thermal conductance positions AMCs as potential thermoelectric materials, once their absolute conductivities are raised toward useful levels.

Reading between the lines

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

  • A natural extension is that any low-dimensional morphology descriptor space will underdetermine transport in disordered two-dimensional carbons; descriptors that capture defect clustering rather than total defect fraction may restore a useful, though possibly multi-valued, structure-property relation.
  • If defect-localized Fermi-energy states really carry the current, then deliberate defect patterning by irradiation, strain, or growth conditions could route current along engineered paths, a possibility the paper does not test.
  • The predicted crystallite-to-defect crossover could be tested directly by spectrally resolved local density-of-state measurements at different gate voltages; if the crossover is absent, the k-means site construction is the likely culprit.
  • The protocol's $a_{\max}$ cutoff is set by the largest crystalline inclusion in each ensemble, so the model implicitly assumes real localization radii never exceed that scale; samples with very large ordered domains might violate that assumption and shift the Fermi-energy conductance.
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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 / 3 minor

Summary. This paper combines MAP-generated mesoscale amorphous graphene structures (sAMC-500, sAMC-q400, sAMC-300) with a tight-binding Lanczos partial diagonalization and a Miller-Abrahams variable-range hopping percolation model to compute ensemble conductances. The authors reproduce the experimentally reported five-order-of-magnitude conductance gap between the most and least ordered ensembles, predict that the sAMC-q400 ensemble conducts similarly to sAMC-300 despite lying near the insulating experimental AMC-400 in the (log η_MRO, ρ_sites) descriptor space, and conclude that a unique morphology-to-conductance map does not exist in this descriptor space. They also analyze the crystallinity of the hopping sites and report a metamorphosis from crystallite-localized conduction at band edges to defect-localized conduction near the Fermi energy.

Significance. The paper is methodologically transparent and has several genuine strengths: it provides a machine-precision benchmark of the Lanczos diagonalization against exact diagonalization (SI Section S6), makes code and data publicly available (SI Section S10), uses mesoscale aperiodic samples without periodic boundary conditions, and reproduces a nontrivial experimental conductance gap. If the central predictions are robust, the conclusion that partial morphology descriptors such as (log η_MRO, ρ_sites) cannot uniquely determine conductance would be a useful cautionary result for the amorphous-materials community, and the crystallinity analysis of conduction networks would open a genuinely new way to think about gate-controlled transport in AMCs. The strength of these claims, however, rests on the site-construction protocol in the VRH model, which is the main fragility discussed below.

