W. Humphrey, A. Dalke, and K. Schulten, VMD: Visual molecular dynamics, Journal of Molecular Graphics, vol.14, issue.1, pp.33-38, 1996.
DOI : 10.1016/0263-7855(96)00018-5

A. Gavezzotti, Molecular aggregation: structure analysis and molecular simulation of crystals and liquids, p.15, 2007.
DOI : 10.1093/acprof:oso/9780198570806.001.0001

S. Jones and J. M. Thornton, Principles of protein-protein interactions., Proceedings of the National Academy of Sciences, vol.93, issue.1, pp.13-20, 1996.
DOI : 10.1073/pnas.93.1.13

L. R. Forrest and M. S. Sansom, Membrane simulations: bigger and better? Current opinion in structural biology, pp.174-181, 2000.

I. Y. Gushchin, V. I. Gordeliy, and S. Grudinin, Role of the HAMP Domain Region of Sensory Rhodopsin Transducers in Signal Transduction, Biochemistry, vol.50, issue.4, pp.574-580, 2010.
DOI : 10.1021/bi101032a
URL : https://hal.archives-ouvertes.fr/hal-00784550

V. Galiatsatos, Molecular simulation methods for predicting polymer properties, p.15, 2005.

T. Schlick, Molecular modeling and simulation: an interdisciplinary guide, p.15, 2010.
DOI : 10.1007/978-1-4419-6351-2

P. Paricaud, M. P?edota, A. A. Chialvo, and P. T. Cummings, From dimer to condensed phases at extreme conditions: Accurate predictions of the properties of water by a Gaussian charge polarizable model, The Journal of Chemical Physics, vol.122, issue.24, pp.244511-244526, 2005.
DOI : 10.1063/1.1940033
URL : https://hal.archives-ouvertes.fr/hal-01153443

F. Ooms, Molecular Modeling and Computer Aided Drug Design. Examples of their Applications in Medicinal Chemistry, Current Medicinal Chemistry, vol.7, issue.2, pp.141-158, 2000.
DOI : 10.2174/0929867003375317

C. A. Taft, V. B. Da-silva, and C. H. Da-silva, Current topics in computer???aided drug design, Journal of Pharmaceutical Sciences, vol.97, issue.3, pp.1089-1098, 2008.
DOI : 10.1002/jps.21293

A. Wlodawer and J. Vondrasek, INHIBITORS OF HIV-1 PROTEASE: A Major Success of Structure-Assisted Drug Design 1. Annual review of biophysics and biomolecular structure, pp.249-284, 1998.

H. Alonso, A. A. Bliznyuk, and J. E. Gready, Combining docking and molecular dynamic simulations in drug design. Medicinal research reviews, pp.531-568, 2006.

G. A. Mansoori, Principles of nanotechnology: molecular-based study of condensed matter in small systems, p.15, 2005.
DOI : 10.1142/5749

G. Cuniberti, G. Fagas, and K. Richter, Introducing molecular electronics, p.15, 2005.

C. B. Anfinsen, Principles that Govern the Folding of Protein Chains, Science, vol.181, issue.4096, pp.223-230, 1973.
DOI : 10.1126/science.181.4096.223

K. M. Merz, M. Scott, and L. Grand, The Protein Folding Problem and Tertiary Structure Prediction, Birkhäuser, vol.15, issue.1, p.89, 1994.
DOI : 10.1007/978-1-4684-6831-1

A. R. Leach, Molecular modelling. Longman Singapore, p.89, 1996.

R. Mackinnon, Potassium channels, FEBS Letters, vol.298, issue.1, pp.62-65, 2003.
DOI : 10.1016/S0014-5793(03)01104-9

R. Featherstone, A Divide-and-Conquer Articulated-Body Algorithm for Parallel O(log(n)) Calculation of Rigid-Body Dynamics. Part 1: Basic Algorithm, The International Journal of Robotics Research, vol.18, issue.9, pp.867-875, 1999.
DOI : 10.1177/02783649922066619

R. Featherstone, A Divide-and-Conquer Articulated-Body Algorithm for Parallel O(log(n)) Calculation of Rigid-Body Dynamics. Part 2: Trees, Loops, and Accuracy, The International Journal of Robotics Research, vol.18, issue.9, pp.876-892, 1920.
DOI : 10.1177/02783649922066628

G. J. Martyna, M. L. Klein, and M. Tuckerman, Nos?????Hoover chains: The canonical ensemble via continuous dynamics, The Journal of Chemical Physics, vol.97, issue.4, p.2635, 1992.
DOI : 10.1063/1.463940

A. Z. Panagiotopoulos, Direct determination of phase coexistence properties of fluids by Monte Carlo simulation in a new ensemble, Molecular Physics, vol.5, issue.4, pp.813-826, 1987.
DOI : 10.1080/00268978200101841

J. W. Ponder and D. A. Case, Force fields for protein simulations Advances in protein chemistry, pp.27-85, 2003.

S. Chib and E. Greenberg, Understanding the metropolis-hastings algorithm. The American Statistician, pp.327-335, 1995.

J. S. Liu, F. Liang, and W. H. Wong, The Multiple-Try Method and Local Optimization in Metropolis Sampling, Journal of the American Statistical Association, vol.95, issue.449, pp.121-134, 2000.
DOI : 10.1080/01621459.2000.10473908

J. I. Siepmann and D. Frenkel, Configurational bias Monte Carlo: a new sampling scheme for flexible chains, Molecular Physics, vol.75, issue.1, pp.59-70, 1992.
DOI : 10.1063/1.1730021

J. P. Ryckaert, G. Ciccotti, and H. J. Berendsen, Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes, Journal of Computational Physics, vol.23, issue.3, pp.327-341, 1977.
DOI : 10.1016/0021-9991(77)90098-5

H. C. Andersen, Rattle: A ???velocity??? version of the shake algorithm for molecular dynamics calculations, Journal of Computational Physics, vol.52, issue.1, pp.24-34, 1983.
DOI : 10.1016/0021-9991(83)90014-1

A. C. Van-duin, S. Dasgupta, F. Lorant, W. A. Goddard, and I. , ReaxFF:?? A Reactive Force Field for Hydrocarbons, The Journal of Physical Chemistry A, vol.105, issue.41, pp.9396-9409, 2001.
DOI : 10.1021/jp004368u

D. W. Brenner, Empirical potential for hydrocarbons for use in simulating the chemical vapor deposition of diamond films, Physical Review B, vol.42, issue.15, pp.9458-91, 1990.
DOI : 10.1103/PhysRevB.42.9458

A. K. Rappe, C. J. Casewit, K. S. Colwell, W. A. Goddard, I. et al., UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations, Journal of the American Chemical Society, vol.114, issue.25, pp.10024-10035, 1992.
DOI : 10.1021/ja00051a040

P. V. Schleyer, A. D. Mackerell, J. R. Brooks, C. L. Brooks, I. et al., CHARMM: The Energy Function and Its Parameterization with an Overview of the Program, The Encyclopedia of Computational Chemistry, vol.1, issue.20, pp.271-277, 1998.

