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Foundations of Molecular Modeling and Simulation pp 53–77Cite as

Optimizing Molecular Models Through Force-Field Parameterization via the Efficient Combination of Modular Program Packages

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Abstract

A central goal of molecular simulations is to predict physical or chemical properties such that costly and elaborate experiments can be minimized. The reliable generation of molecular models is a critical issue to do so. Hence, striving for semiautomated and fully automated parameterization of entire force fields for molecular simulations, the authors developed several modular program packages in recent years. The programs run with limited user interactions and can be executed in parallel on modern computer clusters. Various interlinked resolutions of molecular modeling are addressed: For intramolecular interactions, a force-field optimization package named Wolf2Pack has been developed that transfers knowledge gained from quantum mechanics to Newtonian-based molecular models. For intermolecular interactions, especially Lennard–Jones parameters, a modular optimization toolkit of programs and scripts has been created combining global and local optimization algorithms. Global optimization is performed by a tool named CoSMoS, while local optimization is done by the gradient-based optimization workflow named GROW or by a derivative-free method called SpaGrOW. The overall goal of all program packages is to realize an easy, efficient, and user-friendly development of reliable force-field parameters in a reasonable time. The various tools are needed and interlinked since different stages of the optimization process demand different courses of action. In this paper, the conception of all programs involved is presented and how they communicate with each other.

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Notes

  1. 1.

    http://www.wolf2pack.com.

References

  1. Case, D.A., Babin, V., Berryman, J.T., Betz, R.M., Cai, Q., Cerutti, D.S., Cheatham III, T.E., Darden, T.A., Duke, R.E., Gohlke, H., Goetz, A.W., Gusarov, S. Homeyer, N., Janowski, P., Kaus, J., Kolossváry, I., Kovalenko, A., Lee, T.S., LeGrand, S., Lucko, T., Luo, R., Madej, B., Merz, K.M., Paesani, F., Roe, D.R., Roitberg, A., Sagui, C., Salomon-Ferrer, R., Seabra, G., Simmerling, C.L., Smith, W., Swails, J., Walker, R.C., Wang, J., Wolf, R.M., Wu, X., Kollmann, P.A.: AMBER 14. http://ambermd.org. University of California, San Francisco (2014)

  2. Brooks, B.R., Brooks III, C.L., Mackerell, A.D., Nilsson, L., Petrella, R.J., Roux, B., Won, Y., Archontis, G., Bartels, C., Caflisch, S.B.A., Caves, L., Cui, Q., Dinner, A.R., Feig, M., Fischer, S., Gao, J., Hodoscek, M., Im, W., Kuczera, K., Lazaridis, T., Ma, J., Ovchinnikov, V., Paci, E., Pastor, R.W., Post, C.B., Pu, J.Z., Schaefer, M., Tidor, B., Venable, R.M., Woodcock, H.L., Wu, X., Yang, W., York, D.M., Karplus, M.: Charmm: the biomolecular simulation program. J. Comp. Chem. 30, 1545–1615 (2009)

    Article  CAS  Google Scholar 

  3. Hess, B., van der Spoel, D., Lindahl, E.: Gromacs user manual 4.5.4. http://www.gromacs.org/Documentation/Manual/manual-4.5.4.pdf (2010)

  4. Plimpton, S.: Fast parallel algorithms for short-range molecular dynamics. J. Comp. Phys. 117, 1–19 (1995)

    Article  CAS  Google Scholar 

  5. Gil, Y., Deelman, E., Ellisman, M., Fahringer, T., Fox, G., Gannon, D., Goble, C., Livny, M., Moreau, L., Myers, J.: Examining the challenges of scientific workflows. Computer 40, 24–32 (2007)

    Article  Google Scholar 

  6. Waldher, B., Kuta, J., Chen, S., Henson, N., Clark, A.E.: ForceFit: a code to fit classical force fields to quantum mechanical potential energy surfaces. J. Comp. Chem. 12, 2307–2316 (2010)