major comments (3)
  1. [Supporting Information §S7; Table 1] The site-radius cutoff amax is an ensemble-dependent numerical filter that is load-bearing for the central conductance predictions. In the hopping-rate expression (Eq. S5) and overlap function f (Eq. S6), larger site radii increase inter-site overlap and therefore increase hopping rates; removing all sites with radius above the largest crystalline inclusion in each ensemble (Table 1: 18.03 Å for sAMC-500 versus 136.47 Å for sAMC-q400 and 199.33 Å for sAMC-300) disproportionately suppresses hopping in the most disordered ensemble while preserving very large sites in the ordered ones. The stated justification, that a physical site cannot be larger than the largest crystallite, is not established: VRH localization lengths are not bounded by crystallite size. Because the predicted sAMC-q400 conductance (G(μ=ε_F) = (2.51 ± 0.40)×10^-5 S, Table 1) and the resulting claim that no unique (log η_MRO, ρ_sites)-to-conductance map exists depend on this filter, I request a controlled sweep over amax or a physically derived localization-size criterion to show that the q400/sAMC-300 conductance overlap and the sAMC-500/sAMC-300 gap are not artifacts of the cutoff.
  2. [Supporting Information §S7; main text, 'Constructing the VRH network'] The k-means site-construction protocol is another load-bearing choice that is not validated. The manuscript classifies multi-pocket delocalized MOs as 'pathological' and partitions them into sites using a 100×100 grid, a density threshold of 0.3 of the maximum, and a minimum separation of 20 Å; these parameters are hand-selected, and the protocol is assumed not to bias the relative site density between crystalline and defective regions or between ensembles. Since the near-ε_F states are reported to be the most affected (main text), and since Figure 3d's 'conducting sites preferentially on defects' conclusion is based on these sites, the absence of any sensitivity analysis with respect to the clustering parameters or an alternative site-definition leaves open the possibility that the defect-localization and q400 conductance results are artifacts of the localization procedure.
  3. [Results, 'sAMC-q400' paragraph; Figure 2c] The non-uniqueness claim is stronger than the evidence presented. The sAMC-q400 ensemble is compared with the experimental AMC-400, but the two differ not only in the (log η_MRO, ρ_sites) position (which is close, not identical) but also in ring statistics: the manuscript states that the 6-c/6-i ratio is reversed relative to experiment (Figure 2c and main text). Because the experimental AMC-400 was not generated with the same MAP protocol and its conductance was measured in a different setup, the comparison conflates structural differences not captured by the two chosen descriptors with a genuine breakdown of the morphology-conductance map. The conclusion should be rephrased as showing that the two descriptors are insufficient, or supported by a simulated ensemble with experimentally matched ring statistics and a controlled comparison within the same simulation pipeline.
minor comments (3)
  1. [Throughout] The manuscript contains several typographical errors, including 'postitve' (p. 9), 'experimetnal' (SI Section S3), 'aloogrithm' (SI Section S5), 'devation' (SI Section S6), and 'pecularities' (p. 17).
  2. [Equation (S5); Table 1] The choice of the attempt frequency ω0 = 1 fs^-1 and the 4kBT energy window should be justified or tested; the absolute conductances in Table 1 scale with ω0, even if the relative gap does not.
  3. [Figure 3a and Table 1] Figure 3a states that error bars are omitted because they are too small to be visible, but Table 1 reports relative uncertainties of roughly 40–100% for several band-edge conductances (e.g., G(μ=ε0) for sAMC-500 = (2.99 ± 2.43)×10^-13 S); please clarify how these are represented in the figure.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: conductance is a forward VRH/percolation model benchmarked against external data; the a_max filter is a robustness concern, not a fitted input.

full rationale

The paper's derivation chain is self-contained as a forward model. Tight-binding parameters are taken from prior external literature, the Miller-Abrahams form and percolation integral are standard, and no parameter is fitted to the experimental conductance values used for benchmarking. The reproduction of the five-order-of-magnitude conductance gap between sAMC-500 and sAMC-300 is an emergent prediction rather than a fit, and the claimed non-uniqueness of the (log-eta_MRO, rho_sites) map follows from comparing a separately generated sAMC-q400 ensemble against the experimental AMC-400 without using AMC-400's conductance as an input. The closest candidate to circularity is the ensemble-dependent a_max site-radius cutoff, which is heuristic and could bias relative conductances; however, the cutoff is set by a morphological quantity (largest crystalline inclusion), not by the target conductance, and the final conductance emerges from percolation over the resulting site network. A controlled a_max sweep would strengthen robustness, but this is a correctness/robustness risk rather than a circular reduction. Self-citations (MAP and prior nanojunction work) provide methodology and context; the central results are computed from published code and are anchored to external experimental benchmarks, so they do not constitute load-bearing self-citation circularity.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new physical entities. The 'hopping sites' are computational constructs derived from tight-binding orbitals and are not proposed as independent physical objects.

free parameters (4)
  • amax site-radius cutoff = sAMC-500: 18.03 Å; sAMC-q400: 136.47 Å; sAMC-300: 199.33 Å
    Sites with effective area exceeding the largest crystalline inclusion are removed as numerical artifacts; this ensemble-dependent cutoff affects the percolation network and the predicted conductances.
  • k-means localization parameters = grid 100x100; density threshold 0.3*max; minimum separation 20 Å; |φ|^4 weighting
    These hand-set parameters determine the number and shape of hopping sites extracted from each molecular orbital, directly affecting the VRH network and the crystallinity analysis.
  • thermal energy window = 4k_BT ≈ 0.103 eV at T=300 K
    Only molecular orbitals within this window of the chemical potential are included in the conductance calculation; the window size is chosen rather than derived.
  • escape frequency ω0 = 1 fs^-1
    Global attempt frequency setting the absolute conductance scale; the value is chosen, though it cancels in relative comparisons between ensembles.
assumptions (5)
  • domain assumption The variable-range hopping picture with Miller-Abrahams hopping rates is the correct transport mechanism for these AMC films.
    The paper relies on the experimental observation of the 2D Mott law to justify VRH; all conductance predictions build on this assumption.
  • domain assumption A nearest-neighbor tight-binding Hamiltonian with parameters from prior literature, restricted to π-electrons, adequately represents the electronic structure.
    The Hamiltonian H uses only 2p_z orbitals and semi-empirical hopping from Refs. 22/23; electron-electron, electron-phonon, substrate, and ripple effects are neglected.
  • ad hoc to paper Molecular orbitals with several disjoint high-density pockets are numerical artifacts to be artificially localized.
    The paper states these delocalized eigenstates would not withstand decoherence; this premise justifies the entire k-means site construction.
  • domain assumption Percolation across the left and right edges of a 40 nm x 40 nm sample gives the ensemble conductance.
    The percolation threshold ξ_c is found by connecting sites between left and right electrodes; the conductance formula from Rodin and Fogler is assumed to apply.
  • standard math Gershgorin's theorem and the LDLT factorization correctly estimate the HOMO position.
    Used to aim the Lanczos routine; benchmarked to machine precision on a small system.

