GROMACS is a versatile package to perform molecular dynamics, i.e. simulate the Newtonian equations of motion for systems with hundreds to millions of particles. It is primarily designed for biochemical molecules like proteins, lipids and nucleic acids that have a lot of complicated bonded interactions, but since GROMACS is extremely fast at calculating the nonbonded interactions (that usually dominate simulations) many groups are also using it for research on non-biological systems, e.g. polymers

References in zbMATH (referenced in 77 articles )

Showing results 1 to 20 of 77.
Sorted by year (citations)

1 2 3 4 next

  1. Bu, Bing; Li, Dechang; Diao, Jiajie; Ji, Baohua: Mechanics of water pore formation in lipid membrane under electric field (2017)
  2. Han Wang, Linfeng Zhang, Jiequn Han, Weinan E: DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics (2017) arXiv
  3. Yang, Jianbin; Stahl, Dominik; Shen, Zuowei: An analysis of wavelet frame based scattered data reconstruction (2017)
  4. Banisch, Ralf; Hartmann, Carsten: A sparse Markov chain approximation of LQ-type stochastic control problems (2016)
  5. Fernández-Pendás, Mario; Akhmatskaya, Elena; Sanz-Serna, J.M.: Adaptive multi-stage integrators for optimal energy conservation in molecular simulations (2016)
  6. Gogolinska, Anna; Jakubowski, Rafal; Nowak, Wieslaw: Petri nets formalism facilitates analysis of complex biomolecular structural data (2016)
  7. Trȩdak, Przemysław; Rudnicki, Witold R.; Majewski, Jacek A.: Efficient implementation of the many-body reactive bond order (REBO) potential on GPU (2016)
  8. Casoni, E.; Jérusalem, A.; Samaniego, C.; Eguzkitza, B.; Lafortune, P.; Tjahjanto, D.D.; Sáez, X.; Houzeaux, G.; Vázquez, M.: Alya: computational solid mechanics for supercomputers (2015)
  9. Hadjidoukas, P.E.; Angelikopoulos, P.; Papadimitriou, C.; Koumoutsakos, P.: $\Pi$4U: a high performance computing framework for Bayesian uncertainty quantification of complex models (2015)
  10. Leimkuhler, Ben; Matthews, Charles: Molecular dynamics. With deterministic and stochastic numerical methods (2015)
  11. Maiolo, M.; Vancheri, A.; Krause, R.; Danani, A.: Wavelets as basis functions to represent the coarse-graining potential in multiscale coarse graining approach (2015)
  12. Michels, Dominik L.; Desbrun, Mathieu: A semi-analytical approach to molecular dynamics (2015)
  13. Nicholas P. Bailey, Trond S. Ingebrigtsen, Jesper Schmidt Hansen, Arno A. Veldhorst, Lasse Bohling, Claire A. Lemarchand, Andreas E. Olsen, Andreas K. Bacher, Lorenzo Costigliola, Ulf R. Pedersen, Heine Larsen, Jeppe C. Dyre, Thomas B. Schroder: RUMD: A general purpose molecular dynamics package optimized to utilize GPU hardware down to a few thousand particles (2015) arXiv
  14. Xue, Xu; Yongjun, Wang; Zhihong, Li: Folding of SAM-II riboswitch explored by replica-exchange molecular dynamics simulation (2015)
  15. Bujotzek, Alexander; Schütt, Ole; Nielsen, Adam; Fackeldey, Konstantin; Weber, Marcus: \ssfZIBgridfree: efficient conformational analysis by partition-of-unity coupling (2014)
  16. Krämer, Andreas; Hülsmann, Marco; Köddermann, Thorsten; Reith, Dirk: Automated parameterization of intermolecular pair potentials using global optimization techniques (2014)
  17. Li, Tong; Gu, YuanTong: A stochastic thermostat algorithm for coarse-grained thermomechanical modeling of large-scale soft matters: theory and application to microfilaments (2014)
  18. Pal, Anirban; Agarwala, Abhishek; Raha, Soumyendu; Bhattacharya, Baidurya: Performance metrics in a hybrid MPI-OpenMP based molecular dynamics simulation with short-range interactions (2014) ioport
  19. Xu, Zhen; Hu, Guo-Hui; Wang, Zhi-Liang; Zhou, Zhe-Wei: Guided motion of short carbon nanotube driven by non-uniform electric field (2014)
  20. Anderson, Joshua A.; Jankowski, Eric; Grubb, Thomas L.; Engel, Michael; Glotzer, Sharon C.: Massively parallel Monte Carlo for many-particle simulations on GPUs (2013)

1 2 3 4 next

Further publications can be found at: