Full-physics, full-chemistry, multi-scale materials modeling and simulation: a new tool for materials

design and optimization

Agency: DARPA

Lead university: California Institute of Technology (Caltech)

Participating university: Rutgers University

Principal Investigator: William A. Goddard, III (Caltech

Co-principal investigators: Alejandro Strachan (Caltech)Richard Muller (Caltech)Alberto Cuitiño (Rutgers)David Goodwin (Caltech)Peter Meulbroek (Caltech)

Starting Date: June 1, 2002Budget First Year: $999,540Addresses with Resumes

Table of ContentsTable of Contents_________________________________________________________________2

1. Executive summary___________________________________________________________3

2 Overall objectives and expected outcome___________________________________________5

3 Full-physics, full-chemistry, multi-scale modeling capabilities and materials design_______6

3.1 Quantum mechanical calculations___________________________________________6

3.2 First-principles-based force fields____________________________________________6

3.3 Large-scale Molecular Dynamics simulations__________________________________7

3.4 Mesoscopic Modeling______________________________________________________8

3.5 Finite Element Modeling___________________________________________________9

3.6 Multiscale modeling: bridging the scales from QM to FEM_____________________10

3.7 Materials Design_________________________________________________________12

3.8 References______________________________________________________________13

4 Large strain and energy density ferroelectrics_____________________________________13

4.1 Introduction_____________________________________________________________13

4.2 Objectives and expected outcome___________________________________________15

4.3 Task list and timeline_____________________________________________________15

4.4 References______________________________________________________________17

5 Simulation of the explosive/propulsive properties of CL-20___________________________17

5.1 Introduction_____________________________________________________________17

5.3 Objectives and expected outcome___________________________________________19

4.3 Task list and timeline_____________________________________________________19

5.4 References______________________________________________________________21

6. The Computational Materials Design Facility (CMDF)_____________________________21

6.1 Introduction_____________________________________________________________21

6.3 Software Design_________________________________________________________21

6.4 Component Descriptions__________________________________________________23

6.6 Task list and time line_____________________________________________________26

7 Personnel___________________________________________________________________28

7.1 Qualifications on the PI and co-PIs__________________________________________28

7.2 Resumes________________________________________________________________30

1. Executive summary

Advanced materials with nano- or micro-structured complexity will play a major role in future defense and commercial applications revolutionizing the properties and functionalities achievable in next-generation materials and devices. The development of such materials could be enormously accelerated if computational methodologies were available to predict and optimize their properties prior to synthesis and characterization. Developing such tools will allow the efficient design of materials with tailored properties and functionalities.

Recent breakthroughs in first-principles-based multiscale modeling indicate that the dream of computational design of materials and devices is now achievable. We propose to develop the Computational Materials Design Facility (CMDF) a first-principles-based, full-physics, full-chemistry, multiscale modeling framework and to exercise it in two classes of materials of relevance for DoD: ferroelectrics and high-energy (HE) materials.

First Principles Multiscale modeling is a new and emerging technology that provides a powerful framework to study materials properties and processes using a hierarchy of overlapping modeling methods in which the parameters and constitutive equations at each level are based on a more fundamental description or theory. First principles multiscale modeling1) starts with Quantum Mechanics (QM) [Muller ,Goddard (Caltech)], which accurately

describes atomic interactions and requires not input from experiment. QM methods are limited to 100’s of atoms, thus:

2) QM must connect to Molecular Dynamics (MD) modeling [Cagin, Strachan (Caltech)] via the development of ab initio Force Fields (FF) [Strachan, Goddard (Caltech)] that allow simulations up to nanoscale (a cube of 20 nm on a side has ~ 1 million atoms).

3) MD must connect to Mesoscale Modeling (MM) (kinetic Monte Carlo, coarse-grain FF, Level Sets and Phase Fields, etc.) [Goodwin (Caltech), Cuitiño (Rutgers)] which allow the study of microstructure (scales of microns);

4) Finally MM must connect to Macroscale Modeling [Finite Element (FE) modeling] [Cuitiño (Rutgers)] that allows the simulation of real devices.

5) The efficient design and optimization requires the integration of the various computational methods (QM, FF, MD, MM, FE) into a full-physics full-chemistry multiscale modeling framework [Meulbroek, Muller (Caltech)].To obtain first principles based results in the macroscale, it is essential that each scale of

simulation overlap sufficiently with the finer description so that parameters and constitutive laws can be determined from the more fundamental theory. This provides a systematic way of bridging the scales from electrons and atoms to real devices. Full-physics, full-chemistry multi-scale modeling of materials is critical to provide the composition-processing-structure-property relationships that are essential for efficient design of new materials and devices.

We propose to develop a general multiscale-modeling framework, illustrated in Figure 1, applicable not only to ferroelectrics and HE materials but to a wide variety of materials (metals, ceramics, semiconductors, polymers, and composites) with possible applications in: Structural materials; Electronics and optical components; Actuators and sensors; Catalysis; Conducting or separating membranes (protons, ions); Biomaterials;

Thermal protection barriers;that can be applied to numerous critical problems ranging from soldier portable fuel cells/batteries and sensors for toxins/explosives to Bragg Fiber Gratings for health monitoring of structural components and new materials for structural applications, penetrators and shields. The challenge over the next decade is not only improve the properties of the single components but also their integration at nano- or micro-scales into multifunctional materials will lead with specific functionalities outside the envelope of current materials.

Figure 1. Schematic representation of the multiscale modeling approach.

Unique aspects of our First Principles based multiscale modeling strategy include: It provides an efficient computational design framework for the development and

optimization of materials. It is general approach that can be applicable to study a wide range of materials. Being based on first principles it allows the simulation of systems never yet synthesized

(predictive power), allowing the design to be carried out in computers to obtain the best candidates for synthesis and characterization.

All the experimental data available can be used to validate our First Principles modeling tool.

Being based on ab-initio QM we can consider extreme or non-equilibrium conditions where experimental data is hard or impossible to get.

2 Overall objectives and expected outcomeWe propose to develop a first-principles-based, full-physics, full-chemistry, multiscale,

modeling framework for the design and optimization of advanced materials denoted Computational Materials Design Facility (CMDF). We will build on recent breakthroughs in multiscale modeling made by members of our team to achieve such a challenging goal, which

requires enormous advances in many areas of science and engineering. We propose to exercise the CMDF with two classes of materials of great technological importance both for commercial and Defense applications:i) High strain high energy density ferroelectric materials: Poly(vinylidene fluoride

triflouroethylene) [P(VDF-TrFE)] copolymer and PZN-PT [(1-x)Pb(Zn1/3Nb2/3)O3-xPbTiO3].

ii) High-energy material: CL-20.Being based on first principles (no empirical parameters) and by way of modeling all the

relevant mechanisms that govern materials behavior at the corresponding length and time scales the CMDF will be a very general tool for the design and optimization of a wide range of materials and not restricted to the ones we propose to study during the first year. The materials (a polymer, an oxide, and a nitramine molecular crystal) have been carefully chosen not only because of the relevance in defense applications but also because they exercise different aspects of the CMDF necessary to make it a general-purpose design tool.

We designed the following set of tasks in order to achieve our main goal: Identify all the relevant unit mechanisms that control materials behavior at the appropriate

scales from electrons and atoms to nano- and micro-structure evolution and macroscopic behavior;

Model all the relevant processes with the appropriate theory (from Quantum Mechanics to Finite Elements) with parameters and constitutive laws at each level obtained from a more fundamental model or theory (down to QM) not using any experimental data;

Validate the theoretical predictions at the various levels against experimental data; Implement the theoretical models in a multi-processor, multi-platform, component-based,

and extensible computational tool (CMDF); Integrate the wide range of modeling tools into the CMDF. The most challenging step here is

systematizing the processes of bridging the various length-scales.Such a challenging project as the development of a general CMDF is a multi-stage process;

during the first stage of the program (one year) a successful project will provide fundamental advances towards: A fundamental atomistic understanding of the detonation and burn of CL20 (coupling

between mechanical load and chemistry); Computational prediction of sensitivity and performance of CL20 HE material; A molecular based fundamental understanding of the mechanisms that govern the

electromechanical properties of P(VDF-TrFE) and PZN-PT; A First-Principles-based multiscale model of electromechanical properties for P(VDF-TrFE)

and PZN-PT; The Computational Materials Design Facility: a predictive, validated, first principles based

multiscale modeling framework for ferroelectric materials.

3 Full-physics, full-chemistry, multi-scale modeling capabilities and materials design

We have assembled at team centered at Caltech and including complementary expertise at Rutgers University to develop a first principles, full physics, full chemistry modeling framework based at its foundation on the ab-initio Quantum Mechanical description of atomic interactions but which enables the simulation of the length and time scales involved in real devices and engineering components. Our approach to achieve this challenging goal is to use a

hierarchy of overlapping modeling methodologies in which the parameters and constitutive equations at each level are based on a more fundamental description or theory. This has been our goal for over 20 years, but we have succeeded recently in bridging the scales from electrons and atoms to real device scales in describing such phenomena as single crystal plasticity in tantalum and transient enhanced diffusion of boron in Si.

The most challenging problem in our modeling effort is to integrate the results obtained at different scales with the various models in a way that requires no empirical adjustments in any stage of the hierarchy. If a given prediction is not in good agreement with experimental data, we must return to the calculation of the fundamental parameters and assumptions entering the model and determine how to obtain a more accurate description. We will not just adjust parameters to improve the agreement with experimental results. This will lead to physics based models that can be used with a high degree of confidence in systems or operating conditions where experimental data is not available. That is, this approach can be used for materials design.Unique aspects of first-principles-based multiscale modeling include: It is based solely on ab-initio QM, which allows the study of complex scenarios including

extreme and non-equilibrium conditions where experimental data are either arduous or impossible to obtain.

It allows the utilization of all available experimental data to validate our first principles modeling tool (since none of this data was used to fit parameters).

It enables the simulation of systems never yet manufactured (predictive power), allowing computational design to virtually screen the best candidates for synthesis and characterization.

3.1 Quantum mechanical calculationsWe propose to use Density Functional Theory (DFT) within the generalized gradient approximation (GGA) to calculate a variety of key materials properties and processes that require a relatively small number of atoms and characterize from First Principles the atomic interactions under a wide range of environments for HE and ferroelectric materials.The ab initio calculations will provide the fundamental information in which our modeling scheme is based, connecting to MD simulations (via First-Principles-based Force Fields), mesoscopic modeling (MM) and finite element calculations.

3.2 First-principles-based force fieldsA key element in the development of next generation materials is the prediction of accurate

atomistic structures, materials properties and processes for various compositions and processing conditions. This requires simulating large systems (millions of atoms) for relatively long times (nanoseconds). Despite enormous advances ab initio quantum mechanical methods are computationally too intensive for such scales. These costs can be decreased by many orders of magnitude by using force fields to describe the atomic interactions analytically (bonds, angles, van der Waals interactions, etc.) in a computationally efficient way. Using FFs we can carry out large-scale Molecular Dynamics and Monte Carlo simulations that allow the characterization of the unit mechanisms that govern the macroscopic behavior. A major limitation in the past was that FFs could not describe chemical reactions and complex phase transitions in which bonds are broken and formed. However we have made a recent breakthrough in this field by developing the ReaxFF [vanDuin, 2001] force field that provides an accurate description of reactive processes with a single FF, allowing simulation of complex materials and processes (including

chemical reactions, charge transfer, polarizabilities, and mechanical properties for metals, oxides, organics, and their interfaces). The parameters for ReaxFF are obtained entirely from QM and used in MD simulations to provide constitutive equations and parameters for meso- and macroscopic modeling.

The total energy of ReaxFF is described with three terms: Electrostatics: self-consistent charge transfer and atomic polarizability. The core and

shell of each atom are described with two independent Gaussian distributions [Goddard, 2002]. The total charge shell is allowed to change in response to the environment of each atom according to the charge equilibration method (QEq) [Rappe, 1991]. Also, the core and shell charge distributions are allowed to be centered around different positions in space allowing for atomic polarization.

Valence: based on the concept of partial bond orders. We define a relationship between bond distance and bond order; the bond order between each pair of atoms scales the bond, angle, and torsion energies leading to the capability to break and form bonds.

Non-bond van der Waals interactions to account for short range Pauli repulsion and longer range dispersion interactions.A key aspect of the ReaxFF is that it is based solely on ab-initio QM calculations. We ask for

a single FF to describe the following key data: The parameters determining the charge distributions are fitted to reproduce ab-initio

charges of a variety of molecules that sample different environments for each atom and atomic polarizabilities [Zhang, 2002];

The parameters determining the valence and vdW parameters are adjusted to describe the following data (also obtained from QM calculations):

o Structure and energetics of a variety of molecules sampling different types of environments, different type of bonds, a variety of bond lengths (bond dissociation curves), angles, and torsions;

o Equations of state (energy-volume and pressure-volume) for bulk systems in a wide pressure range (typically 10% volume expansion to 50% volume in compression) for various phases;

o Defect energies: vacancies, dislocations, grain boundaries, domain walls, etc.We have shown that ReaxFF accurately describes metals, oxides, and covalent systems

(including nitramines and ferroelectric oxides).

3.3 Large-scale Molecular Dynamics simulationsWe will perform equilibrium and non-equilibrium MD simulations designed to model the

fundamental processes that govern the mechanical, thermodynamic, chemical, and electrical properties of the materials choice. MD is key component in our framework that allows the dynamical characterization of the fundamental processes that control the electro-mechanical and chemical properties of materials.

3.4 Mesoscopic ModelingReaching the time and length scales associated with real devices our full-physics, full-chemistry multiscale simulation framework requires the formulation of methods to predict the behavior of structures that go beyond the scales of atomistic simulations. Predicting the behavior at this scale thus requires the use of techniques at meso/macroscopic scales where all the parameters and

constitutive laws come first first-principles theory. We propose to develop mesoscopic models of ferroelectric materials and combustion of HE materials:3.4.1 Level Sets and Phase FieldsWe propose to use Level Sets and Phase Fields techniques to simulate the evolution of polarization domains in ferroelectric materials to characterize their electromechanical properties. This capability is one component in our chain of hierarchical simulations of multiscale modeling of ferroelectric materials. The main goal of this capability is to develop a numerical formulation that can trace the topological changes on the domain structure during switching. The input for this capability will be the energetic of the transitions, the energy barriers, the corresponding mechanisms, and elastic and electric properties. These parameters will be obtained from the lower level simulations (QM and MD). The proposed numerical tool will use these ab initio parameters to concurrently minimize the total energy of the system (including elastic and electric effects) for a given state of strain and electric field. The process will proceed by moving the domain walls until a minimum is attained. Wall domain motion will be followed by a recently developed approach for tracking interfaces, the level set method [Sethian, J A, Level Set Methods and Fast Marching Methods, Cambridge University Press, 1999], which is extremely promising for modeling the temporal evolution of domains. The evolution of the level, x), set is governed by t + F | = 0, where F is the normal velocity of the interface. The location of the domain wall is given by the contour of =0. The advantage of the method is that the evolution of complex three-dimensional topologies can be readily traced. For example, the figure below shows the growth and coalescence of spherical domains.

Figure 5 Example of the level set approach. Contours of = 0 are plotted for different evolution times under constant normal velocity.

While the level set method provides an excellent approach to trace the evolution of a moving interface once a normal velocity F is given, the determination of this velocity requires the solution of a complex electro-mechanical problem. The success of this approach rests on the ability of efficiently and accurately computing the normal velocity of the front. In addition, efficient and massive scalable procedures and algorithms are needed to spatially and temporally resolve the evolution of growth and collapse process. Since the solution of the electric and elastic fields are computationally more intensive, we propose to develop a polygrid approach where the electric and elastic fields are solved in a grid coarser than the one utilized for tracing the domain walls. The location of the domain will be imposed to the coarse grid utilizing the immersed boundary approach [Leveque 1994,Fogelson 2000]. Simulations will be conducted with this approach to determine the average behavior of the collective evolution of the domain regions. It will also allow studying the kinetics of the

transformation, which is of importance to the prediction of the frequency response of the active material. During the first year we will concentrate on the 2D simulations. The main challenge of 3D simulations is the size computational grids and the associated solving time. Emphasis will be place in building the multiscale platform and the connections among the different simulation entities. 3.4.2 Combustion simulations using Cantera.

The full simulation of combustion processes involves time and length scales much larger than those accessible to atomistic simulations. Thus we will use atomistically informed mesoscopic modeling to simulate combustion of nitramines. We will use Chemical Kinetics to simulate one-dimensional heterogeneous combustion simulations that provide spatially-resolved velocity, temperature, and species concentration profiles normal to the surface.

3.5 Finite Element ModelingFE modeling allows the simulation of time and length scales associated with real devices.

The parameters and constitutive equations used at this level should be ultimately calculated for First Principles. The parameters in the constitutive laws used at the macroscopic level will have well defined physical meaning associated with a physical property or process that can be computed with more fundamental methods (MM, MD, or QM). In such a way materials design can be carried out at the macroscale by sampling the parameters space and once optimal parameters are found lower level theory can be used to find under what conditions (composition, micro- or nano-structure) the desired properties can be achieved.

Using FE modeling we will discretely describe an assembly of crystallite and amorphous regions. It is well known that the fully crystalline regions render brittle polymers with limited applications. In addition, switching frequency decreases and activation energy increases. It is therefore of technological importance to simulate the effective behavior of regions of crystalline and amorphous nature. A central component for a successful model necessitates a physical model for the crystalline region encompassing several polarization variants.

