Advertisement

From GROMACS to LAMMPS: GRO2LAM

A converter for molecular dynamics software
  • Hernán Chávez Thielemann
  • Annalisa Cardellini
  • Matteo Fasano
  • Luca Bergamasco
  • Matteo Alberghini
  • Gianmarco Ciorra
  • Eliodoro Chiavazzo
  • Pietro AsinariEmail author
Original Paper
First Online:

Abstract

Atomistic simulations have progressively attracted attention in the study of physical-chemical properties of innovative nanomaterials. GROMACS and LAMMPS are currently the most widespread open-source software for molecular dynamics simulations thanks to their good flexibility, numerous functionalities and responsive community support. Nevertheless, the very different formats adopted for input and output files are limiting the possibility to transfer GROMACS simulations to LAMMPS. In this article, we present GRO2LAM, a modular and open-source Python 2.7 code for rapidly translating input files and parameters from GROMACS to LAMMPS format. The robustness of the tool has been assessed by comparing the simulation results obtained by GROMACS and LAMMPS, after the format conversion by GRO2LAM. Specifically, three nanoscale configurations of interest in both engineering and biomedical fields are studied, namely a carbon nanotube, an iron oxide nanoparticle, and a protein immersed in water. In perspective, GRO2LAM may be the first step to achieve a full interoperability between molecular dynamics software. This would allow to easily exploit their complementary potentialities and post-processing functionalities. Moreover, GRO2LAM could facilitate the cross-check of simulation results, guaranteeing the reproducibility of molecular dynamics models and testing their robustness.

Graphical Abstract

GRO2LAM, a modular and open-source Python code for rapidly translating input files and parameters from GROMACS to LAMMPS format

Keywords

Reproducibility Molecular dynamics GROMACS LAMMPS Conversion 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgments

The authors acknowledge the high-performance computing initiative at Politecnico di Torino (HPC@Polito) and the CINECA Iscra C projects MISURPAC (HP10CJOR5E) and NANOCLUS (HP10CYC6UC) for the availability of high-performance computing resources and support. The authors would also like to acknowledge Dr. Rajat Srivastava for his useful suggestions. The authors declare no competing financial interests.

