August 27, International ANSYS Conference Nick Reynolds, Ph.D. Director, Materials Pre-Sales, US, Accelrys

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1 Multiscale modeling and simulation developing solutions which link the atomistic, mesoscale, and engineering scales August 27, International ANSYS Conference Nick Reynolds, Ph.D. Director, Materials Pre-Sales, US, Accelrys

2 What is Materials Simulation? e.g., mechanical properties of composites, fluid dynamics e.g., morphology of blends, polymer mixtures e.g., solubility, density, adhesion, permeation, crystallization e.g., chemical reactions reaction energies and activation barriers for process simulation, prediction of thin film growth 2008 Accelrys, Inc. 2

Predicting Properties for input to engineering simulations: gas diffusion through polymer membranes Problem and Modeling Approach: Long-wear soft contact lenses must transport O 2 to the evascular

3 Predicting Properties for input to engineering simulations: gas diffusion through polymer membranes Problem and Modeling Approach: Long-wear soft contact lenses must transport O 2 to the evascular cornea, making high swelling in H 2 O desirable, but diffusion of lipids, glycoproteins etc. must then be curtailed by a suitable surface treatment Molecular dynamics simulations, yields mean squared displacements, and hence diffusivities, for O 2 and H 2 O in aqueous solutions of poly(n-vinyl pyrrolidone) (PVP) 1e-04 Diffusivity 5e-05 H 2 O O Wt. frac PVP B. E. Eichinger, D. Rigby and M. Muir, Comp. Polym. Sci., (1995) 2008 Accelrys, Inc. 3

Predicting Mechanical Properties for engineering design optimization As demonstrated by recent work by Toyota Laboratories, In Silico exploration of the alloy formulation space is a very powerful

W Ti Ti Ti.99 W.01 Ti.98 W.02 Ti.97 W.03 Ti.96 W.04 Ti.95 W.05 Ti.94 W.06 Ti.93 W.07 Ti.92 W.08 Ti.91 W.09 Ti.90 W.10 Ti.89 W.11 Ti.88 W.12 Ti.87 W.13 Ti.86 W.14 Ti.85 W.15 Ti.84 W.16 Ti.83 W.17 Ti.

64 W.36 Ti.63 W.37 Ti.62 W.38 Ti.61 W.39 Ti.60 W.40 Ti.59 W.41 Ti.58 W.42 Ti.57 W.43 Ti.56 W.44 Ti.55 W.45 Ti.54 W.46 Ti.53 W.47 Ti.52 W.48 Ti.51 W.49 Ti.50 W.50 Ti.49 W.51 Ti.48 W.52 Ti.47 W.53 Ti.

28 W.72 Ti.27 W.73 Ti.26 W.74 Ti.25 W.75 Ti.24 W.76 Ti.33 W.77 Ti.32 W.78 Ti.21 W.79 Ti.20 W.80 Ti.19 W.81 Ti.18 W.82 Ti.17 W.83 Ti.16 W.84 Ti.15 W.85 Ti.14 W.86 Ti.13 W.87 Ti.12 W.88 Ti.11 W.89 Ti.

4 Predicting Mechanical Properties for engineering design optimization As demonstrated by recent work by Toyota Laboratories, In Silico exploration of the alloy formulation space is a very powerful tool for materials optimization and design Analyzed alloys: Ti 1-m X m X = V, Nb, Ta, Mo, W Composition can be easily varied and examined in terms of density, stability, elastic properties, etc. W Ti Ti Ti.99 W.01 Ti.98 W.02 Ti.97 W.03 Ti.96 W.04 Ti.95 W.05 Ti.94 W.06 Ti.93 W.07 Ti.92 W.08 Ti.91 W.09 Ti.90 W.10 Ti.89 W.11 Ti.88 W.12 Ti.87 W.13 Ti.86 W.14 Ti.85 W.15 Ti.84 W.16 Ti.83 W.17 Ti.82 W.18 Ti.81 W.19 Ti.80 W.20 Ti.79 W.21 Ti.78 W.22 Ti.77 W.23 Ti.76 W.24 Ti.75 W.25 Ti.74 W.26 Ti.73 W.27 Ti.72 W.28 Ti.71 W.29 Ti.70 W.30 Ti.69 W.31 Ti.68 W.32 Ti.67 W.33 Ti.66 W.34 Ti.65 W.35 Ti.64 W.36 Ti.63 W.37 Ti.62 W.38 Ti.61 W.39 Ti.60 W.40 Ti.59 W.41 Ti.58 W.42 Ti.57 W.43 Ti.56 W.44 Ti.55 W.45 Ti.54 W.46 Ti.53 W.47 Ti.52 W.48 Ti.51 W.49 Ti.50 W.50 Ti.49 W.51 Ti.48 W.52 Ti.47 W.53 Ti.46 W.54 Ti.45 W.55 Ti.44 W.56 Ti.43 W.57 Ti.42 W.58 Ti.41 W.59 Ti.40 W.60 Ti.39 W.61 Ti.38 W.62 Ti.37 W.63 Ti.36 W.64 Ti.35 W.65 Ti.34 W.66 Ti.33 W.67 Ti.32 W.68 Ti.31 W.69 Ti.30 W.70 Ti.29 W.71 Ti.28 W.72 Ti.27 W.73 Ti.26 W.74 Ti.25 W.75 Ti.24 W.76 Ti.33 W.77 Ti.32 W.78 Ti.21 W.79 Ti.20 W.80 Ti.19 W.81 Ti.18 W.82 Ti.17 W.83 Ti.16 W.84 Ti.15 W.85 Ti.14 W.86 Ti.13 W.87 Ti.12 W.88 Ti.11 W.89 Ti.20 W.90 Ti.09 W.91 Ti.08 W.92 Ti.07 W.93 Ti.06 W.94 Ti.05 W.95 Ti.04 W.96 Ti.03 W.97 Ti.02 W.98 Ti.20 W.99 W Phys. Rev. B 70, (2004); Science 300, 464 (2003); MRS Bull. 31, 688 (2006) 2008 Accelrys, Inc. 4