We propose to model the assembly of different polarization domains using and extending current finite element models [Kim et al 2002] where the contribution of each of the variants are lumped into a macroscopic formulation of the Gibbs free energy. This energy is a function of the polarization and elastic strain, requiring the knowledge of material parameters such as dielectric susceptibility, elastic constants (at constant polarization), piezoelectric constants, the spontaneous polarization and the spontaneous strain. These parameters will be obtained from the finer scale simulations, resulting on an atomistically informed macroscopic model.

A key aspect of the model is the inclusion of the rate dependency. This is a critical aspect to predict hysteretic behavior and the associated response frequency. Domain wall motion is the main mechanism controlling rate effects. This mechanism is introduced at this scale as an effective speed vij of the growth of the volume fraction of the variant i at the expense of the variant j.

Since our level set model (mesoscale) will be able to provide (atomistically-informed) estimates of the domain wall motion, the evolution of a volume fraction of a given variant over another one will be readily available. Thus, the FE model will simulate the behavior of several crystallite regions under the combined effect of applied strain and electric fields, where the parameters entering into the models as well as the driving mechanisms will be obtained from the finer QM and MD scales.

3.6 Multiscale modeling: bridging the scales from QM to FEMCritical in the success of our project is the ability to bridge the scales from Quantum

Mechanics to device modeling with Finite Elements while taking into account the fundamental processes at intermediate scales.

Figure 3: multiscale modeling of single crystal plasticity in Ta

We have succeeded very recently in developing a first principles based model of single crystal plasticity for Ta [Cuitino2002] using an approach similar to the one proposed here:1) We developed an accurate Force Field (qEAM FF) for Ta based on ab-initio QM

calculations, as explained in Section 1.4.2 (see top left panel of Figure 3).

Unixial tension in Ta

MD simulations

Micromechanical modeling

First Principles Force Field for Ta

QM (circles)qEAM FF (line)

Volume (A3)

Ener

gy (k

cal/m

ol)

bcc Ta EOS

QM (circles)qEAM FF (line)

Volume (A3)

Ener

gy (k

cal/m

ol)

bcc Ta EOS Edge Core energy=0.827 eV/Å

Screw Core energy=0.468 eV/ Å

KinkKink

Kink formation Energies: 0.1 - 1.1 eV

Dislocation mobility Forrest hardening

Dislocations intersection Dislocations multiplication

Dislocation mobility Forrest hardening

Dislocations intersection Dislocations multiplication

strain

strain

stre

ss

stre

ss

stre

ss

ExperimentsMitchell and Spitzig.

Acta Metallurgica1965.

First Principles Modeling

2) We identified and modeled the unit processes that govern plastic deformation at the atomic scales. We used the qEAM Force Field to calculate equilibrium properties of dislocations such as core energies and structures of screw and edge segments. We also calculated the temperature and orientation dependence of the Peierls stress, and kink nucleation energies and lengths for 1/2a<111> screw dislocations (see top right panel of Figure 3).

3) We correlated the macroscopic driving force to the macroscopic response via microscopic modeling. This last step involves two stages, localization of the macroscopic driving force into unit-process driving forces and averaging of the contribution of each unit process into the macroscopic response (bottom right panel of Figure 3).The resulting first principles, atomistically informed, model is found to capture salient

features of the behavior of these crystals (see bottom left panel of Figure 3) such as: i) the dependence of the initial yield point on temperature and strain rate; ii) the presence of a marked stage I of easy glide, specially at low temperatures and high strain rates; iii) the sharp onset of stage II hardening and its tendency to shift towards lower strains, and eventually disappear, as the temperature increases or the strain rate decreases. During the course of this proposal we will extend the framework developed for a single crystal metal to ferroelectric polymers and oxides.

3.7 Materials DesignThe development of new materials presents a wide variety of challenges; in some cases the

design engineer is after improving properties of homogeneous materials (e.g. high elastic moduli and yield strength alloys), in other cases he or she would like to find the optimum nano- or microstructure in order to maximize performance [e.g. introduce defects in P(VDF-TrFE) to decrease hysteresis and maximize energy density and strain] or the goal may be to integrate various materials together to form multi-functional systems with tailored properties (e.g. propellants with higher performance and lower sensitivity, health monitoring of structural materials or self-healing). Key to the success of the CMDF is developing systematic ways to bridge the time and length scales the separate the fundamental atomic interactions and the complex behavior of macroscopic materials and devices. As will become apparent in the following paragraphs the CMDF should have the following features:

1. The input parameters and constitutive laws at each level of theory should have well defined physical meaning identifiable with a materials property or process that can be characterized using a more fundamental level of theory;

2. At each level of theory the CMDF will have generic descriptions that can handle a wide variety of materials to perform combinatorial simulations that will allow the fast screening of materials and optimizations.

Given the wide range of problems that materials design can present their optimization may involve the use of various components of the CMDF; in the more general case we envision the following process:

1. Given the desired property find the most fundamental level (smallest scales) of theory that can describe it;

2. Combinatorial Simulations using a high level method. Vary the input parameters of the chosen level of theory (within values the make physical sense) to search for the optimal set (or sets) of input materials properties to achieve the desired functionality or overall property.

What we have done so far is dividing our overall goal into a set of desired properties that can be computed by a more fundamental level of theory (the input of the higher-level theory coincides with the output of the lower lever theory).

3. Combinatorial Simulations at more fundamental levels of theory. Thus the user will then use more fundamental level theory to find materials whose properties match the ones obtained from the higher-level simulations. Repeat this process (going to even more fundamental techniques) until a set of optimized materials is obtained;

4. Detailed calculations of the more promising materials. The design scientist will then focus all the power of full-physics, full-chemistry multi-scale modeling into the most promising candidates to obtain more accurate prediction of their properties, including ones that may not have been used in the initial stages of design (thermal or chemical stability, etc.).

Note that steps 2 and 3 go from high-level theory to more fundamental descriptions: the target macroscopic property or functionality determines the desired behavior at the smaller scales. On the other hand step 4 involves starting from the fundamental description of atomic interactions (QM) and bridging the scales up to macroscopic behavior.

We believe that use of such computational design tools could cut down the number of materials that need to be experimentally synthesized by one or two orders of magnitude, significantly reducing the cost and time involved in introducing new materials.

3.8 ReferencesA.C.T. van Duin, S. Dasgupta, F. Lorant, and W. A. Goddard, III, J. Phys. Chem. A 105, 9396 (2001) A. Strachan, T. Cagin, W.A. Goddard, III, Phys. Rev. B 60: (22) 15084 (1999).A. Strachan, T. Cagin, W.A. Goddard, III, Phys. Rev. B (2002) (submitted).A. Cuitiño, L. Stainier, G. Wang, A. Strachan, T. Cagin, W. A. Goddard, III and M. Ortiz “A Multiscale Approach for Modeling Crystalline Solids”, J. Comp. Aid. Mat. Design (in press).Goddard, Zhang, Uludogan, Strachan, and Cagin, Proceedings of Fundamental Physics of Ferroelectrics, 2002, R. Cohen and T. Egami, eds. (AIP, Melville, New York, 2002)]A. K. Rappé and W. A. Goddard III, J. Phys. Chem. 95, 3358 (1991) 8340

4 Large strain and energy density ferroelectrics

4.1 IntroductionActuators and sensors are a key component of advanced multifunctional materials and

devices with a wide range of commercial and Defense applications. We propose to study two classes of such materials:

(a) Ferroelectric copolymers Poly(vinylidene fluoride, triflouroethylene) [P(VDF-TrFE)] is a very promising material for a wide range of applications due to their large strains (over 4 %), energy density (~1J/cm3) and high frequency (<100 kHz), they are also lightweight, inexpensive, and conform to complex shapes (see Table 1). PVDF and P(VDF-TrFE) are semicrystalline polymers. Under proper treatment a ferroelectric phase, denoted -phase, (with all trans conformations as shown in Figure 4) can be induced in the crystalline regions. The structural transformation from the ferroelectic phase (all trans) to a paraelectric phase (mixed trans and gauche chains) leads to large strains (~10 %). Unfortunately this process leads to large hysteresis

that is believed to be caused by the large energy barriers associated with switching domains from the paraelectric phase to the ferroelectric one. The hysteresis can be significantly reduced by introducing defects in the polymer and decreasing the correlation length of the -phase as has been demonstrated recently by Zhang and co-workers via electron irradiation (producing nano b-phase crystals separated by trans-gauche regions) [Zhang, 1998] and by Casalini and Roland using an organic peroxide in combination with a free-radical trap leading to chemical cross linking [Casalini, 2001]. Although the detailed mechanisms are not fully understood these treatments are believed to decrease the barriers for domain wall motion and nucleation.

Figure 4: -phase PVDF chain.