    Google Scholar 

  7. Highly optimized object-oriented many-particle dynamics—blue edition. http://codeblue.umich.edu/hoomd-blue/ (2011)

  8. Halverson, J.D., Brandes, T., Lenz, O., Arnold, A., Bevc, S., Starchenko, V., Kremer, K., Stuehn, T., Reith, D.: ESPResSo++: a modern multiscale simulation package for soft matter systems. Comput. Phys. Commun. 184, 1129–1149 (2013)

    Article  CAS  Google Scholar 

  9. Karimi-Varzaneh, H., Qian, H., Chen, X., Carbone, P., Müller-Plathe, F.: Ibisco: a molecular dynamics simulation package for coarse-grained simulation. J. Comp. Chem. 32, 1475–1487 (2011)

    Article  CAS  Google Scholar 

  10. Singer, S.J., Nicolson, G.L.: The fluid mosaic model of the structure of cell membranes. Science 175, 720–731 (1972)

    Article  CAS  Google Scholar 

  11. Zhou, Y., Stell, G.: Chemical association in simple models of molecular and ionic fluids II. Thermodynamic properties. J. Chem. Phys. 96, 1504–1506 (1992)

    Article  CAS  Google Scholar 

  12. Siepmann, J.I., Karaborni, S., Smit, B.: Simulating the critical behaviour of complex fluids. Nature 365, 330–332 (1993)

    Article  CAS  Google Scholar 

  13. O’Connell, S.T., Thompson, P.A.: Molecular dynamics-continuum hybrid computations: a tool for studying complex fluid flow. Phys. Rev. E 52, 5792–5795 (1995)

    Article  Google Scholar 

  14. Kolafa, J., Nezbeda, I., Lisal, M.: Effect of short- and long-range forces on the properties of fluids. III. dipolar and quadrupolar fluids. Mol. Phys. 99, 1751–1764 (2001)

    Article  CAS  Google Scholar 

  15. Valiullin, R., Naumov, S., Galvosas, P., Kärger, J., Woo, H.-J., Porcheron, F., Monson, P.A.: Exploration of molecular dynamics during transient sorption of fluids in mesoporous materials. Nature 443, 965–968 (2006)

    Article  CAS  Google Scholar 

  16. Batra, I.P., Bennett, B.I., Herman, F.: Simple molecular model for crystalline tetrathiofulvalene-tetracyanoquinodimethane (TTF-TCNQ). Phys. Rev. B 11, 4927–4934 (1975)

    Article  CAS  Google Scholar 

  17. Fehlner, T.P.: Molecular models of solid state metal boride structure. J. Solid State Chem. 154, 110–113 (2000)

    Article  CAS  Google Scholar 

  18. Della, C.N., Dongwei, S.: Mechanical properties of carbon nanotubes reinforced ultra high molecular weight polyethylene. Solid State Phenom. 136, 45–49 (2008)

    Article  CAS  Google Scholar 

  19. Lin, S.-T., Blanco, M., Goddard III, W.A.: The two-phase model for calculating thermodynamic properties of liquids from molecular dynamics: validation for the phase diagram of Lennard-Jones fluids. J. Chem. Phys. 119, 11792–11805 (2003)

    Article  CAS  Google Scholar 

  20. Bien, D.E., Chiriac, V.A.: A novel molecular approach to modeling phase change in micro-fluidic systems. In: Proceedings of the 9th Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems, pp. 598–604. IEEE, New Jersey (2004)

    Google Scholar 

  21. Vrabec, J., Gross, J.: Vapor–liquid equilibria simulation and an equation of state contribution for dipole-quadrupole interactions. J. Phys. Chem. B 112, 51–60 (2008)

