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 125 articles , 1 standard article )

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  1. Ataei, Mohammadmehdi; Pirmorad, Erfan; Costa, Franco; Han, Sejin; Park, Chul B.; Bussmann, Markus: A hybrid lattice Boltzmann-molecular dynamics-immersed boundary method model for the simulation of composite foams (2022)
  2. Mongelli, Guy Francis: Molecular dynamics simulations. Key operations in GROMACS (to appear) (2022)
  3. Sha’bani, Farzin; Rash-Ahmadi, Samrand: Length scale effect on the buckling behavior of a graphene sheets using modified couple stress theory and molecular dynamics method (2022)
  4. Bittracher, Andreas; Klus, Stefan; Hamzi, Boumediene; Koltai, Péter; Schütte, Christof: Dimensionality reduction of complex metastable systems via kernel embeddings of transition manifolds (2021)
  5. Jin, Shi; Li, Lei; Xu, Zhenli; Zhao, Yue: A random batch Ewald method for particle systems with Coulomb interactions (2021)
  6. Luciano G. Silvestri, Lucas J. Stanek, Gautham Dharuman, Yongjun Choi, Michael S. Murillo: Sarkas: A Fast Pure-Python Molecular Dynamics Suite for Plasma Physics (2021) arXiv
  7. T. L. Underwood, J. A. Purton, J. R. H. Manning, A. V. Brukhno, K. Stratford, T. Düren, N. B. Wilding, S. C. Parker: dlmontepython: A Python library for automation and analysis of Monte Carlo molecular simulations (2021) arXiv
  8. Younes Nejahi, Mohammad Soroush Barhaghi, Gregory Schwing, Loren Schwiebert, Jeffrey Potoff: Update 2.70 to GOMC: GPU Optimized Monte Carlo for the simulation of phase equilibria and physical properties of complex fluids (2021) not zbMATH
  9. Delbary, Fabrice; Hanke, Martin; Ivanizki, Dmitry: A generalized Newton iteration for computing the solution of the inverse Henderson problem (2020)
  10. Grogan, Francesca; Lei, Huan; Li, Xiantao; Baker, Nathan A.: Data-driven molecular modeling with the generalized Langevin equation (2020)
  11. Teixeira, J. M.: taurenmd: A command-line interface for analysis of Molecular Dynamics simulations (2020) not zbMATH
  12. Zhao, Yu; Liang, Rong; Yang, Yiying; Lin, Songyi: The mechanism of pulsed electric field (PEF) targeting location on the spatial conformation of pine nut peptide (2020)
  13. Agrahari, Ashish Kumar; Doss, George Priya C.; Siva, R.; Magesh, R.; Zayed, Hatem: Molecular insights of the G2019S substitution in LRRK2 kinase domain associated with Parkinson’s disease: A molecular dynamics simulation approach (2019)
  14. Anupama, Rani; Lulu, Sajitha; Madhusmita, Rout; Vino, Sundararajan; Mukherjee, Amitava; Babu, Subramanian: Insights into the interaction of key biofilm proteins in \textitPseudomonasaeruginosa PAO1 with TiO(_2) nanoparticle: an \textitinsilico analysis (2019)
  15. Bai, Xiaolu; Chen, Xiaolin: Rational design, conformational analysis and membrane-penetrating dynamics study of Bac2A-derived antimicrobial peptides against gram-positive clinical strains isolated from pyemia (2019)
  16. Hopp-Hirschler, Manuel; Baz, Jörg; Hansen, Niels; Nieken, Ulrich: Generalized Fickian approach for phase separating fluid mixtures in smoothed particle hydrodynamics (2019)
  17. Hoshi, Takeo; Imachi, Hiroto; Kuwata, Akiyoshi; Kakuda, Kohsuke; Fujita, Takatoshi; Matsui, Hiroyuki: Numerical aspect of large-scale electronic state calculation for flexible device material (2019)
  18. Lei, Huan; Li, Jing; Gao, Peiyuan; Stinis, Panagiotis; Baker, Nathan A.: A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness (2019)
  19. Long, Andrew W.; Ferguson, Andrew L.: Landmark diffusion maps (L-dMaps): accelerated manifold learning out-of-sample extension (2019)
  20. Motamedi, Mohsen; Sohail, Ayesha: A theoretical framework for the biophysics of tubulins (2019)

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