(b) Lead based relaxor piezoelectrics belong to another class of materials of great importance due to their large piezoelectric coefficients. PZN [Pb(Zn 1/3 Nb 2/3 )O3], PMN [Pb(Mg 1/3 Nb 2/3)O3], PZN-xPT [(1-x)Pb(Zn 1/3 Nb 2/3 )O3-xPbTiO3] and PMN-xPT [(1-x)Pb(Mg 1/3 Nb 2/3)O3-xPbTiO3] are important members of this class with great piezoelectric properties (strains up to 1.6 % for PZN-PT, one order of magnitude higher than PZT, Table 1). Strong piezoelectricity in these materials is generally associated with the existence of a morphotropic phase boundary and their electrical properties strongly depend on their domain structures.

Table 1: properties of various actuators

In year one of this project we will focus on P(VDF-TrFE) and develop a multiscale model to predict its electromechanical properties that will be validated against relevant experiments. In the second year we will finish and fully validate all the aspects of the modeling on P(VDF-TrFE) and build on that knowledge to develop a multiscale model for PZN-PT.

H

F

Freq (Hz) MaxStrain (%) Ene. Dens. (J/g) Ene. Dens. (J/cm3)Muscle 2 40 0.07 0.07PZT 1,000,000 0.2 0.013 0.1PZN-PT 1,000,000 1.7 0.13 1PVDF 10,000 0.1 0.0013 0.0024SMA (NiTi) 100 5 15 100P(VDF-TrFE) 100,000 7 0.51 1

Currently, little is known about the molecular or atomistic mechanisms responsible for the mechanical and electro-mechanical properties of these of materials. This lack of a fundamental understanding is due to the fact that the properties of such complex polymers and ceramics are governed by a large number of inter-related processes in a wide range of scales: Nucleation of domain walls and their propagation;

o Role of defectso Composition, degree of cross-linking, presence of the amorphous phase, para- ferro-

electric boundaries [P(VDF-TrFE)];o Composition, oxygen vacancies, grain boundaries (PZN-PT);

Energetics and mobility of domain walls; Phase transformations; Microstructure and it evolution; Crack nucleation and propagation, fatigue.

Previous MD simulations in by the Goddard group lead to good predictions of dielectric constants in crystalline and amorphous PVDF and provide useful insights into the role torsional defects on the electrical properties of such polymers. Furthermore we have recently develop a new force field for BaTiO3 that accurately describes Born effective charges (including the non-isotropic behavior of Oxygen), frequency dielectric constants, equations of state and more importantly the phase transition temperature from the cubic (paraelectric) phase to the tetragonal (ferroelectric) phase.

4.2 Objectives and expected outcomeThe overall objectives of our work on ferroelectrics are to develop a First-principles-based,

multi-scale model to predict the electromechanical properties of ferroelectric polymers and lead-based oxides, validate the accuracy of our model with experimental data, and integrate all the simulation tools and the expert knowledge into the CMDF. In order to achieve our goals we propose to identify and model the unit mechanisms that control the electro-mechanical of P(VDF-TrFE) and PZN-PT at their characteristic scales with the appropriate modeling techniques; at each scale all the parameters and constitutive laws will have precise physical meaning and should computable at a more fundamental level of theory.

The development of a predictive multiscale tool will allow us to explore the role composition, processing, and nano- or micro-structure on the performance of devices and optimize their properties in computers to guide the development of new materials with tailored properties (strains and energy density; frequency; thermal and chemical stability; long term mechanical stability-brittleness).

4.3 Task list and timelineYear 1

By the end of the first year, a successful project will provide a first-principles-based full physics, full-chemistry, multi-scale model of electromechanical properties of the P(VDF-TrFE) electrostrictive polymer. Such a detailed understanding will lead to relationships between the nano-structure of the polymer (degree of cross linking, defects, size and distribution of ferroelectric nano-phase) and its properties, critical for the design of improved materials.In order to achieve our goals we designed the following tasks:1. QM calculations on representative structures to obtain:

Bond dissociation, reactive rearrangements, oxidation, surface reconstruction;

Identification and modeling of switching mechanisms in model structures; Charges and atomic polarizabilities;

2. Develop ReaxFF based on the QM calculations: Reproduce chemistry (bonds, reactions, oxidation, reconstruction, switching); Charge transfer and atomic polarizability; dielectric, piezoelectric, and pyroelectric

constants; Mechanical properties (densities, elastic constants, surface energies, yield stress);

3. MD simulations on P(VDF-TrFE): Finite temperature EoS (crystalline, nano-crystalline and amorphous phases); Ferroelectric-paraelectric phase transition temperatures as a function of copolymer

composition; Mechanisms of switching (nucleation and migration of domain walls) taking into account

the role of nano-structure (degree of cross linking, defects, size and distribution of ferroelectric nano-crystals) and under various mechanical and electrical loads;

Validation against relevant experimental data;4. Mesoscopic Modeling-Level Set/Phase Field Approach:

Atomistically informed mesoscopic modeling of domain switching; Evolution of micro- nano-structure and mechanical response under alternating electric

field; Validation against relevant experimental data;

5. Macroscopic modeling-Finite Elements: Proof of concept for multi-scale simulations for conceptual devices incorporating QM,

MD and MM modeling; Year 2

In year two of the project we will complete and fully validate our first-principles-based model of the electro-mechanical properties of P(VDF-TrFE) and use the same strategy to model the relaxor ferroelectric PZN-PT. By the end of the second year we will use the CMDF explore the role of nano- and micro-structure on the performance of P(VDF-TrFE) as well as the addition of other monomers. We foresee that such simulations could play an important role in guiding the synthesis of materials with improved performance.Tasks:1. Complete and fully validate the First-Principles-based multi-scale modeling of the

electromechanical properties of P(VDF-TrFE) including the role of amorphous regions: Characterize crack nucleation and propagation to characterize the long term behavior of

the polymers; Explicit FE modeling of amorphous/crystalline regions and their role in the long-term

performance of devices; Prediction of energy density and strain as function of applied conditions;

2. Develop a First Principles based full physics, full chemistry, for ferroelectric oxides (PZN-PT);

3. Use the CMDF to explore various nano- and micro-structures in order to optimize the electromechanical properties of P(VDF-TrFE);

4. Explore the addition of other monomers (such as chlorotrifluoroethylene) to enhance the electromechanical properties of polymers.

4.4 ReferencesQ. M. Zhang, V Bharti, X. Zhao, Science 280, 2101 (1998).R. Casalini, C. M. Roland, Appl. Phys. Lett, 79, 2627 (2001).

5 Simulation of the explosive/propulsive properties of CL-20

5.1 IntroductionEnergetic materials play an essential role in important processes ranging from automotive air bag ejection systems, to rocket engines, to excavation, to national security issues including ordinance and mines. Particularly important in such systems are control of reaction (always but only when triggered) and the amount and time scale of energy release. Particular issues here are reproducibility (the same system works exactly the same way for every device manufactured), reliability (particularly the effects of aging, where oxidation, moisture, radiation may change the nature of the explosive), and manufacturability (efficiency and reproducibility). The lack of an atomistic understanding of the processes responsible for detonation and deflagration is a significant impediment to improving the reliability and aging of solid-phase highly energetic (HE) materials and the effective development of more efficient and reliable new HE materials. A recent breakthrough in computational methods now provides the capability of using first principles theory to attain this atomistic understanding. The CMDF using the Reactive Force Field (ReaxFF) provides a means for realistic modeling of the initial steps of detonation and deflagration, allowing the multibody reactions of HE materials to be described under realistic conditions. ReaxFF has been tested against rigorous QM methods for simple reactions, establishing that ReaxFF can describe a variety of reactive processes. However, the approximation in ReaxFF must be tested under conditions appropriate for detonation.

We propose here to validate the detailed predictions of ReaxFF for reactive processes in the energetic material CL-20 (Figure 6) against all available data, under conditions as close as possible to experiment and to analyze our results in terms of quantities accessible to experiment. This study will include a study of the pure CL-20 material as well as consideration of the binders and aluminum that are present when the material is used as a propellant. In addition we propose to build the foundation for describing the oxidation, diffusion, and radiation damage processes associated with aging.

Figure 6: the nitramine CL20Enormous progress in understanding the mechanisms for the chemical processes in high

explosives was achieved in 2000 and 2001 by Chakraborty et al. [1-3] who carried out accurate QM calculations on the unimolecular decomposition processes in RDX and HMX. In principle QM provides a means for predicting all of the above processes at the requisite level of resolution (spatial and temporal) in the absence of experimental inputs. However, QM is completely

N NN N

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impractical for the scales of time and length relevant to high explosives, and traditional force fields have not been able to describe the reactive events in which chemical bonds are formed and broken. Fortunately a recent breakthrough – ReaxFF – in computational methods now provides exactly these capabilities.

Figure 7 compares the energetics between the QM methods and ReaxFF for the primary decomposition pathways for RDX. Only one reaction along the N-N homolysis pathway was used to determine the ReaxFF parameters, and all of the subsequent reactions in the N-N homolysis pathway, as well as all of the reactions in the concerted and HONO elimination pathways, use these parameters. ReaxFF not only describes the intermediates in the three decomposition pathways but also the transition states. We know of no other FF with this level of transferability of parameters. ReaxFF allows simulations on systems 1000 times larger than possible using QM alone. These larger simulations allow for the first time direct investigations of shock initiation of energetic materials using realistic potentials.

