DataVXCarbon LettersPaper
journal article·2025-06

Development and evaluation of machine-learned interatomic potentials for carbon nanotubes for molecular dynamics simulations

Vijay Choyal, Saurabh Mishra, Nitin Luhadiya, S. I. Kundalwal

Carbon Letters· Volume 35· Issue 3
Publisher page
Bibliographic record

Abstract

No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.

Source: Not provided · Quality: missing

Research context

Nanomaterials

OpenAlex topics: Machine Learning in Materials Science · Carbon Nanotubes in Composites · Fuel Cells and Related Materials

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (71)

Show source-provided references
1. Linkov P, Artemyev M, Efimov AE, Nabiev I (2013) Comparative advantages and limitations of the basic metrology methods applied to the characterization of nanomaterials. Nanoscale 5(19):8781. https://doi.org/10.1039/c3nr02372a10.1039/c3nr02372a ↗
2. Holec D, Zhou L, Riedl H, Koller CM, Mayrhofer PH, Friák M, Šob M, Körmann F, Neugebauer J, Music D, Hartmann MA, Fischer FD (2017) Atomistic modeling-based design of novel materials. Adv Eng Mater 19(4):1600688. https://doi.org/10.1002/adem.20160068810.1002/adem.201600688 ↗
3. Marx D, Hutter J (2009) Ab initio molecular dynamics. https://doi.org/10.1017/CBO978051160963310.1017/CBO9780511609633 ↗
4. Xuan W, Liu Y-H, Chen S-Y, Dyer MS, Chen H-YT (2024) Unveiling the morphology of carbon-supported Ru nanoparticles by multiscale modeling. Nano Lett 24(9):2689–2697. https://doi.org/10.1021/acs.nanolett.3c0379610.1021/acs.nanolett.3c03796 ↗
5. Mortazavi B, Silani M, Podryabinkin EV, Rabczuk T, Zhuang X, Shapeev AV (2021) First-principles multiscale modeling of mechanical properties in graphene/borophene heterostructures empowered by machine-learning interatomic potentials. Adv Mater. https://doi.org/10.1002/adma.20210280710.1002/adma.202102807 ↗
6. Zuo Y, Chen C, Li X, Deng Z, Chen Y, Behler J, Csányi G, Shapeev AV, Thompson AP, Wood MA, Ong SP (2020) Performance and cost assessment of machine learning interatomic potentials. J Phys Chem A 124(4):731–745. https://doi.org/10.1021/acs.jpca.9b0872310.1021/acs.jpca.9b08723 ↗
7. Behler J, Parrinello M (2007) Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys Rev Lett 98(14):146401. https://doi.org/10.1103/PhysRevLett.98.14640110.1103/PhysRevLett.98.146401 ↗
8. Behler J (2016) Perspective: machine learning potentials for atomistic simulations. J Chem Phys. https://doi.org/10.1063/1.496619210.1063/1.4966192 ↗
9. Behler J (2011) Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations. Phys Chem Chem Phys 13(40):17930. https://doi.org/10.1039/c1cp21668f10.1039/c1cp21668f ↗
10. Bartók AP, Payne MC, Kondor R, Csányi G (2010) Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons. Phys Rev Lett 104(13):136403. https://doi.org/10.1103/PhysRevLett.104.13640310.1103/PhysRevLett.104.136403 ↗
11. Thompson AP, Swiler LP, Trott CR, Foiles SM, Tucker GJ (2015) Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials. J Comput Phys 285:316–330. https://doi.org/10.1016/j.jcp.2014.12.01810.1016/j.jcp.2014.12.018 ↗
12. Shapeev AV (2016) Moment tensor potentials: a class of systematically improvable interatomic potentials. Multiscale Model Simul 14(3):1153–1173. https://doi.org/10.1137/15M105418310.1137/15M1054183 ↗
13. Artrith N, Urban A (2016) An implementation of artificial neural-network potentials for atomistic materials simulations: performance for TiO2. Comput Mater Sci 114:135–150. https://doi.org/10.1016/j.commatsci.2015.11.04710.1016/j.commatsci.2015.11.047 ↗