H. Lin and D. G. Truhlar, QM/MM: what have we learned, where are we, and where do we go from here? Theoretical Chemistry Accounts: Theory, Computation, and Modeling, Theoretica Chimica Acta), vol.117, issue.6, pp.185-199, 2007.

L. Yang, C. Tan, M. J. Hsieh, J. Wang, Y. Duan et al., New-Generation Amber United-Atom Force Field, The Journal of Physical Chemistry B, vol.110, issue.26, pp.13166-13176, 2006.
DOI : 10.1021/jp060163v

W. L. Jorgensen, J. D. Madura, and C. J. Swenson, Optimized intermolecular potential functions for liquid hydrocarbons, Journal of the American Chemical Society, vol.106, issue.22, pp.6638-6646, 1984.
DOI : 10.1021/ja00334a030

M. G. Martin and J. I. Siepmann, -Alkanes, The Journal of Physical Chemistry B, vol.102, issue.14, pp.2569-2577, 1998.
DOI : 10.1021/jp972543+
URL : https://hal.archives-ouvertes.fr/edutice-00109613

A. Jain, N. Vaidehi, and G. Rodriguez, A fast recursive algorithm for molecular dynamics simulation, Journal of Computational Physics, vol.106, issue.2, pp.258-268, 1920.
DOI : 10.1016/S0021-9991(83)71106-X

R. Rossi, M. Isorce, S. Morin, J. Flocard, K. Arumugam et al., Adaptive torsion-angle quasi-statics: a general simulation method with applications to protein structure analysis and design, Bioinformatics, vol.23, issue.13, pp.408-435, 2007.
DOI : 10.1093/bioinformatics/btm191
URL : https://hal.archives-ouvertes.fr/inria-00390312

D. S. Bae and E. J. Haug, A Recursive Formulation for Constrained Mechanical System Dynamics: Part I. Open Loop Systems, Mechanics of Structures and Machines, vol.15, issue.3, pp.359-382, 1920.
DOI : 10.1177/027836498300200301

P. Güntert, C. Mumenthaler, and K. Wüthrich, Torsion angle dynamics for NMR structure calculation with the new program Dyana, Journal of Molecular Biology, vol.273, issue.1, pp.283-298, 1920.
DOI : 10.1006/jmbi.1997.1284

R. Abagyan, M. Totrov, and D. Kuznetsov, ICM?A new method for protein modeling and design: Applications to docking and structure prediction from the distorted native conformation, Journal of Computational Chemistry, vol.8, issue.5, pp.488-506, 1920.
DOI : 10.1002/jcc.540150503

A. K. Mazur and R. A. Abagyan, New Methodology for Computer-Aided Modelling of Biomolecular Structure and Dynamics 1. Non-Cyclic Structures, Journal of Biomolecular Structure and Dynamics, vol.51, issue.4, pp.815-832, 1989.
DOI : 10.1093/protein/1.4.275

A. K. Mazur, V. E. Dorofeev, and R. A. Abagyan, Derivation and testing of explicit equations of motion for polymers described by internal coordinates, Journal of Computational Physics, vol.92, issue.2, pp.261-272, 1920.
DOI : 10.1016/0021-9991(91)90210-C

S. He and H. A. Scheraga, Macromolecular conformational dynamics in torsional angle space, The Journal of Chemical Physics, vol.108, issue.1, pp.271-286, 1998.
DOI : 10.1063/1.475378

V. Katritch, M. Totrov, and R. Abagyan, ICFF: A new method to incorporate implicit flexibility into an internal coordinate force field, Journal of Computational Chemistry, vol.212, issue.2, pp.254-265, 1920.
DOI : 10.1002/jcc.10091

T. Schlick, E. Barth, and M. Mandziuk, Biomolecular dynamics at long timesteps: Bridging the timescale gap between simulation and experimentation. Annual review of biophysics and biomolecular structure, pp.181-222, 1920.

S. J. Marrink, H. J. Risselada, S. Yefimov, D. P. Tieleman, A. H. De et al., The MARTINI Force Field:?? Coarse Grained Model for Biomolecular Simulations, The Journal of Physical Chemistry B, vol.111, issue.27, pp.7812-7824, 2007.
DOI : 10.1021/jp071097f

S. Mostaghim, M. Hoffmann, P. H. Konig, T. Frauenheim, and J. Teich, Molecular force field parametrization using multi-objective evolutionary algorithms, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753), pp.212-219, 2004.
DOI : 10.1109/CEC.2004.1330859
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.161.3071

K. Voltz, J. Trylska, V. Tozzini, V. Kurkal-siebert, J. Langowski et al., Coarse-grained force field for the nucleosome from self-consistent multiscaling, Journal of Computational Chemistry, vol.114, issue.9, pp.1429-1439, 2008.
DOI : 10.1002/jcc.20902

A. Liwo, Y. He, and H. A. Scheraga, Coarse-grained force field: general folding theory, Physical Chemistry Chemical Physics, vol.114, issue.Suppl. 3, pp.16890-16901, 2011.
DOI : 10.1039/c1cp20752k
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3362049

P. J. Hoogerbrugge and J. Koelman, Simulating Microscopic Hydrodynamic Phenomena with Dissipative Particle Dynamics, Europhysics Letters (EPL), vol.19, issue.3, p.155, 1992.
DOI : 10.1209/0295-5075/19/3/001

P. Espanol and P. Warren, Statistical Mechanics of Dissipative Particle Dynamics, Europhysics Letters (EPL), vol.30, issue.4, pp.191-196, 1995.
DOI : 10.1209/0295-5075/30/4/001

M. Praprotnik, L. D. Site, and K. Kremer, Multiscale Simulation of Soft Matter: From Scale Bridging to Adaptive Resolution, Annual Review of Physical Chemistry, vol.59, issue.1, pp.545-571, 2008.
DOI : 10.1146/annurev.physchem.59.032607.093707

S. O. Nielsen, R. E. Bulo, P. B. Moore, and B. Ensing, Recent progress in adaptive multiscale molecular dynamics simulations of soft matter, Physical Chemistry Chemical Physics, vol.5, issue.39, pp.12401-12414, 2010.
DOI : 10.1039/c004111d

J. H. Park and A. Heyden, Solving the equations of motion for mixed atomistic and coarse-grained systems, Molecular Simulation, vol.105, issue.10-11, pp.962-973, 2009.
DOI : 10.1021/ct700324x

M. Praprotnik, S. Matysiak, L. D. Site, K. Kremer, and C. Clementi, Adaptive resolution simulation of liquid water, Journal of Physics: Condensed Matter, vol.19, issue.29, p.292201, 2007.
DOI : 10.1088/0953-8984/19/29/292201

S. O. Nielsen, P. B. Moore, and B. Ensing, Adaptive multiscale molecular dynamics of macromolecular fluids. Physical review letters, p.237802, 2010.

