Multiscale Materials Modeling in an Industrial Environment

Literature Cited

1. Goh GB, Eike DM, Murch BP, Brooks CL. 1.  2015. Accurate modeling of ionic surfactants at high concentration. J. Phys. Chem. B 119:6217–24 [Google Scholar]
2. Toivakka M, Alam P, Touaiti F, Oravilahti A, Oravilahti T. 2.  et al. 2015. Understanding coating strength at the molecular and microscopic level. Tappi J. 14:373–84 [Google Scholar]
3. Papatzani S, Paine K, Calabria-Holley J. 3.  2015. A comprehensive review of the models on the nanostructure of calcium silicate hydrates. Constr. Build. Mater. 74:219–34 [Google Scholar]
4. Jain S, Ginzburg VV, Jog P, Weinhold J, Srivastava R, Chapman WG. 4.  2009. Modeling polymer-induced interactions between two grafted surfaces: comparison between interfacial statistical associating fluid theory and self-consistent field theory. J. Chem. Phys. 131:044908 [Google Scholar]
5. Ma LK, Srivastava R, Barpanda D, Fowler T, Theophanous T, Verghese N. 5.  2013. An inverse approach to characterize anisotropic thermal conductivities of a dry fibrous preform composite. J. Reinf. Plast. Compos. 32:1916–27 [Google Scholar]
6. Christensen J, Albertus P, Sanchez-Carrera RS, Lohmann T, Kozinsky B. 6.  et al. 2012. A critical review of Li/air batteries. J. Electrochem. Soc. 159:R1–R30 [Google Scholar]
7. Albertus P, Girishkumar G, McCloskey B, Sanchez-Carrera RS, Kozinsky B. 7.  et al. 2011. Identifying capacity limitations in the Li/oxygen battery using experiments and modeling. J. Electrochem. Soc. 158:A343–51 [Google Scholar]
8. Sasabe H, Takamatsu J, Motoyama T, Watanabe S, Wagenblast G. 8.  et al. 2010. High-efficiency blue and white organic light-emitting devices incorporating a blue iridium carbene complex. Adv. Mater. 22:5003–7 [Google Scholar]
9. Deglmann P, Müller I, Becker F, Schäfer A, Hungenberg KD, Weiß H. 9.  2009. Prediction of propagation rate coefficients in free radical solution polymerization based on accurate quantum chemical methods: vinylic and related monomers, including acrylates and acrylic acid. Macromol. React. Eng. 3:496–515 [Google Scholar]
10. Jain A, Ong SP, Hautier G, Chen W, Richards WD. 10.  et al. 2013. Commentary: the materials project: a materials genome approach to accelerating materials innovation. APL Mater. 1:011002 [Google Scholar]
11. 11. Natl. Sci. Technol. Counc 2011. Materials Genome Initiative for Global Competitiveness 2011. Washington, DC: Natl. Sci. Technol. Counc https://www.whitehouse.gov/sites/default/files/microsites/ostp/materials_genome_initiative-final.pdf
12. Dirac PAM. 12.  1929. The basis of statistical quantum mechanics. Proc. Camb. Philos. Soc. 25:62–66 [Google Scholar]
13. Szabo A, Ostlund NS. 13.  1996. Modern Quantum Chemistry: Introduction to Advanced Electronic Structure Theory. New York: Dover [Google Scholar]
14. Gauss J. 14.  1998. Coupled-cluster theory. Encyclopedia of Computational Chemistry P. von Ragué Schleyer New York: Wiley [Google Scholar]