14. Guo R, Li G, Tang J, Wang Y, Song X (2023) Small-data-based machine learning interatomic potentials for graphene grain boundaries enabled by structural unit model. Carbon Trends 11:100260. https://doi.org/10.1016/j.cartre.2023.10026010.1016/j.cartre.2023.100260 ↗
15. Mortazavi B, Shapeev AV (2022) Anisotropic mechanical response, high negative thermal expansion, and outstanding dynamical stability of biphenylene monolayer revealed by machine-learning interatomic potentials. FlatChem 32:100347. https://doi.org/10.1016/j.flatc.2022.10034710.1016/j.flatc.2022.100347 ↗
16. Arabha S, Rajabpour A (2021) Thermo-mechanical properties of nitrogenated holey graphene (C2N): a comparison of machine-learning-based and classical interatomic potentials. Int J Heat Mass Transf 178:121589. https://doi.org/10.1016/j.ijheatmasstransfer.2021.12158910.1016/j.ijheatmasstransfer.2021.121589 ↗
17. Choyal V, Sagar N, Sai Gautam G (2024) Constructing and evaluating machine-learned interatomic potentials for Li-based disordered rocksalts. J Chem Theory Comput 20(11):4844–4856. https://doi.org/10.1021/acs.jctc.4c0003910.1021/acs.jctc.4c00039 ↗
18. Wang L, Yang T, Wei M, Guan R, Wei W, Jiang J (2025) Machine learning and DFT dual-guidance of carbon dots implanted SrTiO3 hollow nanosphere for efficient all-pH-value photocatalysis. J Mater Sci Technol 217:169–181. https://doi.org/10.1016/j.jmst.2024.08.02810.1016/j.jmst.2024.08.028 ↗
19. Wang L, Yang T, Feng B, Xu X, Shen Y, Li Z, Arramel JJ (2023) Constructing dual electron transfer channels to accelerate CO2 photoreduction guided by machine learning and first-principles calculation. Chin J Catal 54:265–277. https://doi.org/10.1016/S1872-2067(23)64546-210.1016/S1872-2067(23)64546-2 ↗
20. Hedman D, McLean B, Bichara C, Maruyama S, Larsson JA, Ding F (2024) Dynamics of growing carbon nanotube interfaces probed by machine learning-enabled molecular simulations. Nat Commun 15(1):4076. https://doi.org/10.1038/s41467-024-47999-710.1038/s41467-024-47999-7 ↗
21. Iijima S (1991) Helical microtubules of graphitic carbon. Nature 354(6348):56–58. https://doi.org/10.1038/354056a010.1038/354056a0 ↗
22. Popov V (2004) Carbon nanotubes: properties and application. Mater Sci Eng R Rep 43(3):61–102. https://doi.org/10.1016/j.mser.2003.10.00110.1016/j.mser.2003.10.001 ↗
23. Kundalwal SI, Choyal V (2018) Transversely isotropic elastic properties of carbon nanotubes containing vacancy defects using MD. Acta Mech 229(6):2571–2584. https://doi.org/10.1007/s00707-018-2123-510.1007/s00707-018-2123-5 ↗
24. Rathi A, Kundalwal SI (2020) Mechanical and fracture behavior of MWCNT/ZrO2/epoxy nanocomposite systems: experimental and numerical study. Polym Compos 41(6):2491–2507. https://doi.org/10.1002/pc.2555110.1002/pc.25551 ↗
25. Liu L, Han J, Xu L, Zhou J, Zhao C, Ding S, Shi H, Xiao M, Ding L, Ma Z, Jin C, Zhang Z, Peng L-M (2020) Aligned, high-density semiconducting carbon nanotube arrays for high-performance electronics. Science 368(6493):850–856. https://doi.org/10.1126/science.aba598010.1126/science.aba5980 ↗
26. Mishra S, Luhadiya N, Kundalwal SI (2023) Atomistic insights into the H2 adsorption and desorption behavior of novel Li-functionalized polycrystalline CNTs. Carbon 207:23–35. https://doi.org/10.1016/j.carbon.2023.03.00210.1016/j.carbon.2023.03.002 ↗
27. Mishra S, Kundalwal SI (2024) Hydrogen sorption kinetics and mobility on Li-functionalized polycrystalline carbon nanotube bundles: a molecular dynamics study. J Energy Storage 86:111129. https://doi.org/10.1016/j.est.2024.11112910.1016/j.est.2024.111129 ↗