References

  1. 1.
    Lin S, Shih CJ, Sresht V, Rajan AG, Strano MS, Blankschtein D (2017) Adv Colloid Interface Sci 244:36PubMedCrossRefGoogle Scholar
  2. 2.
    Rajan AG, Silmore KS, Swett J, Robertson AW, Warner JH, Blankschtein D, Strano MS (2019) Nat Mater 18:129CrossRefGoogle Scholar
  3. 3.
    Bigdeli MB, Fasano M (2017) Int J Therm Sci 117:98CrossRefGoogle Scholar
  4. 4.
    Fasano M, Borri D, Chiavazzo E, Asinari P (2016) Appl Therm Eng 101:762CrossRefGoogle Scholar
  5. 5.
    Bigdeli MB, Fasano M, Cardellini A, Chiavazzo E, Asinari P (2016) Renew Sustain Energy Rev 60:1615CrossRefGoogle Scholar
  6. 6.
    Fasano M, Humplik T, Bevilacqua A, Tsapatsis M, Chiavazzo E, Wang EN, Asinari P (2016) Nat Commun 7:12762PubMedPubMedCentralCrossRefGoogle Scholar
  7. 7.
    Ahmadi M, Nowroozi A, Shahlaei M (2015) J Mol Graph Model 61:243PubMedCrossRefGoogle Scholar
  8. 8.
    Cardellini A, Fasano M, Chiavazzo E, Asinari P (2016) Phys Lett A 380(20):1735CrossRefGoogle Scholar
  9. 9.
    Gizzatov A, Key J, Aryal S, Ananta J, Cervadoro A, Palange AL, Fasano M, Stigliano C, Zhong M, Di Mascolo D et al (2014) Adv Funct Mater 24(29):4584PubMedPubMedCentralCrossRefGoogle Scholar
  10. 10.
    Choubey SK, Jeyaraman J (2016) J Mol Graph Model 70:54PubMedCrossRefGoogle Scholar
  11. 11.
    Kamali R, Kharazmi A (2013) Comput Phys Commun 184(10):2316CrossRefGoogle Scholar
  12. 12.
    Cosden IA, Lukes JR (2013) Comput Phys Commun 184(8):1958CrossRefGoogle Scholar
  13. 13.
    Somogyi E, Mansour AA, Ortoleva PJ (2016) Comput Phys Commun 202:337CrossRefGoogle Scholar
  14. 14.
    Neumann P, Flohr H, Arora R, Jarmatz P, Tchipev N, Bungartz HJ (2016) Comput Phys Commun 200:324CrossRefGoogle Scholar
  15. 15.
    Mackay F, Ollila ST, Denniston C (2013) Comput Phys Commun 184(8):2021CrossRefGoogle Scholar
  16. 16.
    Bergamasco L, Izquierdo S, Ammar A (2013) J Non-Newtonian Fluid Mech 201:29CrossRefGoogle Scholar
  17. 17.
    Bergamasco L, Izquierdo S, Pagonabarraga I, Fueyo N (2015) Chem Eng Sci 126:471CrossRefGoogle Scholar
  18. 18.
    Cardellini A, Fasano M, Bigdeli MB, Chiavazzo E, Asinari P (2016) J Phys Condens Matter 28 (48):483003PubMedCrossRefGoogle Scholar
  19. 19.
    Tascini AS, Armstrong J, Chiavazzo E, Fasano M, Asinari P, Bresme F (2017) Phys Chem Chem Phys 19(4):3244PubMedCrossRefGoogle Scholar
  20. 20.
    Morciano M, Fasano M, Nold A, Braga C, Yatsyshin P, Sibley DN, Goddard BD, Chiavazzo E, Asinari P, Kalliadasis S (2017) J Chem Phys 146(24):244507PubMedCrossRefGoogle Scholar
  21. 21.
    Allen P, Tildesley D (1989) Computer simulation of liquids. Oxford Science Publications (Clarendon Press), OxfordGoogle Scholar
  22. 22.
    Frenkel D, Smit B (2001) Understanding molecular simulation: from algorithms to applications, vol 1. Academic Press, New YorkGoogle Scholar
  23. 23.
    Fasano M, Bigdeli MB, Sereshk MRV, Chiavazzo E, Asinari P (2015) Renew Sustain Energy Rev 41:1028CrossRefGoogle Scholar
  24. 24.
    Alder B, Wainwright T (1957) J Chem Phys 27(5):1208CrossRefGoogle Scholar
  25. 25.
    Rahman A (1964) Phys Rev 136(2A):A405CrossRefGoogle Scholar
  26. 26.
    Humphrey W, Dalke A, Schulten K (1996) J Mol Graph 14(1):33PubMedCrossRefGoogle Scholar
  27. 27.
    Phillips JC, Braun R, Wang W, Gumbart J, Tajkhorshid E, Villa E, Chipot C, Skeel RD, Kale L, Schulten K (2005) J Comb Chem 26(16):1781CrossRefGoogle Scholar