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Cite this review

Pith. "Pith review of Disentangling morphology and conductance in amorphous graphene." pith.science (2026). https://pith.science/paper/RENYHT7R

@misc{pith2026241118041,
  author       = {Pith},
  title        = {Pith review of: Disentangling morphology and conductance in amorphous graphene},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RENYHT7R}},
  note         = {Machine review of arXiv:2411.18041}
}
read the original abstract

Amorphous graphene or amorphous monolayer carbon (AMC) is a family of carbon films that exhibit a surprising sensitivity of electronic conductance to morphology. We combine deep learning-enhanced simulation techniques with percolation theory to analyze three morphologically distinct mesoscale AMCs. Our approach avoids the pitfalls of applying periodic boundary conditions to these fundamentally aperiodic systems or equating crystalline inclusions with conducting sites. We reproduce the previously reported dependence of charge conductance on morphology and explore the limitations of partial morphology descriptors in witnessing conductance properties. Finally, we perform crystallinity analysis of conductance networks along the electronic energy spectrum and show that they metamorphose from being localized on crystallites at band edges to localized on defects around the Fermi energy opening the possibility of control through gate voltage.

Figures

Figures reproduced from arXiv: 2411.18041 by the authors.

Figure 1
Figure 1. MAP-generated AMCs. To highlight the topological composition of AMC samples, pentagons are highlighted in red, isolated hexagons are in dark green, crystalline hexagons are in lime green (see Section S2 of Supporting Information for a technical definition of isolated and crystalline hexagons), 7- and 8-membered rings are in dark blue, 9- and 10-membered rings are in grey, squares are in turquoise, and triangles are … view at source ↗
Figure 2
Figure 2. Characterisation of disorder in AMC. (a) Ensemble-averaged pair correlation functions of sAMC-500 (green), sAMC-q400 (purple), and sAMC-300 (orange). The pair correlation function g(r) of pristine graphene (blue) is also shown as a reference. (b) Scatter plot of all the structures in the three ensembles in (log ηMRO, ρsites) space. The green, purple, and orange stars mark the respective positions of the sAMC-500, sA… view at source ↗
Figure 3
Figure 3. Electronic conductance and the morphology of conducting pathways in AMC. (a) Electrical conductance G of the three sAMC ensembles, at different gate voltages. The error bars are omitted because they are too small to be visible (see [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗

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Works this paper leans on

67 extracted references · 64 canonical work pages

  1. [2]

    S.; Wang, Y.-P.; Orofeo, C

    Toh, C.-T.; Zhang, H.; Lin, J.; Mayorov, A. S.; Wang, Y.-P.; Orofeo, C. M.; Ferry, D. B.; Andersen, H.; Kakenov, N.; Guo, Z. et al. Synthesis and properties of free-standing monolayer amorphous carbon. Nature 2020, 577, 199--203

    work page 2020

  2. [3]

    Electronic structure of a realistic model of amorphous graphene

    Kapko, V.; Drabold, D.; Thorpe, M. Electronic structure of a realistic model of amorphous graphene. physica status solidi (b) 2010, 247, 1197--1200

    work page 2010

  3. [4]

