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, 2002
Budget First Year: $999,540
Addresses with Resumes
Table of Contents
Table of Contents
_______________________________________________________________ 2
1. Executive summary _________________________________________________________ 3
2 Overall objectives and expected outcome _________________________________________ 4
3 Full-physics, full-chemistry, multi-scale modeling capabilities and materials design______ 5
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 ____________________________________________________ 7
3.5 Finite Element Modeling _________________________________________________ 9
3.6 Multiscale modeling: bridging the scales from QM to FEM ___________________ 10
3.7 Materials Design _______________________________________________________ 11
3.8 References ____________________________________________________________ 12
4 Large strain and energy density ferroelectrics____________________________________ 12
4.1 Introduction ___________________________________________________________ 12
4.2 Objectives and expected outcome _________________________________________ 14
4.3 Task list and timeline ___________________________________________________ 14
4.4 References ____________________________________________________________ 16
5 Simulation of the explosive/propulsive properties of CL-20 _________________________ 16
5.1 Introduction ___________________________________________________________ 16
5.3 Objectives and expected outcome _________________________________________ 18
4.3 Task list and timeline ___________________________________________________ 19
5.4 References ____________________________________________________________ 20
6. The Computational Materials Design Facility (CMDF) ___________________________ 20
6.1 Introduction ___________________________________________________________ 20
6.3 Software Design ________________________________________________________ 21
6.4 Component Descriptions ________________________________________________ 22
6.6 Task list and time line ___________________________________________________ 26
7 Personnel _________________________________________________________________ 27
7.1 Qualifications on the PI and co-PIs ________________________________________ 27
7.2 Resumes ______________________________________________________________ 29
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, fullphysics, 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 modeling
1) 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 multiscale modeling of materials is critical to provide the composition-processing-structureproperty 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 outcome
We 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)O3xPbTiO3].
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 calculations
We 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 fields
A 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 simulations
We 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 Modeling
Reaching 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 Fields
We 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 spatiallyresolved velocity, temperature, and species concentration profiles normal to the surface.
3.5 Finite Element Modeling
FE 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 FEM
Critical 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.
First Principles Force Field for Ta
MD simulations
Energy (kcal/mol)
bcc Ta EOS
Edge Core energy=
0.827 eV/Å
Screw Core energy=
0.468 eV/ Å
QM (circles)
qEAM FF (line)
Kink
Volume (A3)
Kink formation
Energies: 0.1 - 1.1 eV
Unixial tension in Ta
Experiments
stress
stress
Mitchell and Spitzig.
Acta Metallurgica1965.
Micromechanical modeling
Dislocation mobility
Forrest hardening
strain
First Principles Modeling
stress
Dislocations intersection Dislocations multiplication
strain
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).
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 Design
The 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 References
A.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 Introduction
Actuators 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 bphase 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.
H
F
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 [(1x)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.
Muscle
PZT
PZN-PT
PVDF
SMA (NiTi)
P(VDF-TrFE)
Freq (Hz) MaxStrain (%)
2
40
1,000,000
0.2
1,000,000
1.7
10,000
0.1
100
5
100,000
7
Ene. Dens. (J/g)
0.07
0.013
0.13
0.0013
15
0.51
Ene. Dens. (J/cm3)
0.07
0.1
1
0.0024
100
1
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.
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 defects
o Composition, degree of cross-linking, presence of the amorphous phase, para- ferroelectric 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 nonisotropic 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 outcome
The 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 leadbased 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 timeline
Year 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 (PZNPT);
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 References
Q. 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 Introduction
Energetic 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.
O2N
O2N
N
N
N
O2N
NO2
NO2
N
N
N
NO2
Figure 6: the nitramine CL20
Enormous 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
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.
100
NO2 dissociation
80
2MN
+LM2
60
3MN
INT176+
NO2
INT149+
HCN+NO2
MN+MNH+
HCN+NO2
Energy (kcal/mol)
40
RDR+
NO2
concerted
RDRo+
NO2
20
2MN+N2O+
0
MN+LM2+
N2O+H2CO
H2CO
RDX
-20
LM2+2N2O+
RDX'+HONO
MN+2N2O+
RDX''+2HONO
2H2CO
2H2CO
-40
-60
TAZ+3HONO
HONO elimination
-80
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.
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 outcome
The 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 timeline
Year 1
By the end of year one a successful project will provide full physics, full chemistry, firstprinciples-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 spatiallyresolved 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 2
The 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 References
1. 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 firstprinciples-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 Design
While 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 (highperformance computing based on methods developed above).
The design for the CMDF is shown in Figure 10, below:
Additional
aggregation functions
Database
Applications server
'farm'
Nodes
User
Interface:
Icarus
Dispatcher
Figure 10: CMDF Design
Key points
The 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) facilities
Motivations
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 Descriptions
From 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 Database
The 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 Perl1
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.
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:
Aggregator Job Parameters
Node
Database
ete
r
Pa
ra
m
qu
er
ies
Jo
b
SQ
s
Results
Matching
Simulation
Results
L
User
Interface
Job Parameters
SQL queries
Dispatcher
Figure 11: Communications with the Database
6.4.2 User Interface
The 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, faulttolerant HPC grids. Communication strategies are outlined in Figure 12, below:
Database
s
L
SQ
Job Initiation
User
Interface
e
qu
r ie
b
Jo
Pa
ra
m
er
et
s
Computation Initiation
Node
Dispatcher
Job and
Network
status
Status
Figure 12: Communication with the Dispatcher
2
eXtensible Markup Language Remote Procedure Call, a platform independent, net-aware protocol that is supported
by many programming languages.