B. Ensing, S. O. Nielsen, P. B. Moore, M. L. Klein, and M. Parrinello, Energy Conservation in Adaptive Hybrid Atomistic/Coarse-Grain Molecular Dynamics, Journal of Chemical Theory and Computation, vol.3, issue.3, pp.1100-1105, 2007.
DOI : 10.1021/ct600323n

M. Christen and W. F. Van-gunsteren, Multigraining: An algorithm for simultaneous fine-grained and coarse-grained simulation of molecular systems, The Journal of Chemical Physics, vol.124, issue.15, pp.154106-154127, 2006.
DOI : 10.1063/1.2187488

J. Q. Broughton, F. F. Abraham, N. Bernstein, and E. Kaxiras, Concurrent coupling of length scales: Methodology and application, Physical Review B, vol.60, issue.4, pp.2391-2412, 1999.
DOI : 10.1103/PhysRevB.60.2391

A. Nakano, M. E. Bachlechner, R. K. Kalia, E. Lidorikis, P. Vashishta et al., Multiscale simulation of nanosystems, Computing in Science & Engineering, vol.3, issue.4, pp.56-66, 2001.
DOI : 10.1109/5992.931904

S. Ogata, E. Lidorikis, F. Shimojo, A. Nakano, P. Vashishta et al., Hybrid finite-element/molecular-dynamics/electronic-density-functional approach to materials simulations on parallel computers, Computer Physics Communications, vol.138, issue.2, pp.143-154, 2001.
DOI : 10.1016/S0010-4655(01)00203-X

D. D. Marsh, Molecular dynamics-Lattice Boltzmann hybrid method on graphics processors, p.21, 2010.

J. B. Bell, A. L. Garcia, and S. A. Williams, Computational fluctuating fluid dynamics, ESAIM: Mathematical Modelling and Numerical Analysis, vol.44, issue.5, pp.1085-1105, 2010.
DOI : 10.1051/m2an/2010053
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.392.8999

G. Giupponi, G. De-fabritiis, and P. V. Coveney, Hybrid method coupling fluctuating hydrodynamics and molecular dynamics for the simulation of macromolecules, The Journal of Chemical Physics, vol.126, issue.15, pp.154903-154924, 2007.
DOI : 10.1063/1.2720385

O. M. Becker, A. D. Jr, B. Roux, and M. Watanabe, Computational biochemistry and biophysics, p.21, 2001.
DOI : 10.1201/9780203903827

M. B. Ulmschneider, J. P. Ulmschneider, M. S. Sansom, and A. D. Nola, A Generalized Born Implicit-Membrane Representation Compared to Experimental Insertion Free Energies, Biophysical Journal, vol.92, issue.7, pp.2338-2349, 2007.
DOI : 10.1529/biophysj.106.081810

L. Greengard and V. Rokhlin, A fast algorithm for particle simulations, Journal of Computational Physics, vol.73, issue.2, pp.325-348, 1987.
DOI : 10.1016/0021-9991(87)90140-9

T. Darden, D. York, and L. Pedersen, ) method for Ewald sums in large systems, The Journal of Chemical Physics, vol.98, issue.12, pp.10089-10089, 1993.
DOI : 10.1063/1.464397

S. Grudinin and S. Redon, Practical modeling of molecular systems with symmetries, Journal of Computational Chemistry, vol.375, issue.9, pp.1799-1814, 2010.
DOI : 10.1002/jcc.21434
URL : https://hal.archives-ouvertes.fr/hal-00748028

M. Tuckerman, B. J. Berne, and G. J. Martyna, Reversible multiple time scale molecular dynamics, The Journal of Chemical Physics, vol.97, issue.3, p.130, 1990.
DOI : 10.1063/1.463137

S. Redon and M. C. Lin, An efficient, error-bounded approximation algorithm for simulating quasi-statics of complex linkages, Computer-Aided Design, vol.38, issue.4, pp.300-314, 2006.
DOI : 10.1016/j.cad.2006.01.009
URL : https://hal.archives-ouvertes.fr/hal-01148355

S. Redon, N. Galoppo, and M. C. Lin, Adaptive dynamics of articulated bodies, ACM Transactions on Graphics, vol.24, issue.3, pp.936-945, 2005.
DOI : 10.1145/1073204.1073294
URL : https://hal.archives-ouvertes.fr/inria-00390315

M. Bosson, S. Grudinin, X. Bouju, and S. Redon, Interactive physically-based structural modeling of hydrocarbon systems, Journal of Computational Physics, vol.231, issue.6, pp.2581-2598, 2012.
DOI : 10.1016/j.jcp.2011.12.006
URL : https://hal.archives-ouvertes.fr/hal-00755542

H. Yamashita, S. Endo, H. Wako, and A. Kidera, Sampling efficiency of molecular dynamics and Monte Carlo method in protein simulation, Chemical Physics Letters, vol.342, issue.3-4, pp.382-386, 2001.
DOI : 10.1016/S0009-2614(01)00613-3

W. L. Jorgensen and J. Tirado-rives, Monte Carlo vs Molecular Dynamics for Conformational Sampling, The Journal of Physical Chemistry, vol.100, issue.34, pp.14508-14513, 1996.
DOI : 10.1021/jp960880x

J. P. Ulmschneider, M. B. Ulmschneider, and A. D. Nola, Monte Carlo vs Molecular Dynamics for All-Atom Polypeptide Folding Simulations, The Journal of Physical Chemistry B, vol.110, issue.33, pp.16733-16742, 2006.
DOI : 10.1021/jp061619b

B. M. Forrest and U. W. Suter, Generalized coordinate hybrid Monte Carlo, Molecular Physics, vol.242, issue.2, pp.393-410, 1994.
DOI : 10.1080/00268979400100304

Y. Sugita and Y. Okamoto, Replica-exchange molecular dynamics method for protein folding, Chemical Physics Letters, vol.314, issue.1-2, pp.141-151, 1999.
DOI : 10.1016/S0009-2614(99)01123-9

C. Giardina, J. Kurchan, V. Lecomte, and J. Tailleur, Simulating Rare Events in Dynamical Processes, Journal of Statistical Physics, vol.287, issue.4, pp.787-811, 2011.
DOI : 10.1007/s10955-011-0350-4
URL : https://hal.archives-ouvertes.fr/hal-00604423

E. Hairer, C. Lubich, and G. Wanner, Geometric numerical integration: structurepreserving algorithms for ordinary differential equations, of Springer Series in Computational Mathematics, pp.23-94, 2006.