28. Kumar S, Mishra S, Kumar A (2023) Effect of interface on elastic properties and vibration characteristics of CNT-reinforced composites. Recent Adv Mach Mech. https://doi.org/10.1007/978-981-19-3716-3_5410.1007/978-981-19-3716-3_54 ↗
29. Alian AR, Kundalwal SI, Meguid SA (2015) Interfacial and mechanical properties of epoxy nanocomposites using different multiscale modeling schemes. Compos Struct 131:545–555. https://doi.org/10.1016/j.compstruct.2015.06.01410.1016/j.compstruct.2015.06.014 ↗
30. Yu M-F, Lourie O, Dyer MJ, Moloni K, Kelly TF, Ruoff RS (2000) Strength and breaking mechanism of multiwalled carbon nanotubes under tensile load. Science 287(5453):637–640. https://doi.org/10.1126/science.287.5453.63710.1126/science.287.5453.637 ↗
31. Shokrieh MM, Rafiee R (2010) A review of the mechanical properties of isolated carbon nanotubes and carbon nanotube composites. Mech Compos Mater 46(2):155–172. https://doi.org/10.1007/s11029-010-9135-010.1007/s11029-010-9135-0 ↗
32. Hsieh J-Y, Lu J-M, Huang M-Y, Hwang C-C (2006) Theoretical variations in the Young’s modulus of single-walled carbon nanotubes with tube radius and temperature: a molecular dynamics study. Nanotechnology 17(15):3920–3924. https://doi.org/10.1088/0957-4484/17/15/05110.1088/0957-4484/17/15/051 ↗
33. Cao G, Chen X (2007) The effects of chirality and boundary conditions on the mechanical properties of single-walled carbon nanotubes. Int J Solids Struct 44(17):5447–5465. https://doi.org/10.1016/j.ijsolstr.2007.01.00510.1016/j.ijsolstr.2007.01.005 ↗
34. Dilrukshi KGS, Dewapriya MAN, Puswewala UGA (2015) Size dependency and potential field influence on deriving mechanical properties of carbon nanotubes using molecular dynamics. Theor Appl Mech Lett 5(4):167–172. https://doi.org/10.1016/j.taml.2015.05.00510.1016/j.taml.2015.05.005 ↗
35. Fereidoon A, Ghorbanzadeh Ahangari M, Ganji MD, Jahanshahi M (2012) Density functional theory investigation of the mechanical properties of single-walled carbon nanotubes. Comput Mater Sci 53(1):377–381. https://doi.org/10.1016/j.commatsci.2011.08.00710.1016/j.commatsci.2011.08.007 ↗
36. Hernández E, Goze C, Bernier P, Rubio A (1999) Elastic properties of single-wall nanotubes. Appl Phys A Mater Sci Process 68(3):287–292. https://doi.org/10.1007/s00339005089010.1007/s003390050890 ↗
37. Walters DA, Ericson LM, Casavant MJ, Liu J, Colbert DT, Smith KA, Smalley RE (1999) Elastic strain of freely suspended single-wall carbon nanotube ropes. Appl Phys Lett 74(25):3803–3805. https://doi.org/10.1063/1.12418510.1063/1.124185 ↗
38. Yu M-F, Files BS, Arepalli S, Ruoff RS (2000) Tensile loading of ropes of single wall carbon nanotubes and their mechanical properties. Phys Rev Lett 84(24):5552–5555. https://doi.org/10.1103/PhysRevLett.84.555210.1103/PhysRevLett.84.5552 ↗
39. Tombler TW, Zhou C, Alexseyev L, Kong J, Dai H, Liu L, Jayanthi CS, Tang M, Wu S-Y (2000) Reversible electromechanical characteristics of carbon nanotubes underlocal-probe manipulation. Nature 405(6788):769–772. https://doi.org/10.1038/3501551910.1038/35015519 ↗
40. Jin Y, Yuan FG (2003) Simulation of elastic properties of single-walled carbon nanotubes. Compos Sci Technol 63(11):1507–1515. https://doi.org/10.1016/S0266-3538(03)00074-510.1016/S0266-3538(03)00074-5 ↗
41. Meo M, Rossi M (2006) Tensile failure prediction of single wall carbon nanotube. Eng Fract Mech 73(17):2589–2599. https://doi.org/10.1016/j.engfracmech.2006.05.00510.1016/j.engfracmech.2006.05.005 ↗