    Article  CAS  Google Scholar 

  22. Levitt, M., Warshel, A.: Computer simulation of protein folding. Nature 253, 694–698 (1975)

    Article  CAS  Google Scholar 

  23. Gsponer, J., Caflisch, A.: Molecular dynamics simulations of protein folding from the transition state. In: Fersth, A. (ed.) Proceedings of the National Academy of Sciences (PNAS), vol. 99, pp. 6719–6724. Washington (2002)

    Google Scholar 

  24. Snow, C.D., Sorin, E.J., Rhee, Y.M., Pandel, V.S.: How well can simulation predict protein folding kinetics and thermodynamics? Annu. Rev. Biophys. Biomol. Struct. 34, 43–69 (2005)

    Article  CAS  Google Scholar 

  25. Hodgkin, A.L., Huxley, A.F.: A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 117, 500–544 (1952)

    Article  CAS  Google Scholar 

  26. Barkla, B.J., Pantoja, O.: Physiology of ion transport across the tonoplast of higher plants. Annu. Rev. Plant Physiol. Plant Mol. Biol. 47, 159–184 (1996)

    Article  CAS  Google Scholar 

  27. Müller-Plathe, F., Reith, D.: Cause and effect reversed in non-equilibrium molecular dynamics: an easy route to transport coefficients. Comput. Theor. Polymer Sci. 9, 203–209 (1999)

    Article  Google Scholar 

  28. Bordat, P., Reith, D., Müller-Plathe, F.: The influence of interaction details on the thermal diffusion in binary Lennard-Jones liquids. J. Chem. Phys. 115, 8978–8982 (2001)

    Article  CAS  Google Scholar 

  29. Guevara-Carrion, G., Nieto-Draghi, C., Vrabec, J., Hasse, H.: Prediction of transport properties by molecular simulation: methanol and ethanol and their mixture. J. Phys. Chem. B 112, 16664–16674 (2008)

    Article  CAS  Google Scholar 

  30. Grest, G.S., Kremer, K.: Molecular dynamics simulation for polymers in the presence of a heat bath. Phys. Rev. A 33, 3628–3631 (1986)

    Article  CAS  Google Scholar 

  31. Müller-Plathe, F.: Permeation of polymers—a computational approach. Acta Polymer. 45, 259–293 (1994)

    Article  Google Scholar 

  32. Binder, K.: Monte Carlo and molecular dynamics simulations in polymer science. Oxford University Press, Oxford (1995)

    Google Scholar 

  33. Kremer, K., Müller-Plathe, F.: Multiscale simulation in polymer science. Mol. Sim. 28, 729–750 (2002)

    Article  CAS  Google Scholar 

  34. Praprotnik, M., Junghans, C., Delle Site, L., Kremer, K.: Simulation approaches to soft matter: generic statistical properties vs. chemical details. Comput. Phys. Commun. 179, 51–60 (2008)

    Article  CAS  Google Scholar 

  35. Reith, D., Kirschner, K.N.: A modern workflow for force field development—bridging quantum mechanics and atomistic computational models. Comput. Phys. Commun. 182, 2184–2191 (2011)

    Article  CAS  Google Scholar 

  36. Krämer-Fuhrmann, O., Neisius, J., Gehlen, N., Reith, D., Kirschner, K.N.: Wolf2Pack – Portal based atomistic force field development. J. Chem. Inf. Mod. 53, 802–808 (2013)

    Article  Google Scholar 

  37. Vainio, M.J., Johnson, M.S.: Generating conformer ensembles using a multiobjective genetic algorithm. J. Chem. Inf. Mod. 47, 2462–2474 (2007)

    Article  CAS  Google Scholar 

  38. Tasi, G., Mizukami, F., Csontos, J., Gyõrffy, W., Pálinkó, I.: Quantum algebraic–combinatoric study of the conformational properties of n-alkanes. II. J. Math. Chem. 27, 191–199 (2000)

    Article  CAS  Google Scholar 

  39. Jorgensen, W.L., Madura, J.D., Swensen, C.J.: Optimized intermolecular potential functions for liquid hydrocarbons. J. Am. Chem. Soc. 106, 6638–6646 (1984)