J. M. Sanz-serna, Symplectic integrators for Hamiltonian problems: an overview, Acta Numerica, vol.432, issue.9, pp.243-286123, 1992.
DOI : 10.1016/0021-8928(84)90078-9

J. A. Izaguirre, S. Reich, and R. D. Skeel, Longer time steps for molecular dynamics, The Journal of Chemical Physics, vol.110, issue.20, pp.9853-9876, 1999.
DOI : 10.1063/1.478995

C. H. Bennett, Mass tensor molecular dynamics, Journal of Computational Physics, vol.19, issue.3, pp.267-279, 1975.
DOI : 10.1016/0021-9991(75)90077-7

K. A. Feenstra, B. Hess, and H. J. Berendsen, Improving efficiency of large time-scale molecular dynamics simulations of hydrogen-rich systems, Journal of Computational Chemistry, vol.22, issue.8, pp.786-798, 1999.
DOI : 10.1002/(SICI)1096-987X(199906)20:8<786::AID-JCC5>3.0.CO;2-B

M. Preto and S. Tremaine, A Class of Symplectic Integrators with Adaptive Time Step for Separable Hamiltonian Systems, The Astronomical Journal, vol.118, issue.5, pp.2532-2555, 1999.
DOI : 10.1086/301102

F. Rao and M. Spichty, Thermodynamics and kinetics of large-time-step molecular dynamics, Journal of Computational Chemistry, vol.26, issue.5, pp.475-483, 2012.
DOI : 10.1002/jcc.21990

P. Plechac and M. Rousset, Implicit Mass-matrix Penalization of Hamiltonian Dynamics with Application to Exact Sampling of Stiff Systems, Multiscale Modeling & Simulation, vol.8, issue.2, pp.498-539, 2010.
DOI : 10.1137/08072348X

A. Gunaratne, A penalty function method for constrained molecular dynamics, p.24, 2006.

S. Reich, Modified potential energy functions for constrained molecular dynamics, Numerical Algorithms, vol.19, issue.1/4, pp.213-221, 1998.
DOI : 10.1023/A:1019198205349

A. F. Voter, Hyperdynamics: Accelerated Molecular Dynamics of Infrequent Events, Physical Review Letters, vol.78, issue.20, pp.3908-3911, 1997.
DOI : 10.1103/PhysRevLett.78.3908

A. Roitberg and R. Elber, Modeling side chains in peptides and proteins: Application of the locally enhanced sampling and the simulated annealing methods to find minimum energy conformations, The Journal of Chemical Physics, vol.95, issue.12, pp.9277-89, 1991.
DOI : 10.1063/1.461157

A. Z. Panagiotopoulos, Monte Carlo methods for phase equilibria of fluids, Journal of Physics: Condensed Matter, vol.12, issue.3, pp.25-49, 2000.
DOI : 10.1088/0953-8984/12/3/201

A. H. Widmann and U. W. Suter, Parallelization of a Monte Carlo algorithm for the simulation of polymer melts, Computer Physics Communications, vol.92, issue.2-3, pp.229-251, 1995.
DOI : 10.1016/0010-4655(95)00092-0

E. Marinari and G. Parisi, Simulated Tempering: A New Monte Carlo Scheme, Europhysics Letters (EPL), vol.19, issue.6, pp.451-475, 1992.
DOI : 10.1209/0295-5075/19/6/002

N. C. Karayiannis, V. G. Mavrantzas, and D. N. Theodorou, A novel Monte Carlo scheme for the rapid equilibration of atomistic model polymer systems of precisely defined molecular architecture. Physical review letters, pp.105503-105527, 2002.

A. F. Voter, INTRODUCTION TO THE KINETIC MONTE CARLO METHOD, Radiation Effects in Solids, vol.235, issue.10, pp.1-23, 2007.
DOI : 10.1007/978-1-4020-5295-8_1

C. R. Sweet, S. S. Hampton, R. D. Skeel, and J. A. Izaguirre, A separable shadow Hamiltonian hybrid Monte Carlo method, The Journal of Chemical Physics, vol.131, issue.17, pp.131174106-131174130, 2009.
DOI : 10.1063/1.3253687

D. Hamelberg, J. Mongan, and J. A. Mccammon, Accelerated molecular dynamics: A promising and efficient simulation method for biomolecules, The Journal of Chemical Physics, vol.120, issue.24, pp.11919-11943, 2004.
DOI : 10.1063/1.1755656

S. Izrailev, S. Stepaniants, B. Isralewitz, D. Kosztin, H. Lu et al., Steered molecular dynamics. In Computational molecular dynamics: challenges, methods, ideas, pp.39-65, 1998.
DOI : 10.1007/978-3-642-58360-5_2
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.91.619

T. Lelievre, G. Stoltz, and M. Rousset, Free energy computations: A mathematical perspective, p.95, 2010.
DOI : 10.1142/p579

C. Shyu and F. M. Ytreberg, Accurate estimation of solvation free energy using polynomial fitting techniques, Journal of Computational Chemistry, vol.24, issue.1, pp.134-141, 2011.
DOI : 10.1002/jcc.21609

B. Lin, K. Y. Wong, C. Hu, H. Kokubo, and B. M. Pettitt, Fast Calculations of Electrostatic Solvation Free Energy from Reconstructed Solvent Density Using Proximal Radial Distribution Functions, The Journal of Physical Chemistry Letters, vol.2, issue.13, pp.1626-1632, 2011.
DOI : 10.1021/jz200609v

D. Brown, H. Minoux, and B. Maigret, A domain decomposition parallel processing algorithm for molecular dynamics simulations of systems of arbitrary connectivity, Computer Physics Communications, vol.103, issue.2-3, pp.170-186, 1997.
DOI : 10.1016/S0010-4655(97)00040-4

P. B. Callahan and S. R. Kosaraju, A decomposition of multidimensional point sets with applications to k-nearest-neighbors and n-body potential fields, Journal of the ACM, vol.42, issue.1, pp.67-90, 1995.
DOI : 10.1145/200836.200853

T. Hamada, T. Narumi, R. Yokota, K. Yasuoka, K. Nitadori et al., 42 TFlops hierarchical N-body simulations on GPUs with applications in both astrophysics and turbulence, Proceedings of the Conference on High Performance Computing Networking , Storage and Analysis, pp.62-87, 2009.