Figure 7. Unimolecular decomposition mechanisms for RDX. Thin lines show ab initio QM results for intermediates and transition states and thick lines show values obtained with ReaxFF.

Figures 8 and 9 demonstrate the types of simulations possible with the ReaxFF. Both figures show impact “flyer plate” computational experiments between slabs of RDX molecules. Figure 8 shows a progression of frames from a computational experiment where the impact velocity is 10 km/sec. Here we see significant fragmentation of the molecules as the materials begin to expand. In contrast, Figure 9 shows a computational experiment where the impact velocity is only 2 km/sec; in this experiment there is little or no fragmentation of the RDX molecules. Analysis of the shock simulations lead to a Hugoniot curve [particle velocity vs. shock velocity] in very good agreement with experimental results: the calculated sound velocity is only 8% larger than the experimental value. Thus, not only the chemistry is described accurately [Figure 7] with ReaxFF but also the mechanical properties of materials.

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Figure 8: Flyer plate experiments on RDX using an impact velocity of 10 km/sec. The plates are infinite (periodic) in two dimensions, but finite along the direction of impact. The first frame shows the beginning of the experiment. The second frame shows the point at which the two

plates make contact. A shock wave then propagates through each slab until it reaches the point shown in the third frame, which is the point of maximal compression. The material then fails,

shown in the fourth frame, and the material initiates.Figure 9: End of a flyer plate experiment at a lower impact velocity (2 km/sec), showing no initiation of the materials.

5.3 Objectives and expected outcomeThe overall objectives of our work on HE materials are to develop a First-principles-based,

multi-scale model to predict the properties of CL-20, validate the accuracy of our model with experimental data, and integrate all the simulation tools and the expert knowledge into the CMDF. In order to achieve our goals we propose to use atomistic simulations to characterize the unit mechanisms that govern combustion (condensed-phase reactions, reactions at surface and in the gas phase, transport properties, equation of state of products) and use such information in mesoscopic chemical kinetics simulations that allow the simulation of the combustion process with realistic time and space scales. Such a fundamental understanding and its implementation in the CMDF will allow us to explore the role of binders or metallic inclusions on performance and sensitivity and may lead to the development of improved HE materials.

4.3 Task list and timelineYear 1

By the end of year one a successful project will provide full physics, full chemistry, first-principles-based atomistic modeling of the important mechanisms of combustion of CL-20 [including condensed (bulk and surface) and gas-phase reactions] and the use of such information in mesoscopic modeling of combustion.

We designed the following set of tasks to accomplish our objectives:

1. MD simulations: use current ReaxFF for nitramines to study combustion of CL-20:

Develop an molecular-based model of the interface between the condensed-phase and gas phase propellant, focusing on the characterization of the species leaving the surface;

Bulk and surface reactions as a function of temperature;

Characterization of the gas-phase reactions of species found to leave the propellant surface during combustion;

Transport properties;

2. QM calculations for the important unimolecular reactions (as predicted by ReaxFF) to validate and, if necessary, tune the ReaxFF:

Transition states and intermediates for the important reactions;

3. Mesoscopic calculations of surface combustion using first principles data:

Develop a one-dimensional heterogeneous combustion model that can compute spatially-resolved velocity, temperature, and species concentration profiles normal to the surface. The model will allow any number of reversible or irreversible reactions, and will include transport of heat, species, and momentum in both the solid and gaseous phases.

Based on the MD simulations, develop initial reaction mechanisms for the solid near the surface, on the surface, and in the gas for use in the combustion model

Develop initial models for the transport properties of the solid and gas phase valid from room temperature to high temperature.

Year 2The goals for the second year of our effort in HE propellants are to complete and validate our

multi-scale model of combustion and add the necessary capabilities to evaluate role of binders and metals and assess sensitivity (to shock detonation).Tasks:1. Complete and fully validate Multi-scale Model for combustion of CL-20;

Prediction of ignition and burn rates and comparison with experiments; Extend the multi-scale model to include the presence of binder and metal; Role of polymer binder and metallic inclusions in burn rate;

2. Predict sensitivity of HE materials: Use atomistic modeling to study shock propagation in CL-20; Validate the atomistic predictions by comparison with experimental data;

Develop models to obtain accurate transport properties, including multi-component diffusion and the Soret effect. In the gas phase, this will be done using QM-derived parameters for a Stockmayer potential (potential well depth, collision diameter, dipole moment or polarizability) with rigorous expressions from kinetic theory. In the solid, we will determine diffusion coefficients and thermal conductivity from MD simulations.

5.4 References1. D. Chakraborty, R. P. Muller, S. Dasgupta, W. A. Goddard III. Mechanism for unimolecular

decomposition of HMX (1,3,5,7-tetranitro-1,3,5,7-tetrazocine) an ab initio study. J. Phys. Chem. A. 105, 1302 (2001).

2. D. Chakraborty, R. P. Muller, S. Dasgupta, W. A. Goddard III. The mechanism for unimolecular decomposition of RDX (1,3,5-trinitro-1,3,5-triazine), an ab initio study. J. Phys. Chem. A. 104, 2261 (2000).

3. D. Chakraborty, R. P. Muller, S. Dasgupta, W. A. Goddard III. A detailed reaction mechanism for the decomposition of nitramines RDX and HMX. J. Comp. Aided Mat. Design. In press. (2002)

6. The Computational Materials Design Facility (CMDF)

6.1 Introduction We propose to develop a prototype “Computational Materials Design Facility” (CMDF): a first-principles-based, full physics, full chemistry, multiscale, modeling framework for the design and optimization of materials. The CMDF consists of tools to rapidly assay materials properties simulation results, rank the success of the simulations, and visualize the most promising candidates.

The CMDF makes use of a wide variety of modeling techniques to achieve a seamless bridging of scales from electrons and atoms to devices; the software to perform such simulations consists of a large number of incompatible programs, each of which has its own idiosyncrasies. For example, not all simulation packages support advanced parallel hardware, and most packages produce idiomatic output that is not easily interpreted by a single analysis program. What is required to simplify the process of predicting macroscopic materials properties is to understand the results of simulations (visualization), to control simulations from a central location utilizing a simple, cross-platform, cross-application interface, interface to multiple simulation packages running on a network of computers, and to organize and relate groups of simulations to macroscopic properties.

In order to bring these innovative tools to the scientific community, significant development time (on the order of three to five years) is envisioned. Phase I will consist of developing functional prototypes for all aspects of the Computational Materials Design Facility. Phase II will consist of refinement and integration: turning the ideas developed during Phase I into a production-capable system, implementing the expert knowledge developed on multi-scale materials modeling into the CMDF (smart defaults, scripts).

6.3 Software DesignWhile developing scientific results is the motivation for the CMDF, key to the success of the project is a clear model for software design. Many of the issues that face computational scientists today are not a lack of methods, but difficulty in applying those methods due to complex, hard-to-use, non-intuitive software. We propose to develop the framework software that allows the unification of many of these methods in order to solve the most difficult challenge in computational science: simulating processes that span many length and time scales. The CMDF is designed to fulfill two tasks: the preliminary exploration for new materials (testing out concepts, and exploring the problem space), and the calculation of materials properties (high-performance computing based on methods developed above).

The design for the CMDF is shown in Figure 10, below:

Database

Dispatcher

UserI nterf ace:I carus

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Additionalaggregation functions

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Figure 10: CMDF Design

Key pointsThe software design for the CMDF will create a test facility for simulation that makes it easier to: Perform simulations Automate repetitive calculations Automate cascading simulations Capture and visualize results Act as a toolkit, allowing easy extensions and additions Allow the non-expert to access High-Performance Computing (HPC)

facilitiesMotivations

In order to take the next step in developing materials and validating scientific methods in understanding design processes, the following problems must be addressed:

1. Predicting macroscopic materials properties from first principles requires the aggregation of multiple simulation runs. Solution: devise a model with the ability to link multiple simulation results (serially or in parallel)

2. Simulation software is generally designed to work on a single length scale. Software at different scales is largely incompatible and idiosyncratic. Solution: devise a model with the ability to spawn multiple simulation packages using a common control language

3. Developing simulation software is a moving target. Computational scientists are continually developing new methods and software to solve today’s intractable problems. Solution: devise a model with the ability to add packages to the facility

4. Simulation packages can produce huge amounts of data, much of which requires expert analysis. Solution: devise a model with the ability to summarize results into meaningful statistics and properties.

5. Error analysis is difficult for the non-expert, as is identifying the major sources of error. Solution: devise a model with the ability to aggregate simulations, estimate errors in the aggregations using

simple statistics, and automatically invoke new simulations to minimize those errors

6. Complex simulations require huge computational resources such as large clusters of computers. Solution: devise a model with the ability to handle multiple processes and multiple machines nearly transparently.