  28. 28.
    Pearlman DA, Case DA, Caldwell JW, Ross WS, Cheatham TE, DeBolt S, Ferguson D, Seibel G, Kollman P (1995) Comput Phys Commun 91(1–3):1CrossRefGoogle Scholar
  29. 29.
    Hess B, Kutzner C, Van Der Spoel D, Lindahl E (2008) J Chem Theory Comput 4(3):435PubMedPubMedCentralCrossRefGoogle Scholar
  30. 30.
    Plimpton S (1995) J Comput Phys 117(1):1CrossRefGoogle Scholar
  31. 31.
    Tersoff J (1989) Phys Rev B 39(8):5566CrossRefGoogle Scholar
  32. 32.
    Sun H (1998) J Phys Chem B 102(38):7338CrossRefGoogle Scholar
  33. 33.
    Vermaas JV, Hardy DJ, Stone JE, Tajkhorshid E, Kohlmeyer A (2016) J Chem Inf Model 56 (6):1112PubMedPubMedCentralCrossRefGoogle Scholar
  34. 34.
    Rusu VH, Horta VA, Horta BA, Lins RD, Baron R (2014) J Mol Graph Model 48:80PubMedCrossRefGoogle Scholar
  35. 35.
    Smith W, Yong C, Rodger P (2002) Mol Simul 28(5):385CrossRefGoogle Scholar
  36. 36.
    Yong CW (2016) Descriptions and implementations of dl_f notation: a natural chemical expression system of atom types for molecular simulations. J Chem Inf Model 56(8):1405.  https://doi.org/10.1021/acs.jcim.6b00323 PubMedCrossRefGoogle Scholar
  37. 37.
    Shirts MR, Klein C, Swails JM, Yin J, Gilson MK, Mobley DL, Case DA, Zhong ED (2017) J Comput Aided Mol Des 31(1): 147PubMedCrossRefGoogle Scholar
  38. 38.
    Buckingham RA (1938) Philos Trans R Soc A: Math Phys Eng Sci 168:264–283. The Royal SocietyGoogle Scholar
  39. 39.
    Berendsen H, Grigera J, Straatsma T (1987) J Phys Chem 91(24):6269CrossRefGoogle Scholar
  40. 40.
    Nosé S (1984) J Chem Phys 81(1):511CrossRefGoogle Scholar
  41. 41.
    Hoover WG (1985) Phys Rev A 31(3):1695CrossRefGoogle Scholar
  42. 42.
    Parrinello M, Rahman A (1981) J Appl Phys 52(12):7182CrossRefGoogle Scholar
  43. 43.
    Ryckaert JP, Ciccotti G, Berendsen HJ (1977) J Comput Phys 23(3):327CrossRefGoogle Scholar
  44. 44.
    Fasano M, Chiavazzo E, Asinari P (2014) Nanoscale Res Lett 9(1):1CrossRefGoogle Scholar
  45. 45.
    Razavi SS, Hashemianzadeh SM, Karimi H (2011) J Mol Model 17(5):1163PubMedCrossRefGoogle Scholar
  46. 46.
    Salazar-Salinas K, Kubli-Garfias C, Seminario JM (2013) J Mol Model 19(7):2797PubMedCrossRefGoogle Scholar
  47. 47.
    Fasano M, Crisafulli A, Cardellini A, Bergamasco L, Chiavazzo E, Asinari P (2018) Mol Simul 45(4–5):417Google Scholar
  48. 48.
    Chiavazzo E, Fasano M, Asinari P, Decuzzi P (2014) Nat Commun 5:3565PubMedCentralCrossRefGoogle Scholar
  49. 49.
    Darden T, York D, Pedersen L (1993) J Chem Phys 98(12):10089CrossRefGoogle Scholar
  50. 50.
    Hockney R, Eastwood J (1988) Computer simulation using particles. CRC Press, Boca RatonCrossRefGoogle Scholar
  51. 51.
    Falk M, Issels R (2001) Int J Hyperth 17(1):1CrossRefGoogle Scholar
  52. 52.
    Bergamasco L, Alberghini M, Fasano M, Cardellini A, Chiavazzo E, Asinari P (2018) Entropy 20(2):126CrossRefGoogle Scholar
  53. 53.
    Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) Nucleic Acids Res 28(1):235PubMedPubMedCentralCrossRefGoogle Scholar
  54. 54.
    Kaminski GA, Friesner RA, Tirado-Rives J, Jorgensen WL (2001) J Phys Chem B 105(28):6474CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of EnergyPolitecnico di TorinoTorinoItaly

Personalised recommendations

Cite article

Buy options