J. E. Stone, J. C. Phillips, P. L. Freddolino, D. J. Hardy, L. G. Trabuco et al., Accelerating molecular modeling applications with graphics processors, Journal of Computational Chemistry, vol.13, issue.16, pp.2618-2640, 2007.
DOI : 10.1002/jcc.20829
URL : http://cacs.usc.edu/education/cs653/Stone-MDGPU-JCC07.pdf

T. Preis, P. Virnau, W. Paul, and J. J. Schneider, GPU accelerated Monte Carlo simulation of the 2D and 3D Ising model, Journal of Computational Physics, vol.228, issue.12, pp.4468-4477, 2009.
DOI : 10.1016/j.jcp.2009.03.018

A. Gara, M. A. Blumrich, D. Chen, G. L. Chiu, P. Coteus et al., Overview of the Blue Gene/L system architecture, IBM Journal of Research and Development, vol.49, issue.2.3, pp.195-212, 2005.
DOI : 10.1147/rd.492.0195

D. E. Shaw, M. M. Deneroff, R. O. Dror, J. S. Kuskin, R. H. Larson et al., Anton, a special-purpose machine for molecular dynamics simulation, Communications of the ACM, vol.51, issue.7, pp.91-97, 2008.
DOI : 10.1145/1364782.1364802

M. Taiji, MDGRAPE-3 chip: a 165 Gflops application specific LSI for molecular dynamics simulations, Hot Chips, pp.98-202, 2004.

S. M. Larson, C. D. Snow, and M. Shirts, Folding@ Home and Genome@ Home: Using distributed computing to tackle previously intractable problems in computational biology, Computational Genomics, issue.11, p.25, 2002.

B. R. Brooks, C. L. Brooks, I. , A. D. Jr, L. Nilsson et al., CHARMM: The biomolecular simulation program, Journal of Computational Chemistry, vol.103, issue.13, pp.301545-1614, 2009.
DOI : 10.1002/jcc.21287

X. Gonze, Abstract, Zeitschrift f??r Kristallographie - Crystalline Materials, vol.220, issue.5/6, pp.558-562, 2005.
DOI : 10.1524/zkri.220.5.558.65066

K. J. Bowers, E. Chow, H. Xu, R. O. Dror, M. P. Eastwood et al., Scalable Algorithms for Molecular Dynamics Simulations on Commodity Clusters, ACM/IEEE SC 2006 Conference (SC'06), pp.43-43, 2006.
DOI : 10.1109/SC.2006.54

J. C. Phillips, R. Braun, W. Wang, J. Gumbart, E. Tajkhorshid et al., Scalable molecular dynamics with NAMD, Journal of Computational Chemistry, vol.84, issue.16, pp.1781-1802, 2005.
DOI : 10.1002/jcc.20289
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2486339

W. R. Scott, P. H. Hünenberger, I. G. Tironi, A. E. Mark, S. R. Billeter et al., The GROMOS Biomolecular Simulation Program Package, The Journal of Physical Chemistry A, vol.103, issue.19, pp.3596-3607, 1999.
DOI : 10.1021/jp984217f

B. Hess, C. Kutzner, D. Van-der, E. Spoel, and . Lindahl, GROMACS 4:?? Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation, Journal of Chemical Theory and Computation, vol.4, issue.3, pp.435-447, 2008.
DOI : 10.1021/ct700301q

G. Klimeck, M. Mclennan, S. P. Brophy, G. B. Adams, and M. S. Lundstrom, nanoHUB.org: Advancing Education and Research in Nanotechnology, Computing in Science & Engineering, vol.10, issue.5, pp.17-23, 2008.
DOI : 10.1109/MCSE.2008.120
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.117.8058

S. Artemova, S. Grudinin, and S. Redon, A comparison of neighbor search algorithms for large rigid molecules, Journal of Computational Chemistry, vol.250, issue.13, pp.2865-2877, 2011.
DOI : 10.1002/jcc.21868
URL : https://hal.archives-ouvertes.fr/hal-00748053

H. M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T. N. Bhat et al., The Protein Data Bank, Nucleic Acids Research, vol.28, issue.1, pp.235-242, 2000.
DOI : 10.1093/nar/28.1.235

S. Artemova, S. Grudinin, and S. Redon, Fast construction of assembly trees for molecular graphs, Journal of Computational Chemistry, vol.48, issue.8, pp.1589-1598, 2011.
DOI : 10.1002/jcc.21738
URL : https://hal.archives-ouvertes.fr/hal-00746836

S. Artemova and S. Redon, Adaptively Restrained Particle Simulations, Physical Review Letters, vol.109, issue.19, p.27, 2012.
DOI : 10.1103/PhysRevLett.109.190201
URL : https://hal.archives-ouvertes.fr/hal-00756121

G. R. Smith and M. J. Sternberg, Prediction of protein???protein interactions by docking methods, Current Opinion in Structural Biology, vol.12, issue.1, pp.28-35, 2002.
DOI : 10.1016/S0959-440X(02)00285-3

I. Halperin, B. Ma, H. Wolfson, and R. Nussinov, Principles of docking: An overview of search algorithms and a guide to scoring functions, Proteins: Structure, Function, and Genetics, vol.43, issue.8, pp.409-443, 2002.
DOI : 10.1002/prot.10115

M. Matsumoto and T. Nishimura, Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator, ACM Transactions on Modeling and Computer Simulation, vol.8, issue.1, pp.3-30, 1998.
DOI : 10.1145/272991.272995

G. Marsaglia and W. W. Tsang, The Ziggurat Method for Generating Random Variables, Journal of Statistical Software, vol.5, issue.8, pp.1-7, 2000.
DOI : 10.18637/jss.v005.i08

G. Marsaglia, Xorshift RNGs, Journal of Statistical Software, vol.8, issue.14, pp.1-6, 2003.
DOI : 10.18637/jss.v008.i14
URL : http://doi.org/10.18637/jss.v008.i14

C. J. Cramer, Essentials of computational chemistry: theories and models, p.20, 2004.

J. Chen, W. Im, C. L. Brooks, and I. , Application of torsion angle molecular dynamics for efficient sampling of protein conformations, Journal of Computational Chemistry, vol.22, issue.15, pp.1565-1578, 2005.
DOI : 10.1002/jcc.20293

B. Brutovsky, T. Mülders, and G. R. Kneller, Accelerating molecular dynamics simulations by linear prediction of time series, The Journal of Chemical Physics, vol.118, issue.14, pp.6179-6187, 2003.
DOI : 10.1063/1.1559033
URL : https://hal.archives-ouvertes.fr/hal-00087705

A. K. Mazur, Quasi-hamiltonian equations of motion for internal coordinate molecular dynamics of polymers. Arxiv preprint physics, p.23, 1997.