7. Modern component-oriented software design argues that the major pieces of a large software package need to be exposed and scriptable, to allow for batch processing and reuse. Solution: devise a model with the ability to take external commands from a script, and to expose a fixed API.

6.4 Component DescriptionsFrom a software perspective, the CMDF consists of a series of components, each of which is linked to, but programically independent of, the others. The major parts are the database, the user interface (UI), the node, the dispatcher, and the aggregator. The properties of each are as follows: 6.4.1 DatabaseThe database is the repository for all simulation data. And is the key component that allows for the transcendence from one length scale to the next. It allows for the results of multiple simulations to be aggregated into higher-order parameters. The project requires a relational database that

(a)Responds to high-level queries in SQL1

(b)Forms a repository for simulation and program information (c) Provides efficient, extensible, flexible storage of atomistic data (d)Works with both periodic and non-periodic systems (e)Can reference files and/or large block storage

Design: The most common and refined database systems are relational databases (RDB); they consist of a back-end control daemon that handles communication, and a data structure model that organizes the data into a series of tables. Examples of common relational databases are MySQL, PostgreSQL, SQL Server, and Oracle 9i.

In order to prototype the system, the database will be constructed using the supported open source product, PostgreSQL. PostgreSQL is an “object-oriented relational database” that supports the major database connection methods, such as SQL queries, ODBC, JDBC, and Perl-DBI. Development of the database takes on two forms: developing the database table structure, and developing the communications logic that fetches data from the database to each module (the user interface, node, dispatcher, and aggregator ‘middleware’). Communication between the database and the rest of the CMDF will be via TCP/IP socket connections utilizing the PyGres protocol. Communication strategies are outlined in Figure 11, below:1 Previous work has indicated that SQL-driven relational databases provide adequate abstraction for simulation data, while providing a powerful search interface. Current generation relational database (e.g., PostgreSQL) also provide hooks for user-supplied back-end extensions that will be used in the aggregator.

DatabaseSQL queries

MatchingSimulationResults

Job Parameters

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6.4.2 User InterfaceThe user interface (UI) is the only part of the MTF that the average user need see. It provides visualization and control. The CMDF is designed for the non-expert in mind, as both a scientific tool and an education platform. Nowhere is this more evident that the user interface. The UI will have the following properties:

(a)Ability to display large systems at a variety of length scales

(b)Ability to harness complex display tools and methods, such as 3D graphics, surface displays (charge, Van der Waals, solvation), planes of transparency to display the interiors of complex 3D structures (utilizing complex OpenGL graphics)

(c) Control all aspects of the simulation with an intuitive design that provides intelligent defaults to allow the non-expert to get approximate answers when exploring a new system

(d)Extensibility, where new simulation packages and methods are easily included in the UI

(e)Component-oriented design, to speed the development and debugging process

(f) Error analysis that can suggest methods for decreasing total error; i.e., suggest aspects of the system that require most careful high-precision analysis

Design: The user interface (UI) is a Python/GTK shell. Choosing Python implies that the UI will be platform independent, be easy to extend to new problem areas, and can be supported by a large scientific programming community. Considerable effort over the past 18 months has been made at this research group to develop a package of visualization methods called Icarus. This effort will form the core of the UI, leveraging previous efforts.

6.4.3 Dispatcher The tool that will allow simulations to take full advantage of advances in computer hardware and software is the dispatcher subsystem. The dispatcher will have two modes, depending on user needs: development mode and production mode. In general, development mode will consist of multiple ‘jobs’ performed on single processors. It is likely that the user will have multiple simultaneous jobs running at a given time, across a wide variety of computational platforms. The dispatcher will coordinate these multiple jobs. The second mode the dispatcher will use is the ‘production’ mode, where a single, complex computation utilizes multiple processors (HPC). To get the most out of production mode, the code controlled by a node must be parallelized, utilizing a strategy such as MPI. The dispatcher is an abstract layer that represents a ’job’ (a potentially multi-node, multi-process simulation) that provides the following features:

(a)Present an external API for multiple entry points (i.e., can be used as a toolkit)

(b)Extensible (c) Communicate with front-end (d)Communicate with back-end objects (nodes and database)

Design: The dispatcher is an abstract communications structure that sits between the user interface and one or more nodes. It controls and marshals simulation “jobs”, hiding complex communication strategies from the user. It implements a “manager-worker” motif. A unified dispatcher model will be achieved using component-based abstract interfaces.

In development model, communication will consist of XML-RPC2 calls that coordinate the communication between user interface and node. RPC is a robust method used to communicate between processes, potentially running on different computers. In production mode, our component-based program design will allow virtual parallelization for uncoupled problems using a manager-worker scheme. Further, we will develop an MPI-aware dispatcher to solve weakly-coupled problems. For strongly coupled problems, the most efficient parallelization must occur within a single simulation package / problem space. Towards that end, we will make strides towards parallelizing our in-house development efforts. The dispatcher will be fully ‘tera-grid-able’, capable of interfacing with modern heterogeneous, fault-tolerant HPC grids. Communication strategies are outlined in Figure 12, below:

2eXtensible Markup Language Remote Procedure Call, a platform independent, net-aware protocol that is supported by many programming languages.

DispatcherJob andNetworkstatus

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Figure 12: Communication with the Dispatcher

6.4.4 NodeThe node is an abstract control structure that interfaces with external programs in a uniform way and sits on the computational server platform. This abstraction allows for uniform treatment of two computational scenarios: the multiple single-process runs (“development”), and a single, multi-process run (“production”). The node will:

(a)Control an individual simulation module (b)Start jobs (c) Report status (d)Parse results (e)Provide fault control (f) Present an abstract interface that simplifies the control of current

packages and aids in the development of new packages Design: A node represents a fundamental, abstract unit of computation. It is the interface between a simulation package and the MTF, starting the package, parsing its results, and storing them in the database. Three popular simulation packages will be supported in the prototype: “Jaguar” (Schrödinger Inc), a quantum mechanical simulation package; “Seaquest” (Sandia National Labs), a plane wave quantum mechanical package, and “Reaxff” (MSC/Caltech), a molecular force field simulation package. Each simulation package supported in Phase I is command-line driven. The packages each take one or more input files, and generates one or more output files. To extract the results of a simulation from such a package requires the design of a component to parse through these files. The node will start runs, monitor runs, parse output, and return the parsed output to the database.

For production mode, a node will monitor and interface with a multi-process simulation running on a grid of computational processors. Individual nodes will monitor each process. This methodology allows different levels of interface between existing code and the CMDF. For fully MPI-compliant code, the node will simply form a reporting mechanism. For less parallel code, the node will take a more active role in resource management.

Several strategies for interfacing with existing code will be implemented. For monolithic code, a shell-based control will be used. For library-toolkit type code, an extension mechanism (e.g., SWIG) will be used. 6.4.5 AggregatorThe aggregator sits between the user interface and the database. It is the component that utilizes the results of multiple simulations to make predictions of materials properties. While SQL queries provide a powerful means of sorting and aggregating results into scalar properties, many materials properties predictions rely on complex calculations. The aggregator will:

(a)A location for property prediction algorithms

(b) Allow error estimation and propagation

Design: The aggregator is an abstract communications structure that sits between the user interface and the database. It controls and marshals simulation “results”, hiding complex communication strategies from the user. For extensibility and introspection considerations, the aggregator will be designed in Python.

6.6 Task list and time lineYear 1

At the end of the first year a successful project will provide a complete, functional CMDF that allows non-experts to simulate the properties of a wide variety of materials using multiscale methods. The following methods will be integrated into the CMDF during the first year:1. Quantum Mechanics. A general purpose DFT code for molecules and crystals for high

accuracy calculations (structures, chemical reactions, zero-temperature Equations of State). Features of the DFT engine include: GGA and LDA approximation for the exchange and correlation functional; Molecular Mechanics: atom and cell optimization;

2. Molecular Dynamics and Force Field calculations. A general purpose MD code for simulating the fundamental unit mechanisms governing the macroscopic behavior of materials (such as: equations of state, transport properties, reactions in condensed and gas-phase HE materials, domain wall motion). Features of the MD engine include: Generic non-reactive force fields [DREIDING, Universal Force Field, Charge

Equilibration (QEq)] that provide reasonable accuracy for equilibrium structures of all materials;

New-generation reactive force field (ReaxFF) based on First Principles that provide a good description of reactive processes (but the parameters developed during the first two years will be limited to the systems under study);

Molecular Mechanics: atom and cell optimization to obtain equations of state; Molecular Dynamics: various ensembles (NVT, NPT, NVE), non-equilibrium (shocks,

friction, etc.) to simulate dynamical processes at the atomistic level;3. Mesoscopic Simulations-Level Set/Phase Field approach for the simulation of the electro-

mechanical coupling in ferroelectric materials allowing the development of nano- micro-structure-property relationships: Includes coupling between the elastic, electric and polarization fields; Allows smooth boundary interfaces; Includes rate dependency based on Langevin kinetic equations;

