This article is one of four papers on modeling and simulation (part one) to be presented exclusively on the web as part of the August 1999 JOM-e—the electronic supplement to JOM. The second part of this topic supplements the September issue. The coverage was developed by Steven LeClair of the Materials Directorate, Air Force Research Laboratory, Wright-Patterson Air Force Base.
	The following article appears as part of JOM-e, 51 (8) (1999), http://www.tms.org/pubs/journals/JOM/9908/Atkinson/Atkinson-9908.html JOM is a publication of The Minerals, Metals & Materials Society

Modeling and Simulation, Part I: Overview

Predicting Microstructure from Atomistic Rule Set Cellular Automata

K.J.W. Atkinson, Robin W. Grimes, Matthew O. Zacate, Peter D. Lee, Al G. Jackson, and Steve R. LeClair


TABLE OF CONTENTS
INTRODUCTION CRYSTALLOGRAPHY METHODOLOGY Interaction Energy of an Ar Atom with a Specific Surface Site Adsorption Desorption The Flux THE CA RULE SET Flux Stage Adsorption Stage Desorption Stage RESULTS DISCUSSION CONCLUSIONS ACKNOWLEDGEMENTS Reference

This paper describes a study to calculate the energetics associated with the ways in which individual argon gas atoms interact with a calcium (111) surface. Both perfect and defective surfaces have been considered. The energetics are translated into rule sets, involving both temperature and gas atom flux as variables, which form the basis of the cellular automata. In addition, both smooth and rough surfaces can be defined. The result is a simulation tool that can explicitly model the evolution of 10⁴ surface sites over approximately 10^-6 seconds with very modest computing facilities. In these simulations, the formation and growth of domains is observed. However, the rate of growth is not a well-behaved function of temperature or flux, but instead exhibits a critical region in which the growth rate suddenly falls to zero. Surface defects, as defined by the degree of surface roughness, can also have a dramatic effect on growth rates.

INTRODUCTION

When a specific microstructure is required, it is very difficult and expensive to experimentally determine the optimum conditions if the preparation variables increase beyond a few. Consequently, there is considerable interest in predicting the values of variables via computer simulation. Since microstructure ultimately depends on processes occurring at the atomistic level, it is desirable that such a model be atomistically based if it is to be fully transferable. This should also allow the user to include, explicitly, the role of all types of chemical and crystallographic defects.

A number of atomistic-scale simulation techniques exist that could potentially be used as the basis for predicting microstructural evolution. These include molecular dynamics and energy minimization, both of which have been highly successful in predicting energetics and structures in metals, ceramics, and semiconductors. Unfortunately, present computational facilities are not nearly sufficient to allow these methods to become routinely useful predictive tools on scales required for microstructural length and growth times. Monte Carlo (MC) techniques present a tempting alternative since each calculation is concerned with a new configuration. Between each step, an atom is moved to a new position or, possibly, a pair of atoms are swapped. The total energy of this new configuration is then calculated and compared with the energy of the old configuration. On this basis, a decision is made as to whether the system should be updated to the new configuration or remain unaltered. However, even this method becomes computationally challenging when the number of atoms reaches the point necessary to model microstructural evolution. As such, we have implemented an MC-related methodology based on cellular automata (CA).

Figure 1. The crystallographic structure of calcium.

Figure 2. The evolution of a gas-atom layer in calcium.
Formally, CA are discrete dynamical systems, the behavior of which are completely specified in terms of local interactions. Typically, the evolution of CA is controlled by updating the state of each discrete cell on the basis of a rule involving only the states of adjacent cells. The entire cell assembly is updated simultaneously in an update step that may be taken to represent a single step in time. Thus, there are three differences between CA and MC methodologies:

All the cells are updated in a single step in CA.
The interactions between particles are local, not system wide.
Each cell does not necessarily have to consist of a single atom at a single site, but may include multiple sites and multiple states.

Having said this, there are variants of MC that assume local interactions and variants of CA that count interactions beyond the nearest neighbor. Also, in this study only two crystallographic sites are included in each CA cell. Thus, the difference between CA and MC can become somewhat blurred.

As a first step toward implementing a CA model to describe microstructural evolution, this article describes the results of work to develop a simple atomistic simulation of a two-dimensional system comprising a layer of argon-gas atoms on a calcium (111) surface. The simulation results in the formation and growth of domains, with the domain size increasing as a function of simulation temperature. Previous results¹ revealed that domain size increases as a function of flux of atoms incident on the surface. However, for the results presented here, the flux rate was held constant at eight percent.

CRYSTALLOGRAPHY

Alpha calcium metal has a face-centered-cubic (f.c.c.) structure so that the (111) surface exhibits a close packing of calcium atoms. These may be regarded as A sites. When a gas atom attaches itself to the surface, it will reside in a site formed by three metal atoms since this maximizes its interaction with the metal surface. Two sets of interstitial sites exist, which can be regarded as B sites and C sites (Figure 1).