5 Predicting Mechanical Properties for engineering design optimization 450 e/a Young's Modulus (GPa) Young's Modulus (E) Density Density (g/cm 3 ) 0 E is min at Ti 0.75 W Content of W in Ti (atomic %) 2008 Accelrys, Inc. 5

In Silico Materials Analysis and Optimization Low Modulus (E) Alloy Design Rules by Toyota 350 300 250 C 11 -C 12 (GPa) 200 150 100 50 0-50 4 4.5 5 5.

6 In Silico Materials Analysis and Optimization Low Modulus (E) Alloy Design Rules by Toyota C 11 -C 12 (GPa) e/a In cubic symmetry a low modulus requires small C 11 -C 12 Analysis of the results leads to a new low modulus alloy design rule: C 11 -C 12 0 when e/a=4.2 e is the number of valence electrons/atom in the crystal unit cell a is the unit cell length (Ǻ) 2008 Accelrys, Inc. 6

7 I-Beam Design: 4-D Optimization Goal Minimize Weight Minimize Deflection Minimize Stress Design Variables Material Geometry Parameters Beam Length Beam Height Flange Width Flange Thickness Web Thickness 2008 Accelrys, Inc. 7

8 3-D/4-D Optimization Illustration Initial Design: Deflection = 12.0 Mass = Stress = 4.7 3D Optimized Design: Deflection = 1.0 (reduced 92%) Mass = (reduced 32%) Stress = 1.4 (reduced 70%) 4D Optimized Design: Deflection = 1.4 (reduced 88%) Mass = (reduced 43%) Stress = 1.3 (reduced 72%) 4-D Optimization provides added value over 3-D Optimization: 10% more weight savings 2008 Accelrys, Inc. 8

9 Multiscale modeling example: Morphology in hydrated perfluorosulfonic acid membranes Morphology of Nafion at the nanoscale? SAXS, SANS, WAXD: Nanophase segregation into hydrophilic and hydrophobic domains, Debate over the shape and structure of the ionic clusters: spherical, ellipsoid, or lamellar? Observations of the surface morphology via TEM and AFM Three-phase model consisting of spherical water clusters surrounded by sulfonic acid interfaces. Also observed the coalescence and growth of ionic clusters with an increasing water content using AFM. Use mesoscale modeling to compare and contrast with experimental observations Wescott, et al., J. Chem. Phys. 124, (2006) collaboration between Accelrys and General Motors 2008 Accelrys, Inc. 9

as they swell and coalesce. No evidence of a lamellar ordered morphology.

10 Defining the mesoscale model for nafion-water system Results: Mesoscale simulation shows an arrangement of nearly spherical domains which exhibits a linear increase in the characteristic length as they swell and coalesce. No evidence of a lamellar ordered morphology. F S Atomistic molecular dynamics simulations performed to predict the interaction energy of each pair of particle types Accelrys, Inc. 10

11 Workflow of Materials Design through multiple length scales Molecular modeling Components Parameters Properties of components interactions Mesoscale modeling Composite Parameters Morphology volume fractions of components geometry of inclusions distribution/orientation of inclusions Engineering simulation Overall Properties mechanical elasticity, stiffness swelling thermal expansion coefficients conductivity electrical conductivity dielectric constants transport properties diffusivity permeability One can also include the material as a design variable in the engineering design optimization 2008 Accelrys, Inc. 11

Leveraging Simulations to Gain Insights into Polymer Electrolyte Membrane

Bridging g the gap between theory and experiment: which theoretical approaches are best suited to solve real problems in nanotechnology and biology? Leveraging Simulations to Gain Insights into Polymer