Will generate the materials response to specified initial nano-structure and electromechanical external loads

4. Mesoscopic Simulations-Combustion simulations using Cantera. This engine will allow one-dimensional heterogeneous combustion simulations that provide spatially-resolved velocity, temperature, and species concentration profiles normal to the surface: Cantera is an open source chemical kinetics package with many of the same features as

ChemKin; Additionally Cantera supports non-ideal equations of state;

5. Macroscopic simulations. A Finite Elements (FE) modeling engine for the simulation of ferroelectric materials that will allow the simulation of length and time scales of real devices. Features of the FEM engine include: Multi-physics Finite Elements with embedded features for domain switching,

polarization, and rate effects. Atomistically informed energy functionals (elastic and dielectric constants, domain wall

nucleation and migration energies)By the end of year one the CMDF will contain the following features:1. User oriented, intuitive, Graphical User Interface (GUI)2. Dispatcher, controlling a 'job'.  A job is a potentially multi-process, multi-platform entity.  A

separate process to control all this is desirable.  3. Node, an interface between the dispatcher and a module that actually does something

simulation-wise.  This gives us an abstraction even when using existing code4. Aggregator, a location for doing the complex averaging / aggregating, and computation to

level-jump.5. Driven by:

GUI; Scripts (python); As the GUI in used for various applications, it will generate script that could later be used

to completely redo the same process or which can be modified directly for iterative or modified processes

Year 2The main goals for the second year of our project are: Continue the integration of the various computational models into the CMDF, Improve single node performance and scalability of the simulation engines, Integrate the advances on multi-scale modeling into the CMDF.Tasks:1. Database:

Responds to high-level queries in SQL; Forms a repository for simulation and program information; Provides efficient, extensible, flexible storage of atomistic data;

2. Automate the process of optimizing force fields from QM calculations;3. Optimize the procedure of self-consistent charge evaluation;4. Develop scripts and set smart defaults in order to build the expert knowledge into the CMDF;

7 Personnel

7.1 Qualifications on the PI and co-PIsWilliam A. Goddard III (PI), Charles and Mary Ferkel Professor of Chemistry, Materials Science, and Applied Physics, Prof. Goddard has been a pioneer in the first-principles based multiscale simulation methodologies that form the backbone and basis of the modeling effort in this proposal. In addition, he has been at the forefront of the application of these methods to problems in materials and nanotechnology that form the central topics of this project. His contributions in these areas have recently been recognized by:

The 1999 Feynman Prize in Nanotechnology Theory (shared with Dr. Cagin and Ms. Yue of the MSC, also involved in this project)

The 2000 NASA Space Science award (shared with Dr. Vaidehi of the MSC and with Drs. Jain and Rodriguez of JPL/NASA)

The 2000 Tolman Award from the American Chemical Society (SCALACS) for research in Chemistry

The ISI 2000 Most highly cited authors in chemistry (99 top cited authors for 1981-1999)

In addition he is the Director and Founder of the Materials and Process Simulation Center (MSC) at Caltech. This center with a staff of ~45 scientists (including graduate students) all dedicated to multiscale modeling of materials provides a critical mass of expertise in all areas relevant to this proposal from development of QM, FF, MD, and mesoscale methodologies, to application to metallic, ceramic, semiconductor, polymer, fullerene, and biological systems, to development of chemical and mechanical processes. The MSC will serve as a resource to aid the current proposal far beyond the several scientists to be funded in the proposed project, providing a greatly increased probability of success.Alberto Cuitiño, Associate Professor of Mechanical and Aerospace Engineering, Rutgers University. Cuitiño brings expertise in Multiscale modeling of advanced materials, computational mechanics. He has ample experience in developing constitutive models for non-linear materials.Alejandro Strachan, Manager of Materials Properties and Force Field Technology, MSC, Caltech. Strachan brings expertise in developing first principles force fields and in their use, with MD, to characterize fundamental materials properties including plasticity, failure, phase transitions, shock waves. He also has experience in developing multiscale models to predict from first principles the behavior of materials at macroscopic scales.Richard Muller, is an expert in both QM simulation and HE materials. He will assist in the CL-20 simulations in this project, and help integrate the QM simulation capability into the MTF to automatically tune the ReaxFF.Peter Meulbroek,

7.2 ResumesWILLIAM A. GODDARD, III

Beckman Institute (139-74)California Institute of Technology

1201 East California Blvd.Pasadena, California 91125 USA

http://www.wag.caltech.eduPhone:(626) 395-2731, 395-2730, FAX:(626) 585-0918

email: wag@wag.caltech.edu, copy: shirley@wag.caltech.eduCurrent Positions at the California Institute of Technology: Charles and Mary Ferkel Professor of Chemistry, Materials Science, and Applied PhysicsDirector of Materials and Process Simulation Center (MSC): 1990-presentPrevious Professional Positions (all at Caltech):1965-1978 Assistant, Associate, and Full Professor of Theoretical Chemistry1978-1984 Professor of Chemistry and Applied Physics1984-1990 Director of NSF Materials Research Group1992-1997 Director of NSF Grand Challenge Applications Group1984-2001 Charles and Mary Ferkel Professor of Chemistry and Applied Physics1990-present Director of Materials and Process Simulation Center (MSC)2001- present Charles and Mary Ferkel Prof. Chemistry, Materials Science, and Applied PhysicsEducation: Ph.D. Engineering Science (minor physics), California Institute of Technology, 1965;B. S. Engineering (Highest Honors), University of California, Los Angeles, 1960. Awards and Honors:ISI Highly Cited Chemist for 1981 to 1999 (http://isihighlycited.com)NASA Space Sciences Award (2000)Richard Chase Tolman Award, ACS (2000)Feynman Prize for Nanotechnology Theory (1999)Richard M. Badger Teaching Prize (1995)Fellow of American Association for the Advancement of Science (1990)ACS Award for Computers in Chemistry (1988)Member of International Academy of Quantum Molecular Science (1988)Fellow of American Physical Society (1988)Member of National Academy of Science (1984)Buck-Whitney Medal (1978)Professional Memberships: California Catalysis Society (President 1997-8); American Chemical Society;American Physical Society (Fellow); Materials Research Society; American Vacuum Society.Other Professional Activities:Member, Board of Directors Gordon Research Conferences 1988-1994Cofounder of Molecular Simulations Inc. (1984), Board Directors (Chair: 84-91; Memb 84-95) Cofounder of Schrödinger Inc. (1990), Member Board of Directors 1990-2000;Cofounder Systine Inc. (1998). (originally Materials Research Source LLC)Cofounder Bionomix (2000), Chairman of Board of Directors (2000 to present)Research publications: Over 475, see http://www.wag.caltech.edu/publications/papers-byapp

ALBERTO CUITIÑODept. of Mechanical and Aerospace Engineering

Rutgers UniversityPiscataway, New Jersey, 08854

Phone: (732) 445-4210 Fax: (732) 445-3124E-mail: cuitino@jove.rutgers.edu; http:// cronos.rutgers.edu/~cuitino/

EducationPh.D., Solid Mechanics 1993 Brown University

Minor in Materials ScienceM.S., Applied Mathematics 1992 Brown University

B.S. Civil Engineering 1986 University of Buenos AiresProfessional Experience1999-Present Associate Professor of Mechanical and Aerospace Engineering

Rutgers Univeristy2000-2001 Visiting Professor, California Institute of Technology1993-1999 Assistant Professor, Rutgers University1989-1993 Research Assistant, Brown UniversityResearch InterestsComputational Mechanics, micromechanical modeling of advanced materials, multiscale modeling.Professional ActivitiesAssociate Editor, Latin American Applied Research.Relevant Publications Cuitiño, A. M., Stainier, L., Strachan, A., Wang, G., Çaðýn, T., Goddard, III, W. and Ortiz, M., A

Multiscale Modeling Approach for Ta Crystals. Journal of Computer Aided Material Design, in press

Cuitiño, A. M, Alvarez, M. C., Roddy, M. J. and Lordi, N. G. Experimental characterization of the pore structure during compaction of granular viscous solids, Journal of Materials Science, in press .

Stainier, L, Cuitiño, A. M. and Ortiz, M., 2001 Hardening, Rate Sensitivity and Thermal Softening in BCC Crystals. Journal of the Mechanics and Physics of Solids, in press.

Wang Y., Jin, Y.M., Cuitiño, A. M. and Khachaturyan, A. G., 2001. Nanoscale Phase Field Theory of Dislocations: Model and 3D Simulations, Acta Metallurgica et Materialia, 49, pp. 1847—1857.

Gioia, G., Wang, Y., and Cuitiño A. M., 2001. The Energetics of Heterogeneous Deformation in Open-Cell Solid Foams, Proceedings of the Royal Society of London, Series A., 457, pp. 1079—1096.

