Gromacs

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 87 articles )

Showing results 1 to 20 of 87.
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  1. Gong, Li-Hua; He, Xiang-Tao; Tan, Ru-Chao; Zhou, Zhi-Hong: Single channel quantum color image encryption algorithm based on HSI model and quantum Fourier transform (2018)
  2. Sneha, Patil; Panda, Pritam Kumar; Gharemirshamlu, Fatemeh Rahimi; Bamdad, Kourosh; Balaji, Seetharaman: Structural discordance in HIV-1 Vpu from brain isolate alarms amyloid fibril forming behavior- a computational perspective (2018)
  3. Yang, Jianbin; Zhu, Guanhua; Tong, Dudu; Lu, Lanyuan; Shen, Zuowei: B-spline tight frame based force matching method (2018)
  4. Antonov, M. Yu.; Popinako, A. V.; Prokopiev, G. A.; Vasilyev, A. O.: Numerical modelling of ion transport in 5-HT3 serotonin receptor using molecular dynamics (2017)
  5. Bu, Bing; Li, Dechang; Diao, Jiajie; Ji, Baohua: Mechanics of water pore formation in lipid membrane under electric field (2017)
  6. Farhi, Asaf; Singh, Bipin: A novel method for calculating relative free energy of similar molecules in two environments (2017)
  7. Ghale, Purnima; Kroonblawd, Matthew P.; Mniszewski, Sue; Negre, Christian F. A.; Pavel, Robert; Pino, Sergio; Sardeshmukh, Vivek; Shi, Guangjie; Hahn, Georg: Task-based parallel computation of the density matrix in quantum-based molecular dynamics using graph partitioning (2017)
  8. 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
  9. Michael E. Fortunato, Coray M. Colina: pysimm: A python package for simulation of molecular systems (2017)
  10. Yang, Jianbin; Stahl, Dominik; Shen, Zuowei: An analysis of wavelet frame based scattered data reconstruction (2017)
  11. Banisch, Ralf; Hartmann, Carsten: A sparse Markov chain approximation of LQ-type stochastic control problems (2016)
  12. Fernández-Pendás, Mario; Akhmatskaya, Elena; Sanz-Serna, J. M.: Adaptive multi-stage integrators for optimal energy conservation in molecular simulations (2016)
  13. Gogolinska, Anna; Jakubowski, Rafal; Nowak, Wieslaw: Petri nets formalism facilitates analysis of complex biomolecular structural data (2016)
  14. Preto, Jordane: Classical investigation of long-range coherence in biological systems (2016)
  15. Trȩdak, Przemysław; Rudnicki, Witold R.; Majewski, Jacek A.: Efficient implementation of the many-body reactive bond order (REBO) potential on GPU (2016)
  16. 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)
  17. Hadjidoukas, P. E.; Angelikopoulos, P.; Papadimitriou, C.; Koumoutsakos, P.: $\Pi$4U: a high performance computing framework for Bayesian uncertainty quantification of complex models (2015)
  18. Leimkuhler, Ben; Matthews, Charles: Molecular dynamics. With deterministic and stochastic numerical methods (2015)
  19. Maiolo, M.; Vancheri, A.; Krause, R.; Danani, A.: Wavelets as basis functions to represent the coarse-graining potential in multiscale coarse graining approach (2015)
  20. Michels, Dominik L.; Desbrun, Mathieu: A semi-analytical approach to molecular dynamics (2015)

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Further publications can be found at: http://www.gromacs.org/Gromacs_papers