15. Koch W, Holthausen C. 15.  2001. A Chemist's Guide to Density Functional Theory Weinheim: Wiley-VCH, 2nd ed..
16. Hohenberg P, Kohn W. 16.  1964. Inhomogeneous electron gas. Phys. Rev. B 136:B864 [Google Scholar]
17. Kohn W, Sham LJ. 17.  1965. Self-consistent equations including exchange and correlation effects. Phys. Rev. 140:A1133 [Google Scholar]
18. Zhao Y, Truhlar DG. 18.  2008. Density functionals with broad applicability in chemistry. Acc. Chem. Res. 41:157–67 [Google Scholar]
19. Grimme S, Antony J, Ehrlich S, Krieg H. 19.  2010. A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu. J. Chem. Phys. 132:154104 [Google Scholar]
20. Tomasi J, Mennucci B, Cammi R. 20.  2005. Quantum mechanical continuum solvation models. Chem. Rev. 105:2999–3093 [Google Scholar]
21. Frenkel D, Smit B. 21.  2001. Understanding Molecular Simulation: From Algorithms to Applications San Diego: Academic
22. Gubbins KE, Moore JD. 22.  2010. Molecular modeling of matter: impact and prospects in engineering. Ind. Eng. Chem. Res. 49:3026–46 [Google Scholar]
23. Maginn E, Elliott J. 23.  2010. Historical perspective and current outlook for molecular dynamics as a chemical engineering tool. Ind. Eng. Chem. Res. 49:3059–78 [Google Scholar]
24. Theodorou DN. 24.  2010. Progress and outlook in Monte Carlo simulations. Ind. Eng. Chem. Res. 49:3047–58 [Google Scholar]
25. Langner KM, Sevink GJA. 25.  2012. Mesoscale modeling of block copolymer nanocomposites. Soft Matter 8:5102–18 [Google Scholar]
26. Moeendarbary E, Ng TY, Zangeneh M. 26.  2010. Dissipative particle dynamics in soft matter and polymeric applications—a review. Int. J. Appl. Mech. 2:161–90 [Google Scholar]
27. Voth G. 27.  2009. Coarse-Graining of Condensed Phase and Biomolecular Systems Boca Raton, FL: CRC Press, Taylor & Francis Group
28. Español P, Warren P. 28.  1995. Statistical mechanics of dissipative particle dynamics. Europhys. Lett. 30:191–96 [Google Scholar]
29. Goujon F, Malfreyt P, Tildesley DJ. 29.  2004. Dissipative particle dynamics simulations in the grand canonical ensemble: applications to polymer brushes. ChemPhysChem 5:457–64 [Google Scholar]
30. Groot RD, Warren PB. 30.  1997. Dissipative particle dynamics: bridging the gap between atomistic and mesoscopic simulation. J. Chem. Phys. 107:4423–35 [Google Scholar]
31. Groot RD, Madden TJ. 31.  1998. Dynamic simulation of diblock copolymer microphase separation. J. Chem. Phys. 108:8713–24 [Google Scholar]
32. Groot RD, Madden TJ, Tildesley DJ. 32.  1999. On the role of hydrodynamic interactions in block copolymer microphase separation. J. Chem. Phys. 110:9739–49 [Google Scholar]
33. Groot RD. 33.  2003. Electrostatic interactions in dissipative particle dynamics—simulation of polyelectrolytes and anionic surfactants. J. Chem. Phys. 118:11265–77 [Google Scholar]
34. Malfreyt P, Tildesley DJ. 34.  2000. Dissipative particle dynamics simulations of grafted polymer chains between two walls. Langmuir 16:4732–40 [Google Scholar]
35. Noro MG, Meneghini F, Warren PB. 35.  2003. Application of dissipative particle dynamics to materials physics problems in polymer and surfactant science. Mesoscale Phenom. Fluid Syst. 861:242–57 [Google Scholar]
36. Jones JL, Lal M, Ruddock JN, Spenley NA. 36.  1999. Dynamics of a drop at a liquid/solid interface in simple shear fields: a mesoscopic simulation study. Faraday Discuss. 112:129–42 [Google Scholar]
37. Hoogerbrugge PJ, Koelman JMVA. 37.  1992. Simulating microscopic hydrodynamic phenomena with dissipative particle dynamics. Europhys. Lett. 19:155–60 [Google Scholar]
38. Koelman JMVA, Hoogerbrugge PJ. 38.  1993. Dynamic simulations of hard-sphere suspensions under steady shear. Europhys. Lett. 21:363–68 [Google Scholar]
39. Abrams DS, Prausnitz JM. 39.  1975. Statistical thermodynamics of liquid-mixtures—new expression for excess Gibbs energy of partly or completely miscible systems. AIChE J. 21:116–28 [Google Scholar]
40. Flory PJ. 40.  1944. Thermodynamics of heterogeneous polymers and their solutions. J. Chem. Phys. 12:425–38 [Google Scholar]
41. Flory PJ. 41.  1953. Principles of Polymer Chemistry Ithaca, NY: Cornell Univ.
42. Flory PJ. 42.  1942. Thermodynamics of high polymer solutions. J. Chem. Phys. 10:51–61 [Google Scholar]
43. Klamt A. 43.  2005. COSMO-RS: From Quantum Chemistry to Fluid Phase Thermodynamics and Drug Design Amsterdam: Elsevier
44. Klamt A, Eckert F. 44.  2000. COSMO-RS: a novel and efficient method for the a priori prediction of thermophysical data of liquids. Fluid Phase Equilib. 172:43–72 [Google Scholar]
45. Klamt A, Eckert F, Arlt W. 45.  2010. COSMO-RS: an alternative to simulation for calculating thermodynamic properties of liquid mixtures. Annu. Rev. Chem. Biomol. Eng. 1:101–22 [Google Scholar]
46. Kacar G, Peters EAJF, de With G. 46.  2013. A generalized method for parameterization of dissipative particle dynamics for variable bead volumes. EPL 102:40009 [Google Scholar]
47. Soddemann T, Dünweg B, Kremer K. 47.  2003. Dissipative particle dynamics: a useful thermostat for equilibrium and nonequilibrium molecular dynamics simulations. Phys. Rev. E 68:046702 [Google Scholar]
48. Eckl B, Vrabec J, Hasse H. 48.  2008. On the application of force fields for predicting a wide variety of properties: ethylene oxide as an example. Fluid Phase Equilib. 274:16–26 [Google Scholar]
49. Eckl B, Vrabec J, Hasse H. 49.  2008. Molecular modelling and simulation for the process design. Chem. Ing. Tech. 80:25–33 [Google Scholar]
50. Eckert F, Klamt A. 50.  2002. Fast solvent screening via quantum chemistry: COSMO-RS approach. AIChE J. 48:369–85 [Google Scholar]
51. Loschen C, Klamt A. 51.  2014. Prediction of solubilities and partition coefficients in polymers using COSMO-RS. Ind. Eng. Chem. Res. 53:11478–87 [Google Scholar]
52. Theodorou DN, Suter UW. 52.  1985. Detailed molecular structure of a vinyl polymer glass. Macromolecules 18:1467–78 [Google Scholar]
53. Banaszak BJ, Faller R, de Pablo JJ. 53.  2004. Simulation of the effects of chain architecture on the sorption of ethylene in polyethylene. J. Chem. Phys. 120:11304–15 [Google Scholar]
54. Chen B, Potoff JJ, Siepmann JI. 54.  2001. Monte Carlo calculations for alcohols and their mixtures with alkanes. Transferable potentials for phase equilibria. 5. United-atom description of primary, secondary, and tertiary alcohols. J. Phys. Chem. B 105:3093–104 [Google Scholar]
55. Martin MG, Siepmann JI. 55.  1998. Transferable potentials for phase equilibria. 1. United-atom description of n-alkanes. J. Phys. Chem. B 102:2569–77 [Google Scholar]
56. Martin MG, Siepmann JI. 56.  1999. Novel configurational-bias Monte Carlo method for branched molecules. Transferable potentials for phase equilibria. 2. United-atom description of branched alkanes. J. Phys. Chem. B 103:4508–17 [Google Scholar]
57. Wick CD, Martin MG, Siepmann JI. 57.  2000. Transferable potentials for phase equilibria. 4. United-atom description of linear and branched alkenes and alkylbenzenes. J. Phys. Chem. B 104:8008–16 [Google Scholar]
58. Jorgensen WL, Tirado-Rives J. 58.  1988. The OPLS [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin. J. Am. Chem. Soc. 110:1657–66 [Google Scholar]
59. Jorgensen WL, Maxwell DS, Tirado-Rives J. 59.  1996. Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J. Am. Chem. Soc. 118:11225–36 [Google Scholar]
60. MacKerell AD, Bashford D, Bellott M, Dunbrack RL, Evanseck JD. 60.  et al. 1998. All-atom empirical potential for molecular modeling and dynamics studies of proteins. J. Phys. Chem. B 102:3586–616 [Google Scholar]
61. Cornell WD, Cieplak P, Bayly CI, Gould IR, Merz KM Jr. 61.  et al. 1995. A 2nd generation force field for the simulation of proteins, nucleic-acids, and organic molecules. J. Am. Chem. Soc. 117:5179–97 [Google Scholar]
62. Wang JM, Wolf RM, Caldwell JW, Kollman PA, Case DA. 62.  2004. Development and testing of a general amber force field. J. Comput. Chem. 25:1157–74 [Google Scholar]
63. Vanommeslaeghe K, Pamidighantam S, Sheetz RM, Connolly JWD, Roitberg AE, MacKerell AD. 63.  2009. Toward an automatic force field parametrization engine: assignment of parameters by analogy for the CHARMM General Force Field (CGenFF). Abstr. Pap. Am. Chem. Soc. 238:352–COMP [Google Scholar]
64. Vanommeslaeghe K, MacKerell AD. 64.  2012. Automation of the CHARMM General Force Field (CGenFF) I: bond perception and atom typing. J. Chem. Inform. Model. 52:3144–54 [Google Scholar]
65. Plimpton S. 65.  1995. Fast parallel algorithms for short-range molecular-dynamics. J. Comput. Phys. 117:1–19 [Google Scholar]
66. Gross J, Sadowski G. 66.  2002. Modeling polymer systems using the perturbed-chain statistical associating fluid theory equation of state. Ind. Eng. Chem. Res. 41:1084–93 [Google Scholar]
67. Muller EA, Gubbins KE. 67.  2001. Molecular-based equations of state for associating fluids: a review of SAFT and related approaches. Ind. Eng. Chem. Res. 40:2193–211 [Google Scholar]
68. Wertheim MS. 68.  1984. Fluids with highly directional attractive forces. 2. Thermodynamic perturbation theory and integral equations. J. Stat. Phys. 35:35–47 [Google Scholar]
69. Wertheim MS. 69.  1984. Fluids with highly directional attractive forces. 1. Statistical thermodynamics. J. Stat. Phys. 35:19–34 [Google Scholar]
70. Wertheim MS. 70.  1986. Fluids with highly directional attractive forces. 3. Multiple attraction sites. J. Stat. Phys. 42:459–76 [Google Scholar]
71. Wertheim MS. 71.  1986. Fluids with highly directional attractive forces. 4. Equilibrium polymerization. J. Stat. Phys. 42:477–92 [Google Scholar]
72. Lymperiadis A, Adjiman CS, Galindo A, Jackson G. 72.  2007. A group contribution method for associating chain molecules based on the statistical associating fluid theory (SAFT-γ). J. Chem. Phys. 127:234903 [Google Scholar]
73. Cunico LP, Hukkerikar AS, Ceriani R, Sarup B, Gani R. 73.  2013. Molecular structure-based methods of property prediction in application to lipids: a review and refinement. Fluid Phase Equilib. 357:2–18 [Google Scholar]
74. Schuld N, Wolf BA. 74.  1999. Polymer-solvent interaction parameters. Polymer Handbook 7 J Brandrup, EH Immergut, EA Grulke 247–64 New York: Wiley [Google Scholar]
75. Deglmann P, Schafer A, Lennartz C. 75.  2015. Application of quantum calculations in the chemical industry—an overview. Int. J. Quantum Chem. 115:107–36 [Google Scholar]
76. Staverman AJ. 76.  1950. The entropy of high polymer solutions—generalization of formulae. Recl. Trav. Chim. Pays-Bas 69:163–74 [Google Scholar]
77. Horvath C, Melander W, Molnar I. 77.  1976. Solvophobic interactions in liquid-chromatography with nonpolar stationary phases. J. Chromatogr. 125:129–56 [Google Scholar]
78. Oviedo-Roa R, Martínez-Magadán JM, Muñoz-Colunga A, Gómez-Balderas R, Pons-Jiménez M, Zamudio-Rivera LS. 78.  2013. Critical micelle concentration of an ammonium salt through DPD simulations using COSMO-RS-based interaction parameters. AIChE J. 59:4413–23 [Google Scholar]
79. Hildebrand JH, Scott RL. 79.  1949. The Solubility of Non-Electrolytes New York: Reinhold
80. Sun H. 80.  1998. COMPASS: an ab initio force-field optimized for condensed-phase applications—overview with details on alkane and benzene compounds. J. Phys. Chem. B 102:7338–64 [Google Scholar]
81. Fermeglia M, Ferrone M, Pricl S. 81.  2003. Computer simulation of nylon-6/organoclay nanocomposites: prediction of the binding energy. Fluid Phase Equilib. 212:315–29 [Google Scholar]
82. Ahlrichs R, Bar M, Häser M, Horn H, Kölmel C. 82.  1989. Electronic structure calculations on workstation computers: the program system turbomole. Chem. Phys. Lett. 162:165–69 [Google Scholar]
83. Furche F, Ahlrichs R, Hattig C, Klopper W, Sierka M, Weigend F. 83.  2014. Turbomole. Wiley Interdiscip. Rev. Comput. Mol. Sci. 4:91–100 [Google Scholar]
84. Schunk P, Pierce F, Lechman J, Grillet A, in 't Veld PJ. 84.  et al. 2012. Performance of mesoscale modeling methods for predicting rheological properties of charged polystyrene/water suspensions. J. Rheol. 56:353–84 [Google Scholar]
85. Phillips JC, Braun R, Wang W, Gumbart J, Tajkhorshid E. 85.  et al. 2005. Scalable molecular dynamics with NAMD. J. Comput. Chem. 26:1781–802 [Google Scholar]
86. Berendsen HJC, Vanderspoel D, Vandrunen R. 86.  1995. GROMACS: a message-passing parallel molecular-dynamics implementation. Comput. Phys. Commun. 91:43–56 [Google Scholar]
87. González-Melchor M, Mayoral E, Velázquez ME, Alejandre J. 87.  2006. Electrostatic interactions in dissipative particle dynamics using the Ewald sums. J. Chem. Phys. 125:224107 [Google Scholar]
88. Siepmann JI, Frenkel D. 88.  1992. Configurational bias Monte Carlo: a new sampling scheme for flexible chains. Mol. Phys. 75:59–70 [Google Scholar]
89. in 't Veld PJ, Hütter M, Rutledge GC. 89.  2006. Temperature-dependent thermal and elastic properties of the interlamellar phase of semicrystalline polyethylene by molecular simulation. Macromolecules 39:439–47 [Google Scholar]
90. in 't Veld PJ, Stone MT, Truskett TM, Sanchez IC. 90.  2000. Liquid structure via cavity size distributions. J. Phys. Chem. B 104:12028–34 [Google Scholar]
91. Kuppa VK, in 't Veld PJ, Rutledge GC. 91.  2007. Monte Carlo simulation of interlamellar isotactic polypropylene. Macromolecules 40:5187–95 [Google Scholar]
92. de Pablo JJ, Laso M, Siepmann JI, Suter UW. 92.  1993. Continuum-configurational-bias Monte-Carlo simulations of long-chain alkanes. Mol. Phys. 80:55–63 [Google Scholar]
93. Escobedo FA, de Pablo JJ. 93.  1995. Extended continuum configurational bias Monte Carlo methods for simulation of flexible molecules. J. Chem. Phys. 102:2636–52 [Google Scholar]
94. Sun H. 94.  1994. Force field for computation of conformational energies, structures, and vibrational frequencies of aromatic polyesters. J. Comput. Chem. 15:752–68 [Google Scholar]
95. Marrink SJ, Risselada HJ, Yefimov S, Tieleman DP, de Vries AH. 95.  2007. The MARTINI force field: coarse grained model for biomolecular simulations. J. Phys. Chem. B 111:7812–24 [Google Scholar]
96. Shinoda W, DeVane R, Klein ML. 96.  2007. Multi-property fitting and parameterization of a coarse grained model for aqueous surfactants. Mol. Simul. 33:27–36 [Google Scholar]
97. Shinoda W, DeVane R, Klein ML. 97.  2008. Coarse-grained molecular modeling of non-ionic surfactant self-assembly. Soft Matter 4:2454–62 [Google Scholar]
98. Bereau T, Kremer K. 98.  2015. Automated parametrization of the coarse-grained Martini force field for small organic molecules. J. Chem. Theory Comput. 11:2783–91 [Google Scholar]
99. Spenley NA. 99.  2000. Scaling laws for polymers in dissipative particle dynamics. Europhys. Lett. 49:534–40 [Google Scholar]
100. Maiti A, McGrother S. 100.  2004. Bead-bead interaction parameters in dissipative particle dynamics: relation to bead-size, solubility parameter, and surface tension. J. Chem. Phys. 120:1594–601 [Google Scholar]
101. Tolman RC. 101.  1948. Consideration of the Gibbs theory of surface tension. J. Chem. Phys. 16:758–74 [Google Scholar]
102. Kirkwood JG, Buff FP. 102.  1949. The statistical mechanical theory of surface tension. J. Chem. Phys. 17:338–43 [Google Scholar]
103. Andersson MP, Bennetzen MV, Klamt A, Stipp SLS. 103.  2014. First-principles prediction of liquid/liquid interfacial tension. J. Chem. Theory Comput. 10:3401–8 [Google Scholar]
104. Jeschke P, Krämer W, Schirmer U, Witschel M. 104.  2012. Modern Methods in Crop Protection Weinheim: Wiley-VCH
105. Khayet M, Fernández V. 105.  2012. Estimation of the solubility parameters of model plant surfaces and agrochemicals: a valuable tool for understanding plant surface interactions. Theor. Biol. Med. Model. 9:45 [Google Scholar]