Wang Y., Jin Y.M., Cuitiño, A. M., Khachaturyan 2001. A.G. Phase field microelasticity theory and modeling of multiple dislocation dynamics, Applied Physics Letters 78: (16) pp. 2324—2326.

Ortiz, M., Cuitiño, A. M., Knap, J. and Koslowlsky, M. 2001 Mixed Atomistic-Continuum Models of Material Behavior: The Art of Trascending Atomistics and Informing Continua, MRS bulletin, 26: (3) pp. 216-221.

Wang, Y., Gioia and Cuitiño, A. M. 2000 The Deformation Habits of Compressed Open-Cell Solid Foams, Journal of Engineering and Technology (ASME), 122: (4), pp. 376—378.

Wang, Y. and Cuitiño, A. M. 2000 Three Dimensional Nonlinear Open-Cell Foams with Large Deformations Journal of the Mechanics and Physics of Solids, 48, pp. 961—988.

ALEJANDRO STRACHANMaterials and Process Simulation Center, Beckman Institute (139-74)

California Institute of Technology1201 East California Blvd.

Pasadena, California 91125 USAhttp://www.wag.caltech.edu

Phone:(626) 395-9137, FAX:(626) 585-0918email: strachan@wag.caltech.edu

EducationPh. D., Physics 1998 University of Buenos Aires, ArgentinaB.Sc., Physics 1994 University of Buenos Aires, ArgentinaProfessional Experience2001-present Director Materials Properties and Force Field Technologies, MSC, Caltech.1999-2001 PostDoctoral Scholar, Materials Process Simulation Center, Caltech.1994-1998 Graduate Research/Teaching Assistant, Physics Department, University of Buenos Aires, Argentina.

Research InterestsDevelopment of First Principles Force Fields for metals and ceramics. Atomistic studies of phase transitions, plasticity, and failure in metals and ceramics. Development of Multiscale Models that bridge time and length scales in materials modeling.

Professional ActivitiesMember of American Physical Society.

Relevant PublicationsGoddard, Zhang, Uludogan, Strachan, and Cagin, Proceedings of Fundamental Physics of

Ferroelectrics, 2002, R. Cohen and T. Egami, eds. (AIP, Melville, New York, 2002)G. Wang, Alejandro Strachan, Tahir Cagin, and William A. Goddard III , “Atomistic

characterization of screw dislocation in Ta”, Phys. Rev. B, submitted.A. Strachan, T. Cagin, O. Gulseren, S. Mukherjee, R. E. Cohen and W. A. Goddard, III, “First

Principles Force Field for Metallic Tantalum”, submitted to Phys. Rev. B.A. Cuitiño, L. Stainier, G. Wang, A. Strachan, T. Cagin, W. A. Goddard, III and M. Ortiz “A

Multiscale Approach for Modeling Crystalline Solids”, J. Comp. Aid. Mat. Design (in press).Alejandro Strachan, Tahir Cagin, and W. A. Goddard, III, “Crack propagation in a Tantalum

nano-slab”, ”, J. Comp. Aid. Mat. Design (in press).Yue Qi, Alejandro Strachan, Tahir Cagin and William A. Goddard III, “Large Scale Atomistic

Simulations of Screw Dislocation structure, Annihilation and cross-slip in FCC Ni”, Mat. Sci. Eng. A 309 156, (2001)..

Guofeng Wang, Alejandro Strachan, Tahir Cagin, and William A. Goddard III , “Molecular Dynamics Simulations of ½ a<111> Screw Dislocation in Ta”, Mat. Sci. Eng. A 309 133, (2001).

A. Strachan, T. Cagin, and W. A. Goddard, III, “Critical behavior in Spallation Failure of Metals”, Phys. Rev. B, 6305, 0103 (2001).

A Strachan, T. Cagin, W. A. Goddard, III, "Phase diagram of MgO from Density Functional Theory and Molecular Dynamics simulations," Phys. Rev. B 60, 15084 (1999).

Richard P. MullerDirector, Quantum Technologies 626-395-2732Materials and Process Simulation Center 626-585-0918 (FAX)Beckman Institute (139-74) rpm@wag.caltech.eduCalifornia Institute of Technology http://www.wag.caltech.edu/home/rpmPasadena, CA 91125EDUCATION1994 California Institute of Technology, Ph.D., Chemistry

Development and Implementation of Quantum Chemical Techniques for Application to Large MoleculesWilliam A. Goddard, III, Advisor

1988 Rice University, B.A., Cum Laude, ChemistryFELLOWSHIPS AND HONORS Secretary/Treasurer, California Catalysis Society, 1997-2000 Zevi W. Salsburg Award in Chemistry, Rice University, 1988 B.A. Cum Laude, Rice University, 1988 Undergraduate Research Award, American Institute of Chemists, 1988 National Science Foundation Graduate Fellowship, Honorable Mention, 1990PROFESSIONAL EXPERIENCE1997 - Present, Director, Quantum Simulations, Materials and Process Simulation Center1994 - 1997, Postdoctoral Research at The University of Southern California,RECENT RELEVANT PUBLICATIONSComputational Insights on the Challenges for Polymerizing Polar Monomers. Dean M. Philipp, Richard P. Muller, and William A. Goddard, III. Journal of the American Chemical Society, Submitted.Si + SiH4 Reactions and Implications for Hot-Wire CVD of a-Si:H. Computational Studies. Richard P. Muller, William A. Goddard, III, Jason K. Holt, and David G. Goodwin. Material Research Society Symposium Proceedings, 609, A6.1.1 -A6.1.6. The Mechanism for Unimolecular Decomposition of RDX (1,3,5-trinitro-1,3,5-triazine); An Ab Initio Study. Debashis Chakraborty, Richard P. Muller, Siddharth Dasgupta, and William A. Goddard, III. Journal of Physical Chemistry A, 104(11), 2261-2272 (2000).Hybrid ab Initio Quantum Mechanics/Molecular Mechanics Calculations of Free Energy Surfaces for Enzymatic Reactions: The Nucleophilic Attack in Subtilisin. J. Bentzien, R. P. Muller, J. Florián, and A. Warshel J. Phys. Chem. B 102, 2293-2301 (1998). Semiempirical and ab initio modeling of chemical processes: From aqueous solution to enzymes. Richard P. Muller, Jan Florian, and Arieh Warshel. NATO Symposium Series: Biomolecular Structure and Dynamics: Recent Experimental and Theoretical Advances. G. Vergoten, ed. Calculations of chemical processes in solution by density functional and other quantum mechanical techniques. Richard P. Muller, Tomasz A. Wesolowski, and Arieh Warshel. Density functional methods: Applications in chemistry and materials science, M. Springborg, ed. John Wiley & Sons, New York, 1997.

Peter MeulbroekMaterials and Process Simulation Center, Beckman Institute (139-74)California Institute of Technology1201 East California Blvd.Pasadena, California 91125 USAhttp://www.wag.caltech.eduPhone:(626) 395-2720, FAX:(626) 585-0917email: meulbroek@wag.caltech.edu

EducationPh. D., Geology 1996 Cornell University, Ithaca NYB.Sc., Mathematics 1988 University of Chicago, IL

Professional ExperienceJan 2002-pres. Director Software Integration/Design MSC, Caltech1999-2002 Associate Scientist MSC, Caltech1998-1999 Postdoctoral Scholar Woods Hole Oceanographic Inst, MA1997 Postdoctoral Scholar University of Newcastle-upon-Tyne, UK1992-1997 Graduate Student Cornell University1988-1992 Researcher O’Connell & Piper Assoc., Chicago ILResearch InterestsSoftware design and development. Integration of existing codes into large-scale aggregations, database technology. Continuum fluid-phase modeling, Basin modelingProfessional ActivitiesMember of the American Chemical Society, the American Association of Petroleum Geologists, the European Association of Organic Geochemists.Relevant Worki) The Hydrocarbon Toolkit: Simulation Programs to Calculate Phase Behavior”, http://www.wag.caltech.edu/basin-public/ii) “Quaternions Extensions”, http://www.wag.caltech.edu/home/meulbroek/QuaternionExtentions/index.htmliii) “A Smiles Parser”, http://www.wag.caltech.edu/home/meulbroek/smiles/smiles_parser.htm

Relevant PublicationsMeulbroek, P., Macleod, G. (2002), Editors, Equations of State (special edition to Organic Geochemistry), 33.Meulbroek, P. (2002), “Equations of State in Exploration”, Organic Geochemistry 33.Losh, Cathles, Meulbroek (2002), “Gas Washing along a regional transect, offshore Louisiana”, Organic Geochemistry 33. Losh, Cathles, Meulbroek (2001), AAPG Bulletin.Meulbroek, Peter; Cathles, Lawrence, III; Whelan, Jean (1998), “Phase fractionation at South Eugene Island Block 330”, Organic Geochemistry 29, pp.223-239.Meulbroek, Phase Fractionation in Sedimentary Basins (1997), Ph.D. Thesis, Cornell University.

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