    Insulating behavior of an amorphous graphene membrane

    Van Tuan, D.; Kumar, A.; Roche, S.; Ortmann, F.; Thorpe, M.; Ordejon, P. Insulating behavior of an amorphous graphene membrane. Physical Review B 2012, 86, 121408

    work page 2012

  4. [5]

    A.; Niquet, Y.-M.; Delcorte, A.; Charlier, J.-C

    Lherbier, A.; Roche, S.; Restrepo, O. A.; Niquet, Y.-M.; Delcorte, A.; Charlier, J.-C. Highly defective graphene: A key prototype of two-dimensional Anderson insulators. Nano Research 2013, 6, 326--334

    work page 2013

  5. [6]

    Phonons, localization, and thermal conductivity of diamond nanothreads and amorphous graphene

    Zhu, T.; Ertekin, E. Phonons, localization, and thermal conductivity of diamond nanothreads and amorphous graphene. Nano letters 2016, 16, 4763--4772

    work page 2016

  6. [7]

    Thermal transport in amorphous graphene with varying structural quality

    Antidormi, A.; Colombo, L.; Roche, S. Thermal transport in amorphous graphene with varying structural quality. 2D Materials 2020, 8, 015028

    work page 2020

  7. [8]

    Amorphous graphene: a realization of Zachariasen’s glass

    Kumar, A.; Wilson, M.; Thorpe, M. Amorphous graphene: a realization of Zachariasen’s glass. Journal of Physics: Condensed Matter 2012, 24, 485003

    work page 2012

  8. [9]

    Zhang, Y.-T.; Wang, Y.-P.; Zhang, X.; Zhang, Y.-Y.; Du, S.; Pantelides, S. T. Structure of amorphous two-dimensional materials: elemental monolayer amorphous carbon versus binary monolayer amorphous boron nitride. Nano Letters 2022, 22, 8018--8024

    work page 2022

Show all 67 references
  1. [10]

    Stone--wales defects preserve hyperuniformity in amorphous two-dimensional networks

    Chen, D.; Zheng, Y.; Liu, L.; Zhang, G.; Chen, M.; Jiao, Y.; Zhuang, H. Stone--wales defects preserve hyperuniformity in amorphous two-dimensional networks. Proceedings of the National Academy of Sciences 2021, 118, e2016862118

    2021

  2. [11]

    C.; Tromer, R

    Felix, L. C.; Tromer, R. M.; Autreto, P. A.; Ribeiro Junior, L. A.; Galvao, D. S. On the mechanical properties and thermal stability of a recently synthesized monolayer amorphous carbon. The Journal of Physical Chemistry C 2020, 124, 14855--14860

    2020

  3. [12]

    Tian, H.; Ma, Y.; Li, Z.; Cheng, M.; Ning, S.; Han, E.; Xu, M.; Zhang, P.-F.; Zhao, K.; Li, R. et al. Disorder-tuned conductivity in amorphous monolayer carbon. Nature 2023, 615, 56--61

    2023

  4. [13]

    Y.; Bengio, Y.; Simine, L

    Kilgour, M.; Gastellu, N.; Hui, D. Y.; Bengio, Y.; Simine, L. Generating multiscale amorphous molecular structures using deep learning: a study in 2D. The Journal of Physical Chemistry Letters 2020, 11, 8532--8537

    2020

  5. [14]

    D.; Simine, L

    Madanchi, A.; Kilgour, M.; Zysk, F.; K \"u hne, T. D.; Simine, L. Simulations of disordered matter in 3D with the morphological autoregressive protocol (MAP) and convolutional neural networks. The Journal of Chemical Physics 2024, 160, 024101

    2024

  6. [15]

    L.; Cs \'a nyi, G

    Deringer, V. L.; Cs \'a nyi, G. Machine learning based interatomic potential for amorphous carbon. Physical Review B 2017, 95, 094203

    2017

  7. [16]

    Anderson, P. W. Absence of diffusion in certain random lattices. Physical review 1958, 109, 1492

    1958

  8. [17]

    Thouless, D. J. Electrons in disordered systems and the theory of localization. Physics Reports 1974, 13, 93--142

    1974

  9. [18]

    A.; Ramakrishnan, T

    Lee, P. A.; Ramakrishnan, T. V. Disordered electronic systems. Reviews of modern physics 1985, 57, 287

    1985

  10. [19]

