AMBER

AmberTools is a set of programs for biomolecular simulation and analysis. They are designedto work well with each other, and with the “regular” Amber suite of programs. You can performmany simulation tasks with AmberTools, and you can do more extensive simulations with thecombination of AmberTools and Amber itself.


References in zbMATH (referenced in 46 articles , 1 standard article )

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  1. Younes Nejahi; Mohammad Soroush Barhaghi; Jason Mick; Brock Jackman; Kamel Rushaidat; Yuanzhe Li; Loren Schwiebert; Jeffrey Potoff: GOMC: GPU Optimized Monte Carlo for the simulation of phase equilibria and physical properties of complex fluids (2019) not zbMATH
  2. Hinze, Thomas: The Java environment for nature-inspired approaches (JENA): a workbench for biocomputing and biomodelling enthusiasts (2018)
  3. Chen, Duan: Fractional Poisson-Nernst-Planck model for ion channels. I: Basic formulations and algorithms (2017)
  4. Griebel, Michael (ed.); Schüller, Anton (ed.); Schweitzer, Marc Alexander (ed.): Scientific computing and algorithms in industrial simulations. Projects and products of Fraunhofer SCAI (2017)
  5. Michael E. Fortunato, Coray M. Colina: pysimm: A python package for simulation of molecular systems (2017) not zbMATH
  6. Kulik, Marta; Trylska, Joanna: Structural and energetic comparison of the complexes of aminoglycosides with the model of the ribosomal A-site (2016)
  7. Leimkuhler, Benedict; Shang, Xiaocheng: Adaptive thermostats for noisy gradient systems (2016)
  8. Preto, Jordane: Classical investigation of long-range coherence in biological systems (2016)
  9. Trȩdak, Przemysław; Rudnicki, Witold R.; Majewski, Jacek A.: Efficient implementation of the many-body reactive bond order (REBO) potential on GPU (2016)
  10. 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
  11. Viricel, Clément; Simoncini, David; Allouche, David; De Givry, Simon; Barbe, Sophie; Schiex, Thomas: Approximate counting with deterministic guarantees for affinity computation (2015)
  12. Yang, Edward Z.; Campagna, Giovanni; Ağacan, Ömer S.; El-Hassany, Ahmed; Kulkarni, Abhishek; Newton, Ryan R.: Efficient communication and collection with compact normal forms (2015)
  13. 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
  14. Poursina, Mohammad; Anderson, Kurt S.: An improved fast multipole method for electrostatic potential calculations in a class of coarse-grained molecular simulations (2014)
  15. Anderson, Joshua A.; Jankowski, Eric; Grubb, Thomas L.; Engel, Michael; Glotzer, Sharon C.: Massively parallel Monte Carlo for many-particle simulations on GPUs (2013)
  16. Geng, Weihua; Jacob, Ferosh: A GPU-accelerated direct-sum boundary integral Poisson-Boltzmann solver (2013)
  17. Oden, J. Tinsley; Prudencio, Ernesto E.; Bauman, Paul T.: Virtual model validation of complex multiscale systems: applications to nonlinear elastostatics (2013)
  18. Votapka, Lane W.; Czapla, Luke; Zhenirovskyy, Maxim; Amaro, Rommie E.: DelEnsembleElec: computing ensemble-averaged electrostatics using DelPhi (2013)
  19. Chen, Duan; Chen, Zhan; Wei, Guo-Wei: Quantum dynamics in continuum for proton transport. II: Variational solvent-solute interface (2012)
  20. Bisson, Mauro; Bernaschi, Massimo; Melchionna, Simone: Parallel molecular dynamics with irregular domain decomposition (2011)

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