J. B. Anderson, Quantum Monte Carlo: origins, development, applications, p.24, 2007.

U. H. Hansmann and Y. Okamoto, New Monte Carlo algorithms for protein folding, Current Opinion in Structural Biology, vol.9, issue.2, pp.177-183, 1999.
DOI : 10.1016/S0959-440X(99)80025-6

G. Turk, Interactive collision detection for molecular graphics, p.31, 1989.

J. L. Bentley and J. H. Friedman, Data Structures for Range Searching, ACM Computing Surveys, vol.11, issue.4, pp.397-409, 1979.
DOI : 10.1145/356789.356797

M. Teschner, S. Kimmerle, B. Heidelberger, G. Zachmann, L. Raghupathi et al., Collision Detection for Deformable Objects, Computer Graphics Forum, vol.20, issue.3, pp.61-81, 2005.
DOI : 10.1111/1467-8659.t01-1-00592
URL : https://hal.archives-ouvertes.fr/inria-00539916

J. Onderik and R. Durikovic, Efficient neighbor search for particle-based fluids, Journal of the Applied Mathematics Statistics and Informatics (JAMSI), vol.4, issue.1, pp.29-43, 2008.

B. C. Vemuri, Y. Cao, and L. Chen, Fast Collision Detection Algorithms with Applications to Particle Flow, Computer Graphics Forum, vol.17, issue.2, pp.121-134, 1998.
DOI : 10.1111/1467-8659.00233

J. Barnes and P. Hut, A hierarchical O(N log N) force-calculation algorithm, Nature, vol.6, issue.6096, pp.446-449, 1986.
DOI : 10.1038/324446a0

A. W. Appel, An Efficient Program for Many-Body Simulation, SIAM Journal on Scientific and Statistical Computing, vol.6, issue.1, pp.85-103, 1985.
DOI : 10.1137/0906008

I. Carlbom, An Algorithm for Geometric Set Operations Using Cellular Subdivision Techniques, IEEE Computer Graphics and Applications, pp.44-55, 1987.
DOI : 10.1109/MCG.1987.276987

A. Guttman, R-trees: a dynamic index structure for spatial searching, Proceedings of the 1984 ACM SIGMOD International Conference on Management of Data, pp.47-57, 1984.

S. Gottschalk, Collision queries using oriented bounding boxes, pp.31-38, 2000.

W. C. Thibault and B. F. Naylor, Set operations on polyhedra using binary space partitioning trees, Proceedings of the 14th annual conference on Computer graphics and interactive techniques, pp.153-162, 1987.
DOI : 10.1145/37401.37421

A. Donev, S. Torquato, and F. H. Stillinger, Neighbor list collision-driven molecular dynamics simulation for nonspherical hard particles. I. Algorithmic details, Journal of Computational Physics, vol.202, issue.2, pp.737-764, 2005.
DOI : 10.1016/j.jcp.2004.08.014

G. Paul, A Complexity O(1) priority queue for event driven molecular dynamics simulations, Journal of Computational Physics, vol.221, issue.2, pp.615-625, 2007.
DOI : 10.1016/j.jcp.2006.06.042

V. R. De-angulo, J. Cortés, and T. Siméon, BioCD : An efficient algorithm for self-collision and distance computation between highly articulated molecular models, Robotics: Science and Systems I, p.31, 2005.
DOI : 10.15607/RSS.2005.I.032

L. Guibas, A. Nguyen, D. , and L. Zhang, Collision detection for deforming necklaces, Proceedings of the eighteenth annual symposium on Computational geometry , SCG '02, pp.33-42, 2002.
DOI : 10.1145/513400.513405

I. Lotan, F. Schwarzer, D. Halperin, and J. C. Latombe, Efficient maintenance and selfcollision testing for kinematic chains, Proceedings of the eighteenth annual symposium on Computational geometry, pp.43-52, 2002.
DOI : 10.1145/513400.513406
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.7498

I. Lotan, F. Schwarzer, D. Halperin, and J. C. Latombe, Algorithm and Data Structures for Efficient Energy Maintenance during Monte Carlo Simulation of Proteins, Journal of Computational Biology, vol.11, issue.5, pp.902-932, 2004.
DOI : 10.1089/cmb.2004.11.902

W. R. Taylor and Z. Katsimitsoulia, A soft collision detection algorithm for simple Brownian dynamics, Computational Biology and Chemistry, vol.34, issue.1, pp.1-10, 2010.
DOI : 10.1016/j.compbiolchem.2009.11.003

W. F. Van-gunsteren, H. J. Berendsen, F. Colonna, D. Perahia, J. P. Hollenberg et al., On searching neighbors in computer simulations of macromolecular systems, Journal of Computational Chemistry, vol.257, issue.3, pp.272-279, 1984.
DOI : 10.1002/jcc.540050311

E. S. Fomin, Consideration of data load time on modern processors for the Verlet table and linked-cell algorithms, Journal of Computational Chemistry, vol.66, issue.7, pp.1386-1399, 2011.
DOI : 10.1002/jcc.21722

V. Yip and R. Elber, Calculations of a list of neighbors in Molecular Dynamics simulations, Journal of Computational Chemistry, vol.5, issue.7, pp.921-927, 1989.
DOI : 10.1002/jcc.540100709

Z. Yao, J. S. Wang, G. R. Liu, and M. Cheng, Improved neighbor list algorithm in molecular simulations using cell decomposition and data sorting method, Computer Physics Communications, vol.161, issue.1-2, pp.27-35, 2004.
DOI : 10.1016/j.cpc.2004.04.004