    Article  CAS  Google Scholar 

  40. Martin, M.G., Siepmann, J.I.: Transferable potentials for phase equilibria. 1. United-atom description of n-alkanes. J. Phys. Chem. B 102, 2569–2577 (1998)

    Article  CAS  Google Scholar 

  41. Ungerer, P., Beauvais, C., Delhommelle, J., Boutin, A., Rousseau, B., Fuchs, A.H.: Optimization of the anisotropic united atoms intermolecular potential for n-alkanes. J. Phys. Chem. 112, 5499–5510 (2000)

    Article  CAS  Google Scholar 

  42. Bourasseau, E., Haboudou, M., Boutin, A., Fuchs, A.H., Ungerer, P.: New optimization method for intermolecular potentials: optimization of a new anisotropic united atoms potential for olefins: prediction of equilibrium properties. J. Chem. Phys. 118, 3020–3035 (2003)

    Article  CAS  Google Scholar 

  43. Stoll, J., Vrabec, J., Hasse, H.: A set of molecular models for carbon monoxide and halogenated hydrocarbons. J. Chem. Phys. 119, 11396–11407 (2003)

    Article  CAS  Google Scholar 

  44. Reith, D., Pütz, M., Müller-Plathe, F.: Deriving effective mesoscale potentials from atomistic simulations. J. Comp. Chem. 24, 1624–1636 (2003)

    Article  CAS  Google Scholar 

  45. Oostenbrink, C., Villa, A., Mark, A.E., van Gunsteren, W.F.: A biomolecular force field based on the free enthalpy of hydration and solvation: the GROMOS force-field parameter sets 53A5 and 53A6. J. Comp. Chem. 25, 1656–1676 (2004)

    Article  CAS  Google Scholar 

  46. Sun, H.: Prediction of fluid densities using automatically derived VDW parameters. Fluid Phase Eq. 217, 59–76 (2004)

    Article  CAS  Google Scholar 

  47. Eckl, B., Vrabec, J., Hasse, H.: On the application of force fields for predicting a wide variety of properties: ethylene oxide as an example. Fluid Phase Eq. 274, 16–26 (2008)

    Article  CAS  Google Scholar 

  48. Cacelli, I., Cimoli, A., Livotto, P.R., Prampolini, G.: An automated approach for the parameterization of accurate intermolecular force-fields: pyridine as a case study. J. Comp. Chem. 33, 1055–1067 (2012)

    Article  CAS  Google Scholar 

  49. Ucyigitler, S., Camurdan, M.C., Elliott, J.R.: Optimization of transferable site–site potentials using a combination of stochastic and gradient search algorithms. Ind. Eng. Chem. Res. 51, 6219–6231 (2012)

    Article  CAS  Google Scholar 

  50. Eckelsbach, S., Janzen, T., Köster, A., Mirshnichenko, S., Muñoz Muñoz, Y.M., Vrabec, J.: Molecular models for cyclic alcanes and ethyl acetate as well as surface tension data from molecular simulation. In: Nagel, W.E., Kröner, D.E., Resch, M.M. (eds.) High Performance Computing in Science and Engineering ‘14, Transactions of the High Performance Computing Center, HLRS, Stuttgart (2014), pp. 645–659. Springer, Berlin (2015)

    Google Scholar 

  51. Muñoz Muñoz, Y.M., Guevara-Carrion, G., Llano-Restrepo, M., Vrabec, J.: Lennard–Jones force field parameters for cyclic alkanes from cyclopropane to cyclohexane. Fluid Phase Eq. 404, 150–160 (2015)

    Google Scholar 

  52. Kirschner, K.N., Yongye, A.B., Tschampel, S.M., Gonzalez-Outeirino, J., Daniels, C.R., Foley, B.L., Woods, R.J.: GLYCAM06: a generalizable biomolecular force field. Carbohydrates. J. Comp. Chem. 29, 622–655 (2008)