42. Song X, Ge Q, Yen S-C (2009) A first-principles study on the elastic properties of single-walled carbon nanotubesg. Proc Inst Mech Eng Part N: J Nanoeng Nanosyst 223(3–4):163–168. https://doi.org/10.1177/17403499JNN18110.1177/17403499JNN181 ↗
43. Yazdani H, Hatami K, Eftekhari M (2017) Mechanical properties of single-walled carbon nanotubes: a comprehensive molecular dynamics study. Mater Res Express 4(5):055015. https://doi.org/10.1088/2053-1591/aa700310.1088/2053-1591/aa7003 ↗
44. Mishra S, Kundalwal SI (2022) Topological defects embedded large-sized single-walled carbon nanotubes for hydrogen storage: a molecular dynamics study. Int J Hydrogen Energy 47(86):36605–36621. https://doi.org/10.1016/j.ijhydene.2022.08.21210.1016/j.ijhydene.2022.08.212 ↗
45. Suenaga K, Wakabayashi H, Koshino M, Sato Y, Urita K, Iijima S (2007) Imaging active topological defects in carbon nanotubes. Nat Nanotechnol 2(6):358–360. https://doi.org/10.1038/nnano.2007.14110.1038/nnano.2007.141 ↗
46. Alian AR, Meguid SA, Kundalwal SI (2017) Unraveling the influence of grain boundaries on the mechanical properties of polycrystalline carbon nanotubes. Carbon 125:180–188. https://doi.org/10.1016/j.carbon.2017.09.05610.1016/j.carbon.2017.09.056 ↗
47. Yuan J, Liew KM (2009) Effects of vacancy defect reconstruction on the elastic properties of carbon nanotubes. Carbon 47(6):1526–1533. https://doi.org/10.1016/j.carbon.2009.01.04810.1016/j.carbon.2009.01.048 ↗
48. Zhou ZR, Liao K (2013) Effect of inter-defect interaction on tensile fatigue behavior of a single-walled carbon nanotube with stone-wales defects. J Appl Mech. https://doi.org/10.1115/1.402353610.1115/1.4023536 ↗
49. Sihn S, Varshney V, Roy AK, Farmer BL (2015) Modeling for predicting strength of carbon nanostructures. Carbon 95:181–189. https://doi.org/10.1016/j.carbon.2015.08.00810.1016/j.carbon.2015.08.008 ↗
50. Jhon YI, Kim C, Seo M, Cho WJ, Lee S, Jhon YM (2016) Tensile characterization of single-walled carbon nanotubes with helical structural defects. Sci Rep 6(1):20324. https://doi.org/10.1038/srep2032410.1038/srep20324 ↗
51. Mishra S, Maware PP, Choyal V, Kundalwal SI (2023) Atomistic insights into the fracture mechanisms of Stone–Wales-defected CNTs under transversely isotropic loading. Eur Phys J Plus 138(5):443. https://doi.org/10.1140/epjp/s13360-023-04104-z10.1140/epjp/s13360-023-04104-z ↗
52. Gubaev K, Podryabinkin EV, Hart GLW, Shapeev AV (2019) Accelerating high-throughput searches for new alloys with active learning of interatomic potentials. Comput Mater Sci 156:148–156. https://doi.org/10.1016/j.commatsci.2018.09.03110.1016/j.commatsci.2018.09.031 ↗
53. Podryabinkin EV, Shapeev AV (2017) Active learning of linearly parametrized interatomic potentials. Comput Mater Sci 140:171–180. https://doi.org/10.1016/j.commatsci.2017.08.03110.1016/j.commatsci.2017.08.031 ↗
54. Choyal V, Patil M, Luhadiya N, Kundalwal SI (2025) Machine-learned interatomic potentials for accurate analysis of the mechanical properties of boron nitride sheets. J Phys Mater 8(1):015003. https://doi.org/10.1088/2515-7639/ad963510.1088/2515-7639/ad9635 ↗
55. Kresse G, Furthmüller J (1996) Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys Rev B 54(16):11169–11186. https://doi.org/10.1103/PhysRevB.54.1116910.1103/PhysRevB.54.11169 ↗
56. Blöchl PE (1994) Projector augmented-wave method. Phys Rev B 50(24):17953–17979. https://doi.org/10.1103/PhysRevB.50.1795310.1103/PhysRevB.50.17953 ↗