Q. F. Fang, R. Wang, and C. S. Liu, Movable hash algorithm for search of the neighbor atoms in molecular dynamics simulation, Computational Materials Science, vol.24, issue.4, pp.453-456, 2002.
DOI : 10.1016/S0927-0256(02)00152-0

R. J. Petrella, I. Andricioaei, B. R. Brooks, and M. Karplus, An improved method for nonbonded list generation: Rapid determination of near-neighbor pairs, Journal of Computational Chemistry, vol.264, issue.2, pp.222-231, 2003.
DOI : 10.1002/jcc.10123

P. Gonnet, A simple algorithm to accelerate the computation of non-bonded interactions in cell-based molecular dynamics simulations, Journal of Computational Chemistry, vol.4, issue.2, pp.570-573, 2007.
DOI : 10.1002/jcc.20563

W. Mattson and B. M. Rice, Near-neighbor calculations using a modified cell-linked list method, Computer Physics Communications, vol.119, issue.2-3, pp.135-148, 1999.
DOI : 10.1016/S0010-4655(98)00203-3

Z. W. Cui, Y. Sun, and J. M. Qu, The neighbor list algorithm for a parallelepiped box in molecular dynamics simulations, Science Bulletin, vol.54, issue.9, pp.1463-1469, 2009.
DOI : 10.1007/s11434-009-0197-0

B. K. Shoichet, I. D. Kuntz, and D. L. Bodian, Molecular docking using shape descriptors, Journal of Computational Chemistry, vol.103, issue.3, pp.380-397, 1992.
DOI : 10.1002/jcc.540130311

K. W. Kaufmann, G. H. Lemmon, S. L. Deluca, J. H. Sheehan, and J. Meiler, Practically Useful: What the Rosetta Protein Modeling Suite Can Do for You, Biochemistry, issue.14, pp.492987-2998, 2010.

J. T. Klosowski, M. Held, J. S. Mitchell, H. Sowizral, and K. Zikan, Efficient collision detection using bounding volume hierarchies of k-DOPs. Visualization and Computer Graphics, IEEE Transactions on, vol.4, issue.1, pp.21-36, 2002.

S. Gottschalk, M. C. Lin, and D. Manocha, OBBTree, Proceedings of the 23rd annual conference on Computer graphics and interactive techniques , SIGGRAPH '96, pp.171-180, 1996.
DOI : 10.1145/237170.237244

N. Beckmann, H. P. Kriegel, R. Schneider, and B. Seeger, The R*-tree: an efficient and robust access method for points and rectangles, ACM SIGMOD Record, vol.19, issue.2, pp.322-331, 1990.
DOI : 10.1145/93605.98741

N. Megiddo, Linear-time algorithms for linear programming in R3 and related problems, 23rd Annual Symposium on Foundations of Computer Science (sfcs 1982), pp.759-776, 1983.
DOI : 10.1109/SFCS.1982.24

P. M. Hubbard, Collision detection for interactive graphics applications, IEEE Transactions on Visualization and Computer Graphics, pp.218-230, 1995.
DOI : 10.1109/2945.466717

P. N. Yianilos, Data structures and algorithms for nearest neighbor search in general metric spaces, Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms, pp.311-321, 1993.

G. Barequet, B. Chazelle, L. J. Guibas, J. S. Mitchell, and A. Tal, BOXTREE: A Hierarchical Representation for Surfaces in 3D, Computer Graphics Forum, vol.15, issue.3, pp.387-396, 1996.
DOI : 10.1111/1467-8659.1530387

H. Sundar, R. S. Sampath, and G. Biros, Bottom-Up Construction and 2:1 Balance Refinement of Linear Octrees in Parallel, SIAM Journal on Scientific Computing, vol.30, issue.5, pp.2675-2708, 2008.
DOI : 10.1137/070681727

E. Ramirez, H. Navarro, R. Carmona, and J. Ramos, Optimizing Collision Detection based on OBB Trees Generated with Genetic Algorithm, IV Iberoamerican Symposium in Computer Graphics -SIACG, pp.1-7, 2009.

K. Fischer, B. Gaertner, and M. Kutz, Fast Smallest-Enclosing-Ball Computation in High Dimensions, Proceedings of the 11th Annual European Symposium on Algorithms (ESA), pp.630-641, 2003.
DOI : 10.1007/978-3-540-39658-1_57

S. Quinlan, Efficient distance computation between non-convex objects, Proceedings of the 1994 IEEE International Conference on Robotics and Automation, pp.3324-3329, 1994.
DOI : 10.1109/ROBOT.1994.351059

U. Breymann, Designing Components with the C++ STL, p.73, 1998.

B. Bernard and R. Samudrala, A generalized knowledge-based discriminatory function for biomolecular interactions, Proteins: Structure, Function, and Bioinformatics, vol.69, issue.12, Part 1, pp.115-128, 2009.
DOI : 10.1002/prot.22323

J. S. Vitter, External memory algorithms and data structures: dealing with massive data, ACM Computing Surveys, vol.33, issue.2, pp.209-271, 2001.
DOI : 10.1145/384192.384193

L. Arge, G. S. Brodal, and R. Fagerberg, Cache-Oblivious Data Structures, Handbook on Data Structures and Applications, p.52, 2004.
DOI : 10.1201/9781420035179.ch34
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.211.8599

M. Frigo, C. E. Leiserson, H. Prokop, and S. Ramachandran, Cache-oblivious algorithms, Proceedings of 40th Annual Symposium on Foundations of Computer Science (FOCS '99, p.52, 1999.

S. E. Yoon and D. Manocha, Cache-Efficient Layouts of Bounding Volume Hierarchies, Computer Graphics Forum, vol.25, issue.3, pp.507-516, 2006.
DOI : 10.1145/357332.357335

T. J. Kim, B. Moon, D. Kim, and S. E. Yoon, RACBVHs: Random-accessible compressed bounding volume hierarchies. Visualization and Computer Graphics [189] P. Terdiman. Memory-optimized bounding-volume hierarchies, IEEE Transactions on, vol.16, issue.2, pp.273-286, 2001.