    Article  CAS  Google Scholar 

  53. Faller, R., Schmitz, H., Biermann, O., Müller-Plathe, F.: Automatic parameterization of force fields for liquids by simplex optimization. J. Comp. Chem. 20, 1009–1017 (1999)

    Article  CAS  Google Scholar 

  54. Dupradeau, F.-Y., Pigache, A., Zaffran, T., Savineau, C., Lelong, R., Grivel, N., Lelong, D., Rosanski, W., Cieplak, P.: The R.E.D. tools: advances in RESP and ESP charge derivation and force field library building. Phys. Chem. Chem. Phys. 12, 7821–7839 (2010)

    Article  CAS  Google Scholar 

  55. Hülsmann, M.: Effiziente und neuartige Verfahren zur Optimierung von Kraftfeldparametern bei atomistischen Molekularen Simulationen kondensierter Materie. In: Fraunhofer SCAI (ed.) Fraunhofer-Verlag, Ph.D. thesis, University of Cologne, Germany (2012)

    Google Scholar 

  56. Krämer, A., Hülsmann, M., Köddermann, T., Reith, D.: Automated parameterization of intermolecular pair potentials using global optimization techniques. Comput. Phys. Commun. 185, 3228–3239 (2014)

    Article  Google Scholar 

  57. Regis, R., Shoemaker, C.: Constrained global optimization of expensive black box functions using radial basis functions. J. Glob. Opt. 31, 153–171 (2005)

    Article  Google Scholar 

  58. Hülsmann, M., Köddermann, T., Vrabec, J., Reith, D.: GROW: A gradient-based optimization workflow for the automated development of molecular models. Comput. Phys. Commun. 181, 499–513 (2010)

    Article  Google Scholar 

  59. Nocedal, J., Wright, S.J.: Numerical Optimization. Springer, New York (1999)

    Book  Google Scholar 

  60. Hülsmann, M., Vrabec, J., Maaß, A., Reith, D.: Assessment of numerical optimization algorithms for the development of molecular models. Comput. Phys. Commun. 181, 887–905 (2010)

    Article  Google Scholar 

  61. Hülsmann, M., Müller, T.J., Köddermann, T., Reith, D.: Automated force field optimization of small molecules using a gradient-based workflow package. Mol. Sim. 36, 1182–1196 (2011)

    Article  Google Scholar 

  62. Köddermann, T., Kirschner, K.N., Vrabec, J., Hülsmann, M., Reith, D.: Liquid-liquid equilibria of dipropylene glycol dimethyl ether and water by molecular dynamics. Fluid Phase Eq. 310, 25–31 (2011)

    Article  Google Scholar 

  63. Hülsmann, M., Kopp, S., Huber, M., Reith, D.: Efficient gradient and Hessian calculations for numerical optimization algorithms applied to molecular simulations. In: Proceedings of the International Conference on Mathematical Modeling in Physical Sciences (IC-MSQUARE), Budapest, Hungary (2012), IOP Publishing, Journal of Physics: Conference Series 410, 012007 (2013)

    Google Scholar 

  64. Hülsmann, M., Kopp, S., Huber, M., Reith, D.: Utilization of efficient gradient and Hessian computations in the force field optimization process of molecular simulations. Comput. Sci. Disc. 6, 015005 (2013)

    Article  Google Scholar 

  65. Hülsmann, M., Reith, D.: SpaGrOW—a derivative-free optimization scheme for intermolecular force field parameters based on sparse grids methods. Entropy 15, 3640–3687 (2013)

    Article  Google Scholar 

  66. Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. J. Roy. Stat. Soc. Ser. B 67, 301–320 (2005)

    Article  Google Scholar 

  67. Smolyak, S.A.: Quadrature and interpolation formulas for tensor products of certain classes of functions. Sov. Math. Doklady 4, 240–243 (1963)