57. Perdew JP, Burke K, Ernzerhof M (1996) Generalized gradient approximation made simple. Phys Rev Lett 77(18):3865–3868. https://doi.org/10.1103/PhysRevLett.77.386510.1103/PhysRevLett.77.3865 ↗
58. Fletcher R (1991) A new variational result for quasi-Newton formulae. SIAM J Optim 1(1):18–21. https://doi.org/10.1137/080100210.1137/0801002 ↗
59. Thompson AP, Aktulga HM, Berger R, Bolintineanu DS, Brown WM, Crozier PS, In’t Veld PJ, Kohlmeyer A, Moore SG, Nguyen TD, Shan R, Stevens MJ, Tranchida J, Trott C, Plimpton SJ (2022) LAMMPS—a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. Comput Phys Commun 271:108171. https://doi.org/10.1016/j.cpc.2021.10817110.1016/j.cpc.2021.108171 ↗
60. Mishra S, Yang F, Kundalwal SI (2025) Molecular modeling of hydrogen adsorption and mobility in transition metal-functionalized polycrystalline CNTs using an energy-centered approach. Appl Surf Sci 686:162121. https://doi.org/10.1016/j.apsusc.2024.16212110.1016/j.apsusc.2024.162121 ↗
61. Chen MQ, Quek SS, Sha ZD, Chiu CH, Pei QX, Zhang YW (2015) Effects of grain size, temperature and strain rate on the mechanical properties of polycrystalline graphene—a molecular dynamics study. Carbon 85:135–146. https://doi.org/10.1016/j.carbon.2014.12.09210.1016/j.carbon.2014.12.092 ↗
62. Javvaji B, Raha S, Mahapatra DR (2017) Length-scale and strain rate-dependent mechanism of defect formation and fracture in carbon nanotubes under tensile loading. J Nanopart Res 19(2):37. https://doi.org/10.1007/s11051-016-3735-010.1007/s11051-016-3735-0 ↗
63. Nardelli MB, Yakobson BI, Bernholc J (1998) Brittle and ductile behavior in carbon nanotubes. Phys Rev Lett 81(21):4656–4659. https://doi.org/10.1103/PhysRevLett.81.465610.1103/PhysRevLett.81.4656 ↗
64. Tang C, Guo W, Chen C (2009) Molecular dynamics simulation of tensile elongation of carbon nanotubes: temperature and size effects. Phys Rev B 79(15):155436. https://doi.org/10.1103/PhysRevB.79.15543610.1103/PhysRevB.79.155436 ↗
65. Wang L, Li D, Shi J, Cai K (2020) Strength analysis of a defective diamondene nanoribbon under uni-axial tension. Comput Mater Sci 173:109459. https://doi.org/10.1016/j.commatsci.2019.10945910.1016/j.commatsci.2019.109459 ↗
66. Krishnan A, Dujardin E, Ebbesen TW, Yianilos PN, Treacy MMJ (1998) Young’s modulus of single-walled nanotubes. Phys Rev B 58(20):14013–14019. https://doi.org/10.1103/PhysRevB.58.1401310.1103/PhysRevB.58.14013 ↗
67. Dereli G, Süngü B (2007) Temperature dependence of the tensile properties of single-walled carbon nanotubes: O ( N ) tight-binding molecular-dynamics simulations. Phys Rev B 75(18):184104. https://doi.org/10.1103/PhysRevB.75.18410410.1103/PhysRevB.75.184104 ↗
68. Zhu F, Liao H, Tang K, Chen Y, Liu S (2015) Molecular dynamics study on the effect of temperature on the tensile properties of single-walled carbon nanotubes with a Ni-coating. J Nanomater 2015:1–7. https://doi.org/10.1155/2015/76718210.1155/2015/767182 ↗
69. Bian L, Li H, Cheng Y (2021) Temperature and size-dependent modeling for predicting mechanical properties of carbon nanotubes. Appl Math Model 98:518–536. https://doi.org/10.1016/j.apm.2021.05.02210.1016/j.apm.2021.05.022 ↗
70. Shen L, Li J (2004) Transversely isotropic elastic properties of single-walled carbon nanotubes. Phys Rev B 69(4):45414. https://doi.org/10.1103/PhysRevB.69.04541410.1103/PhysRevB.69.045414 ↗
71. Chen M, Zhan H, Zhu Y, Wu H, Gu Y (2017) Mechanical properties of penta-graphene nanotubes. J Phys Chem C 121(17):9642–9647. https://doi.org/10.1021/acs.jpcc.7b0275310.1021/acs.jpcc.7b02753 ↗