6.4.4 Node
The 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 Aggregator
The 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 line
Year 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 gasphase 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 electromechanical coupling in ferroelectric materials allowing the development of nano- microstructure-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 code
4. 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 2
The 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-PIs
William 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 19811999)
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 nonlinear 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 CL20 simulations in this project, and help integrate the QM simulation capability into the MTF to
automatically tune the ReaxFF.
Peter Meulbroek,
7.2 Resumes
WILLIAM A. GODDARD, III
Beckman Institute (139-74)
California Institute of Technology
1201 East California Blvd.
Pasadena, California 91125 USA
http://www.wag.caltech.edu
Phone:(626) 395-2731, 395-2730, FAX:(626) 585-0918
email: wag@wag.caltech.edu, copy: shirley@wag.caltech.edu
Current Positions at the California Institute of Technology:
Charles and Mary Ferkel Professor of Chemistry, Materials Science, and Applied Physics
Director of Materials and Process Simulation Center (MSC): 1990-present
Previous Professional Positions (all at Caltech):
1965-1978
Assistant, Associate, and Full Professor of Theoretical Chemistry
1978-1984
Professor of Chemistry and Applied Physics
1984-1990
Director of NSF Materials Research Group
1992-1997
Director of NSF Grand Challenge Applications Group
1984-2001
Charles and Mary Ferkel Professor of Chemistry and Applied Physics
1990-present Director of Materials and Process Simulation Center (MSC)
2001- present Charles and Mary Ferkel Prof. Chemistry, Materials Science, and Applied Physics
Education:
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 1988-1994
Cofounder 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 CUITIÑO
Dept. of Mechanical and Aerospace Engineering
Rutgers University
Piscataway, New Jersey, 08854
Phone: (732) 445-4210 Fax: (732) 445-3124
E-mail: cuitino@jove.rutgers.edu; http:// cronos.rutgers.edu/~cuitino/
Education
Ph.D., Solid Mechanics
Minor in Materials Science
M.S., Applied Mathematics
1993
Brown University
1992
Brown University
B.S. Civil Engineering
1986 University of Buenos Aires
Professional Experience
1999-Present Associate Professor of Mechanical and Aerospace Engineering
Rutgers Univeristy
2000-2001
Visiting Professor, California Institute of Technology
1993-1999
Assistant Professor, Rutgers University
1989-1993
Research Assistant, Brown University
Research Interests
Computational Mechanics, micromechanical modeling of advanced materials, multiscale
modeling.
Professional Activities
Associate Editor, Latin American Applied Research.
Relevant Publications

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




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
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 STRACHAN
Materials and Process Simulation Center, Beckman Institute (139-74)
California Institute of Technology
1201 East California Blvd.
Pasadena, California 91125 USA
http://www.wag.caltech.edu
Phone:(626) 395-9137, FAX:(626) 585-0918
email: strachan@wag.caltech.edu
Education
Ph. D., Physics
1998 University of Buenos Aires, Argentina
B.Sc., Physics
1994 University of Buenos Aires, Argentina
Professional Experience
2001-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 Interests
Development 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 Activities
Member of American Physical Society.
Relevant Publications
Goddard, 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. Muller
Director, Quantum Technologies
626-395-2732
Materials and Process Simulation Center
626-585-0918 (FAX)
Beckman Institute (139-74)
rpm@wag.caltech.edu
California Institute of Technology
http://www.wag.caltech.edu/home/rpm
Pasadena, CA 91125
EDUCATION
1994 California Institute of Technology, Ph.D., Chemistry
Development and Implementation of Quantum Chemical Techniques for Application to
Large Molecules
William A. Goddard, III, Advisor
1988 Rice University, B.A., Cum Laude, Chemistry
FELLOWSHIPS 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, 1990
PROFESSIONAL EXPERIENCE
1997 - Present,
Director, Quantum Simulations, Materials and Process Simulation Center
1994 - 1997, Postdoctoral Research at The University of Southern California,
RECENT RELEVANT PUBLICATIONS
Computational 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 Meulbroek
Materials and Process Simulation Center, Beckman Institute (139-74)
California Institute of Technology
1201 East California Blvd.
Pasadena, California 91125 USA
http://www.wag.caltech.edu
Phone:(626) 395-2720, FAX:(626) 585-0917
email: meulbroek@wag.caltech.edu
Education
Ph. D., Geology
1996 Cornell University, Ithaca NY
B.Sc., Mathematics
1988 University of Chicago, IL
Professional Experience
Jan 2002-pres.
Director Software Integration/Design MSC, Caltech
MSC, Caltech
1999-2002
Associate Scientist
1998-1999
Postdoctoral Scholar
Woods Hole Oceanographic Inst, MA
1997
Postdoctoral Scholar
University of Newcastle-upon-Tyne, UK
1992-1997
Graduate Student
Cornell University
1988-1992
Researcher
O’Connell & Piper Assoc., Chicago IL
Research Interests
Software design and development. Integration of existing codes into large-scale aggregations,
database technology. Continuum fluid-phase modeling, Basin modeling
Professional Activities
Member of the American Chemical Society, the American Association of Petroleum Geologists,
the European Association of Organic Geochemists.
Relevant Work
i)
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.html
iii)
“A Smiles Parser”, http://www.wag.caltech.edu/home/meulbroek/smiles/smiles_parser.htm
Relevant Publications
Meulbroek, 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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