    F.; Davis, E

    Mott, N. F.; Davis, E. A. Electronic processes in non-crystalline materials; Oxford University Press Oxford, 1971; Vol. 14

    1971

  11. [20]

    Hopping conductivity in disordered systems

    Ambegaokar, V.; Halperin, B.; Langer, J. Hopping conductivity in disordered systems. Physical review B 1971, 4, 2612

    1971

  12. [21]

    I.; Efros, A

    Shklovskii, B. I.; Efros, A. L. Electronic properties of doped semiconductors; Springer Science & Business Media, 1984; Vol. 45

    1984

  13. [22]

    Sterpone, F.; Rossky, P. J. Molecular modeling and simulation of conjugated polymer oligomers: Ground and excited state chain dynamics of PPV in the gas phase. The Journal of Physical Chemistry B 2008, 112, 4983--4993

    2008

  14. [23]

    E.; Willard, A

    Jailaubekov, A. E.; Willard, A. P.; Tritsch, J. R.; Chan, W.-L.; Sai, N.; Gearba, R.; Kaake, L. G.; Williams, K. J.; Leung, K.; Rossky, P. J. et al. Hot charge-transfer excitons set the time limit for charge separation at donor/acceptor interfaces in organic photovoltaics. Nat...

    2013

  15. [24]

    H.; Van Loan, C

    Golub, G. H.; Van Loan, C. F. Matrix Computations, 3rd ed.; JHU press, 1996; pp 470--499

    1996

  16. [25]

    C.; Geim, A

    Meyer, J. C.; Geim, A. K.; Katsnelson, M. I.; Novoselov, K. S.; Booth, T. J.; Roth, S. The structure of suspended graphene sheets. Nature 2007, 446, 60--63

    2007

  17. [26]

    Stone-Wales defects in graphene and other planar sp 2-bonded materials

    Ma, J.; Alf \`e , D.; Michaelides, A.; Wang, E. Stone-Wales defects in graphene and other planar sp 2-bonded materials. Physical Review B—Condensed Matter and Materials Physics 2009, 80, 033407

    2009

  18. [27]

    Electrical resistivity of polycrystalline graphene: Effect of grain-boundary-induced strain fields

    Krasavin, S.; Osipov, V. Electrical resistivity of polycrystalline graphene: Effect of grain-boundary-induced strain fields. Scientific Reports 2022, 12, 14553

    2022

  19. [28]

    Impurity band and conductivity of compensated semiconductors

    Shklovskii, B.; Efros, A. Impurity band and conductivity of compensated semiconductors. Soviet physics JETP 1971, 33, 468--474

    1971

  20. [29]

    Percolation and conductivity: A computer study

    Pike, G.; Seager, C. Percolation and conductivity: A computer study. I. Physical review B 1974, 10, 1421

    1974

  21. [30]

    A hopping model for activated charge transport in amorphous silicon

    Gr \"u newald, M.; Thomas, P. A hopping model for activated charge transport in amorphous silicon. physica status solidi (b) 1979, 94, 125--133

    1979

  22. [31]

    Theory of the field-effect mobility in amorphous organic transistors

    Vissenberg, M.; Matters, M. Theory of the field-effect mobility in amorphous organic transistors. Physical Review B 1998, 57, 12964

    1998

  23. [32]

    Percolation approach to hopping transport in organic disordered solids

    Baranovskii, S.; Zvyagin, I.; Cordes, H.; Yamasaki, S.; Thomas, P. Percolation approach to hopping transport in organic disordered solids. physica status solidi (b) 2002, 230, 281--288

    2002

  24. [33]

    Concentration dependence of the hopping mobility in disordered organic solids

    Rubel, O.; Baranovskii, S.; Thomas, P.; Yamasaki, S. Concentration dependence of the hopping mobility in disordered organic solids. Physical Review B 2004, 69, 014206

    2004

  25. [34]

    A percolation approach to the temperature and charge carrier concentration dependence of the hopping conductivity in organic materials

    Zvyagin, I. A percolation approach to the temperature and charge carrier concentration dependence of the hopping conductivity in organic materials. physica status solidi c 2008, 5, 725--729

    2008

  26. [35]

    Hopping transport in systems of finite thickness or length

    Rodin, A.; Fogler, M. Hopping transport in systems of finite thickness or length. Physical Review B 2011, 84, 125447