    Google Scholar 

  68. Griebel, M., Schneider, M., Zenger, C.: A combination technique for the solution of sparse grid problems. Technical Report, Institute for Computer Science, Technical University of Munich, Germany (1990)

    Google Scholar 

  69. Ditchfield, R., Hehre, W.J., Pople, J.A.: Self consistent molecular orbital methods. IX. An extended Gaussian type basis for molecular orbital studies of organic molecules. J. Chem. Phys. 54, 724–728 (1971)

    Article  CAS  Google Scholar 

  70. Dunning, T.H.: Gaussian basis sets for use in correlated molecular calculations. I. The atoms boron through neon and hydrogen. J. Chem. Phys. 90, 1007–1023 (1989)

    Article  CAS  Google Scholar 

  71. Maier, J.A., Martinez, C., Kasavajhala, K., Wickstrom, L., Hauser, K.E., Simmerling, C.: ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J. Chem. Theory Comput. 11, 3696–3713 (2015)

    Article  CAS  Google Scholar 

  72. Wang, J., Wolf, R.M., Caldwell, J.W., Kollman, P.A., Case, D.A.: Development and testing of a general amber force field. J. Comput. Chem. 25, 1157–1174 (2004)

    Article  CAS  Google Scholar 

  73. Dickson, C.J., Madej, B.D., Skjevik, Å.A., Betz, R.M., Teigen, K., Gould, I.A., Walker, R.C.: Lipid14: the amber lipid force field. J. Chem. Theory Comput. 10, 865–879 (2014)

    Article  CAS  Google Scholar 

  74. Wang, J.M., Kollman, P.A.: Automatic parameterization of force field by systematic search and genetic algorithms. J. Comp. Chem. 22, 1219–1228 (2001)

    Article  CAS  Google Scholar 

  75. Vaiana, A.C., Cournia, Z., Costescu, I.B., Smith, J.C.: AFMM: a molecular mechanics force field vibrational parametrization program. Comput. Phys. Commun. 167, 34–42 (2005)

    Article  CAS  Google Scholar 

  76. Guvench, O., MacKerell Jr, A.D.: Automated conformational energy fitting for force field development. J. Mol. Model. 14, 667–679 (2008)

    Article  CAS  Google Scholar 

  77. Mayne, C.G., Saam, J., Schulten, K., Tajkhorshid, E., Gumbart, J.C.: Rapid parameterization of small molecules using the force field toolkit. J. Comp. Chem. 32, 2757–2770 (2013)

    Article  Google Scholar 

  78. Hopkins, C.W., Roitberg, A.E.: Fitting of dihedral terms in classical force fields as an analytic linear least-squares problem. J. Chem. Inf. Mod. 54, 1978–1986 (2014)

    Article  CAS  Google Scholar 

  79. Burger, S.K., Ayers, P.W., Schofield, J.: Efficient parameterization of torsional terms for force fields. J. Comp. Chem. 35, 1438–1445 (2014)

    Article  CAS  Google Scholar 

  80. Betz, R.M., Walker, R.C.: Paramfit: automated optimization of force field parameters for molecular dynamics simulations. J. Comp. Chem. 36, 79–87 (2015)

    Article  CAS  Google Scholar 

  81. Vanommeslaeghe, K., Mingjun, Y., MacKerell, A.D.: Robustness in the fitting of molecular mechanics parameters. J. Comp. Chem. 36, 1083–1101 (2015)

    Article  CAS  Google Scholar 

  82. Vanduyfhuys, L., Vandenbrande, S., Verstraelen, T., Schmid, R., Waroquier, M., Van Speybroeck, V.: QuickFF: a program for a quick and easy derivation of force fields for metal-organic frameworks from ab initio input. J. Comp. Chem. 36, 1015–1027 (2015)

    Article  CAS  Google Scholar 

  83. Gordon, M.D., Schmidt, M.W.: Advances in electronic structure theory: GAMESS a decade later. In: Gordon, M.S., Schmidt, W., Dykstra, C.E. (eds.) Theory and Applications of Computational Chemistry: The First Forty Years, pp. 1167–1189. Elsevier Amsterdam Boston (2005)