K. Van-workum and J. F. Douglas, Symmetry, equivalence, and molecular self-assembly, Physical Review E, vol.73, issue.3, pp.31502-54, 2006.
DOI : 10.1103/PhysRevE.73.031502

I. G. Tironi, R. M. Brunne, and W. F. Van-gunsteren, On the relative merits of flexible versus rigid models for use in computer simulations of molecular liquids, Chemical Physics Letters, vol.250, issue.1, pp.19-24, 1996.
DOI : 10.1016/0009-2614(95)01434-9

R. Tarjan, Depth-First Search and Linear Graph Algorithms, 12th Annual Symposium on, pp.114-121, 1971.
DOI : 10.1137/0201010
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.327.8418

P. Mateti and N. Deo, On Algorithms for Enumerating All Circuits of a Graph, SIAM Journal on Computing, vol.5, issue.1, pp.90-99, 1976.
DOI : 10.1137/0205007

G. García, E. Espinosa, I. Ruiz, and M. Gómez-nieto, Efficient Parallel Solution to Calculate All Cycles in Graphs, Applied Parallel Computing, pp.765-766, 2006.
DOI : 10.1007/3-540-48051-X_41

G. Macgillivray and M. L. Yu, Generalized partitions of graphs, Discrete Applied Mathematics, vol.91, issue.1-3, pp.143-153, 1999.
DOI : 10.1016/S0166-218X(98)00124-3

A. Levin, D. Paulusma, and G. J. Woeginger, The computational complexity of graph contractions I: Polynomially solvable and NP-complete cases, Networks, vol.6, issue.3, pp.178-189, 2008.
DOI : 10.1002/net.20214

M. S. Warren and J. K. Salmon, A parallel hashed Oct-Tree N-body algorithm, Proceedings of the 1993 ACM/IEEE conference on Supercomputing , Supercomputing '93, pp.12-21, 1993.
DOI : 10.1145/169627.169640
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.30.5842

I. Wald and V. Havran, On building fast kd-trees for ray tracing, and on doing that in O(NlogN) In Interactive Ray Tracing, IEEE Symposium, pp.61-69, 2006.

T. Asano, D. Ranjan, T. Roos, E. Welzl, and P. Widmayer, Space-filling curves and their use in the design of geometric data structures, Theoretical Computer Science, vol.181, issue.1, pp.3-15, 1997.
DOI : 10.1016/S0304-3975(96)00259-9

P. M. Campbell, K. D. Devine, J. E. Flaherty, L. G. Gervasio, and J. D. Teresco, Dynamic octree load balancing using space-filling curves, p.68, 2003.

T. Ulrich and M. Deloura, Game programming gems, Charles River Media, p.71, 2000.

M. Blum, R. W. Floyd, V. Pratt, R. L. Rivest, and R. E. Tarjan, Linear time bounds for median computations, Proceedings of the fourth annual ACM symposium on Theory of computing , STOC '72, pp.119-124, 1972.
DOI : 10.1145/800152.804904

R. M. Mukherjee and K. S. Anderson, Orthogonal Complement Based Divide-and-Conquer Algorithm for constrained multibody systems, Nonlinear Dynamics, vol.25, issue.2???3, pp.199-215, 2007.
DOI : 10.1007/s11071-006-9083-3

R. W. Hockney and J. W. Eastwood, Computer simulation using particles. Institute of Physics publishing, p.89, 1992.

J. J. Monaghan, Smoothed particle hydrodynamics, Reports on Progress in Physics, vol.68, issue.8, pp.1703-89, 2005.
DOI : 10.1088/0034-4885/68/8/R01

O. Etzmuss, J. Gross, and W. Strasser, Deriving a particle system from continuum mechanics for the animation of deformable objects. Visualization and Computer Graphics, IEEE Transactions on, vol.9, issue.4, pp.538-550, 2003.

B. Eberhardt, A. Weber, and W. Strasser, A fast, flexible, particle-system model for cloth draping, IEEE Computer Graphics and Applications, vol.16, issue.5, pp.52-59, 1996.
DOI : 10.1109/38.536275

V. I. Yamakov and E. H. Glaessgen, Nanoscale fracture: To twin or not to twin, Nature Materials, vol.83, issue.11, pp.795-796, 2007.
DOI : 10.1038/nmat2041

M. Jaraiz, G. H. Gilmer, J. M. Poate, and T. D. De-la-rubia, Atomistic calculations of ion implantation in Si: Point defect and transient enhanced diffusion phenomena, Applied Physics Letters, vol.68, issue.3, pp.409-411, 1996.
DOI : 10.1063/1.116701

G. Peilert, J. Konopka, H. Stöcker, W. Greiner, M. Blann et al., Dynamical treatment of Fermi motion in a microscopic description of heavy ion collisions, Physical Review C, vol.46, issue.4, pp.1457-93, 1992.
DOI : 10.1103/PhysRevC.46.1457

R. D. Engle, R. D. Skeel, and M. Drees, Monitoring energy drift with shadow Hamiltonians, Journal of Computational Physics, vol.206, issue.2, pp.432-452, 2005.
DOI : 10.1016/j.jcp.2004.12.009

S. K. Gray, D. W. Noid, and B. G. Sumpter, Symplectic integrators for large scale molecular dynamics simulations: A comparison of several explicit methods, The Journal of Chemical Physics, vol.101, issue.5, pp.4062-4072, 1994.
DOI : 10.1063/1.467523

I. P. Omelyan, I. M. Mryglod, and R. Folk, Symplectic analytically integrable decomposition algorithms: classification, derivation, and application to molecular dynamics, quantum and celestial mechanics simulations, Computer Physics Communications, vol.151, issue.3, pp.272-314, 2003.
DOI : 10.1016/S0010-4655(02)00754-3

M. Tuckerman, Statistical mechanics: theory and molecular simulation, p.102, 2010.

A. Rahman, Correlations in the Motion of Atoms in Liquid Argon, Physical Review, vol.136, issue.2A, pp.405-411, 1964.
DOI : 10.1103/PhysRev.136.A405

B. Adams, M. Pauly, R. Keiser, and L. J. Guibas, Adaptively sampled particle fluids, ACM Transactions on Graphics, vol.26, issue.3, pp.48-134, 2007.
DOI : 10.1145/1276377.1276437
URL : https://lirias.kuleuven.be/bitstream/123456789/244123/1/adams-sig07.pdf

F. Bertails, T. Y. Kim, M. P. Cani, and U. Neumann, Adaptive Wisp Tree: a multiresolution control structure for simulating dynamic clustering in hair motion, Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on Computer animation, pp.207-213, 2003.
URL : https://hal.archives-ouvertes.fr/inria-00519007

V. R. De-angulo, J. Cortés, and J. M. Porta, Rigid-CLL: Avoiding constant-distance computations in cell linked-lists algorithms, Journal of Computational Chemistry, vol.5, issue.Suppl 1, pp.294-154, 2012.
DOI : 10.1002/jcc.21974