    2011

  27. [36]

    Advanced percolation solution for hopping conductivity

    Nenashev, A.; Jansson, F.; Oelerich, J.; Huemmer, D.; Dvurechenskii, A.; Gebhard, F.; Baranovskii, S. Advanced percolation solution for hopping conductivity. Physical Review B 2013, 87, 235204

    2013

  28. [37]

    Molecular geometry fluctuations and field-dependent mobility in conjugated polymers

    Yu, Z.; Smith, D.; Saxena, A.; Martin, R.; Bishop, A. Molecular geometry fluctuations and field-dependent mobility in conjugated polymers. Physical Review B 2001, 63, 085202

    2001

  29. [38]

    Scaling of current distributions in variable-range hopping transport on two-and three-dimensional lattices

    Pasveer, W.; Bobbert, P.; Huinink, H.; Michels, M. Scaling of current distributions in variable-range hopping transport on two-and three-dimensional lattices. Physical Review B 2005, 72, 174204

    2005

  30. [39]

    Charge transport in disordered organic photoconductors

    B \"a ssler, H. Charge transport in disordered organic photoconductors. A Monte Carlo simulation study. Physica Status Solidi B (Basic Research);(Germany) 1993, 175, 15--56

    1993

  31. [40]

    T.; Liu, W.; Balzer, D.; Kassal, I

    Willson, J. T.; Liu, W.; Balzer, D.; Kassal, I. Jumping kinetic Monte Carlo: Fast and accurate simulations of partially delocalized charge transport in organic semiconductors. The Journal of Physical Chemistry Letters 2023, 14, 3757--3764

    2023

  32. [41]

    Private electronic correspondence, 2024-04-26

    Liu, L. Private electronic correspondence, 2024-04-26

    2024

  33. [42]

    Electronic conduction through monolayer amorphous carbon nanojunctions

    Gastellu, N.; Kilgour, M.; Simine, L. Electronic conduction through monolayer amorphous carbon nanojunctions. The Journal of Physical Chemistry Letters 2022, 13, 339--344

    2022

  34. [43]

    Zachariasen, W. H. The atomic arrangement in glass. Journal of the American Chemical Society 1932, 54, 3841--3851

    1932

  35. [44]

    Atomistic structure of band-tail states in amorphous silicon

    Dong, J.; Drabold, D. Atomistic structure of band-tail states in amorphous silicon. Physical review letters 1998, 80, 1928

    1998

  36. [45]

    Universal features of localized eigenstates in disordered systems

    Ludlam, J.; Taraskin, S.; Elliott, S.; Drabold, D. Universal features of localized eigenstates in disordered systems. Journal of Physics: Condensed Matter 2005, 17, L321

    2005

  37. [46]

    F.; Davis, E

    Mott, N. F.; Davis, E. A. Electronic processes in non-crystalline materials; OUP Oxford, 2012

    2012

  38. [47]

    I.; Barker, A

    Liu, J.; van der Zee, B.; Alessandri, R.; Sami, S.; Dong, J.; Nugraha, M. I.; Barker, A. J.; Rousseva, S.; Qiu, L.; Qiu, X. et al. N-type organic thermoelectrics: demonstration of ZT> 0.3. Nature communications 2020, 11, 5694

    2020

  39. [48]

    Zhao, J.; Zhu, G.; Huang, W.; He, Z.; Feng, X.; Ma, Y.; Dong, X.; Fan, Q.; Wang, L.; Hu, Z. et al. Synthesis of large-scale undoped and nitrogen-doped amorphous graphene on MgO substrate by chemical vapor deposition. Journal of Materials Chemistry 2012, 22, 19679--19683 mcitet...