    Google Scholar 

  84. O’Boyle, N., Banck, M., James, C., Morley, C., Vandermeersch, T., Hutchison, G.: Open babel: an open chemical toolbox. J. Cheminf. 3, 33 (2011)

    Article  Google Scholar 

  85. R: A language and environment for statistical computing. manual. http://www.R-project.org. The R Foundation for Statistical Computing, Vienna, Austria (2009)

  86. PyMOL(TM) Molecular Graphics System, Version 1.6.0.0. http://pymol.org && http://sourceforge.net/projects/pymol/ (2009)

  87. The LaTeX Project. http://latex-project.org/

  88. Deublein, S., Eckl, B., Stoll, J., Lishchuk, S.V., Guevara-Carrion, G., Glass, C.W., Merker, T., Bernreuther, M., Hasse, H., Vrabec, J.: ms2: a molecular simulation tool for thermodynamic properties. Comput. Phys. Commun. 182, 2350–2367 (2011)

    Article  CAS  Google Scholar 

  89. Stoll, J., Vrabec, J., Hasse, H., Fischer, J.: Comprehensive study of the vapour–liquid equilibria of the pure two–centre Lennard-Jones plus point quadrupole fluid. Fluid Phase Eq. 179, 339–362 (2001)

    Article  CAS  Google Scholar 

  90. Bégué, J.-P., Bonnet-Delpon, D., Crousse, B.: Fluorinated alcohols: anew medium for selective and clean reaction. Synlett, 18–29 (2004)

    Google Scholar 

  91. Rochester, C.H., Symonds, J.R.: Densities of solutions of four fluoralcohols in water. J. Fluorine Chem. 4, 141–148 (1974)

    Article  CAS  Google Scholar 

  92. Gross, T., Karger, N., Price, W.E.: p, T dependence of self-diffusion in 2-fluoroethanol, 2,2 difluoroethanol and 2,2,2-trifluoroethanol. J. Mol. L. 75, 159–168 (1998)

    Article  CAS  Google Scholar 

  93. Meeks, A.C., Goldfarb, I.J.: Vapor pressure of fluoroalcohols. J. Chem. Eng. Data 12, 196 (1967)

    Article  CAS  Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical Engineering (EMT) and Institute for Technology, Renewables and Energy Efficiency (TREE), Bonn-Rhein-Sieg University of Applied Sciences, Grantham-Allee 20, 53757, Sankt Augustin, Germany

    Marco Hülsmann, Andreas Krämer, Doron D. Heinrich & Dirk Reith

  2. Department of Simulation Engineering, Fraunhofer-Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757, Sankt Augustin, Germany

    Marco Hülsmann, Ottmar Krämer-Fuhrmann & Dirk Reith

  3. Department of Computer Science and the Institute of Visual Computing (IVC), Bonn-Rhein-Sieg University of Applied Sciences, Grantham-Allee 20, 53757, Sankt Augustin, Germany

    Karl N. Kirschner

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  1. Department of Chemical &, Northwestern University, Evanston, Illinois, USA

    Randall Q Snurr

  2. Chemical Engineering, Imperial College London, London, United Kingdom

    Claire S. Adjiman

  3. York Buffalo, The State University of New, Buffalo, New York, USA

    David A. Kofke

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Hülsmann, M., Kirschner, K.N., Krämer, A., Heinrich, D.D., Krämer-Fuhrmann, O., Reith, D. (2016). Optimizing Molecular Models Through Force-Field Parameterization via the Efficient Combination of Modular Program Packages. In: Snurr, R., Adjiman, C., Kofke, D. (eds) Foundations of Molecular Modeling and Simulation. Molecular Modeling and Simulation. Springer, Singapore. https://doi.org/10.1007/978-981-10-1128-3_4

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