    2012 arXiv

  40. [49]

    Conditional image generation with pixelcnn decoders

    Van den Oord, A.; Kalchbrenner, N.; Espeholt, L.; Vinyals, O.; Graves, A., et al. Conditional image generation with pixelcnn decoders. Advances in neural information processing systems 2016, 29

    2016

  41. [50]

    Pixel recurrent neural networks

    Van Den Oord, A.; Kalchbrenner, N.; Kavukcuoglu, K. Pixel recurrent neural networks. International conference on machine learning. 2016; pp 1747--1756

    2016

  42. [51]

    sAMC generation

    Madanchi , A.; Gastellu , N. sAMC generation . sAMC\_generation v1.0.1 on Zenodo , https://doi.org/10.5281/zenodo.15085683

    doi

  43. [52]

    El-Machachi, Z.; Wilson, M.; Deringer, V. L. Exploring the configurational space of amorphous graphene with machine-learned atomic energies. Chemical Science 2022, 13, 13720--13731

    2022

  44. [53]

    J.; Tutein, A

    Stuart, S. J.; Tutein, A. B.; Harrison, J. A. A reactive potential for hydrocarbons with intermolecular interactions. The Journal of chemical physics 2000, 112, 6472--6486

    2000

  45. [54]

    Fast parallel algorithms for short-range molecular dynamics

    Plimpton, S. Fast parallel algorithms for short-range molecular dynamics. Journal of computational physics 1995, 117, 1--19

    1995

  46. [55]

    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., et al. LAMMPS-a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scale...

    2022

  47. [56]

    Disorder-tuned conductivity in amorphous monolayer carbon

    Tian, H.; Ma, Y.; Li, Z.; Cheng, M.; Ning, S.; Han, E.; Xu, M.; Zhang, P.-F.; Zhao, K.; Li, R., et al. Disorder-tuned conductivity in amorphous monolayer carbon. Nature 2023, 615, 56--61

    2023

  48. [57]

    Defects, tight binding, and first-principles molecular-dynamics simulations on a-Si

    Fedders, P.; Drabold, D.; Klemm, S. Defects, tight binding, and first-principles molecular-dynamics simulations on a-Si. Physical Review B 1992, 45, 4048

    1992

  49. [58]

    Consistency requirement in the Pariser-Parr-Pople model

    Linderberg, J. Consistency requirement in the Pariser-Parr-Pople model. Chemical Physics Letters 1967, 1, 39--41

    1967

  50. [59]

    E.; Willard, A

    Jailaubekov, A. E.; Willard, A. P.; Tritsch, J. R.; Chan, W.-L.; Sai, N.; Gearba, R.; Kaake, L. G.; Williams, K. J.; Leung, K.; Rossky, P. J., et al. Hot charge-transfer excitons set the time limit for charge separation at donor/acceptor interfaces in organic photovoltaics. Na...

    2013

  51. [60]

    The spectral transformation Lanczos method for the numerical solution of large sparse generalized symmetric eigenvalue problems

    Ericsson, T.; Ruhe, A. The spectral transformation Lanczos method for the numerical solution of large sparse generalized symmetric eigenvalue problems. Mathematics of Computation 1980, 35, 1251--1268

    1980

  52. [61]

    Higham, N. J. Functions of matrices: theory and computation; SIAM, 2008; p 323

    2008

  53. [62]

    Least squares quantization in PCM

    Lloyd, S. Least squares quantization in PCM. IEEE transactions on information theory 1982, 28, 129--137

    1982

  54. [63]

    Pedregosa, F. et al. Scikit-learn: Machine Learning in P ython. Journal of Machine Learning Research 2011, 12, 2825--2830

    2011

  55. [64]

    Impurity conduction at low concentrations

    Miller, A.; Abrahams, E. Impurity conduction at low concentrations. Physical Review 1960, 120, 745

    1960

  56. [65]

    Economou, E.; Cohen, M. H. Localization in disordered materials: Existence of mobility edges. Physical Review Letters 1970, 25, 1445

    1970

  57. [66]

    ArpackMAC

    Gastellu , N. ArpackMAC . ArpackMAC v1.0.0.01 on Zenodo , https://doi.org/10.5281/zenodo.15131343

    doi

  58. [67]

    PercolateMAC

    Gastellu , N. PercolateMAC . percolate\_mac v1.0.0 on Zenodo , https://doi.org/10.5281/zenodo.15078860

    doi

  59. [68]

    ) retain a highly crystalline character, while their near- _F MOs are concentrated near ``defects

    Gastellu , N. DisorderAnalysisMAC . disorder\_analysis\_MAC v1.0.0 on Zenodo , https://doi.org/10.5281/zenodo.15081260 mcitethebibliography supp_info.tex0000664000000000000000000012410714776127052012322 0ustar rootroot [journal=jacsat,manuscript=article] achemso chemformula [T...

    doi

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.