If we examine the (111) surface more closely, it is clear that the distance between an interstitial B site and a nearest neighbor C site is rather short. Indeed, the gas atom in Figure 1 overlaps considerably onto the C site. As such, if a gas atom resides in a B site, it is not possible for a second gas atom to occupy a nearest-neighbor C site due to steric hindrance. It is important to realize that this is true only because of the specific matching of the size of the calcium (111) surface to the size of the argon atoms.

When the first few gas atoms impinge on the surface in the evolution of a gas-atom layer, they may reside in either B or C sites. As more atoms collect, patches or islands of B- and C-type atoms form. The distance between two neighbor B-type gas atoms (i.e., in next-nearest-neighbor sites) is the same as between two C-type gas atoms (Figure 2). However, the distance between allowed, non-hindered B- and C-type atoms is greater (i.e., between third or fourth neighbors). This results in a surface area with a lower density of gas atoms, termed a growth fault. In three dimensions, this would give rise to a stacking fault. Faults are important to the stability of a gas atom on the surface since the interaction energy of gas atoms is lower when they interact over longer distances. Therefore, atoms adjacent to growth faults have lower energy. This is the driving force that results in the formation and growth of islands of gas atoms that occupy only one type of site.

THE CA RULE SET

METHODOLOGY
Interaction Energy of an Ar Atom with a Specific Surface Site In this study, two types of interactions are considered: The interaction between inert gas atoms. The interaction between inert gas atoms and the metal substrate. Both forms of interaction have two physical origins--electron-cloud overlap repulsion and van der Waals attraction. As described by Lennard-Jones potentials, if R is the distance between the atoms i and j, the interaction energy, E_ij, is given by E_ij = a_ijR^-12 - b_ijR^-6 where a and b are constants specific to the atoms i and j. For each potential type, the constant b corresponds to the van der Waals interaction, and the a constant is the electron-cloud repulsion. Details of how the parameters were derived are given in Reference 1. Using these potentials, it is possible to calculate the interaction energies that define the stability of an argon atom at a specific site on the surface. With calcium atoms located in the A sites of the f.c.c. lattice, argon atoms are prescribed to sit in the ideal B- and C-crystallographic f.c.c. sites. Hence, the interaction energy, E_Ar,Ca, between an argon atom and the calcium substrate is given by E_Ar,Ca = 3(a_Ar,CaR^-12 - b_Ar,CaR^-6) where R = r_Ar + r_Ca, the sum of the argon and calcium atom radii, respectively. The interaction energy between two argon atoms on the surface is given by E_Ar,Ar = a_Ar,ArR^-12 - b_Ar,ArR^-6 where R = 2r_Ca. It should be remembered that the separation of the gas atoms is defined by the substrate. To simplify the analysis further, for each surface, argon-atom interactions other than those between that atom and the three neighboring calcium atoms and the next near-neighbor argon atoms are neglected. Given the above, it is possible to calculate the interaction energy, E_a, of an argon atom at all possible configurations that may occur on the surface: E_a = 3E_Ar,Ca + nE_Ar,Ar where n is the number of next-nearest-neighbors sites, which are already occupied by argon atoms. In the case of a rough surface, this expression is somewhat modified because some of the next-nearest-neighbor interactions are between argon and calcium. These energies are then used to determine the probabilities of events occuring on a stochastic basis, which forms the basis for the time evolution of the surface. This link between the local atomic structure and the probability for adsorption or desorption is the key feature of the model. Adsorption The incoming atoms are assumed to have a Maxwellian distribution of kinetic energies, e: n(e) = (1/kT)exp(-e/kT) where T is the temperature in Kelvin, and k is Boltzmann's constant. The fraction of incoming atoms that adhere to the surface depends on the substrate's ability to dissipate enough kinetic energy for the colliding atom to become trapped in the potential well of the site. Thus, we make the following assumptions: Any atom with a kinetic energy less than E_a will become trapped. There is no energy barrier at the surface. Thus, the probability that an incident gas atom sticks, P_att, in a potential well of depth E_a is Desorption Assuming a single-step activated process, the probability, P_des, that desorption occurs between t and t + dt is given by P_des(R_d,t) = R_dexp(-R_dt) where the desorption rate, R_d, is given by R_d = g_df_oexp(E_a/kT) where g_d is a geometric factor taken to be unity, f_o is the attempt frequency, and E_a is the usual environmentally dependent interaction energy. Thus, the total number of atoms that desorb over a single time step, s_t, is To a good approximation, the attempt frequency is equivalent to the vibrational frequency of the gas atom on the surface. Thus, P_des = 1 - exp{(exp - E_a/kT)} To relate the results to seconds, the characteristic attempt frequency of argon can be taken as 4.45 x 10¹⁰ s^-1, a value calculated by fitting a harmonic function to the energy profile of an argon atom at a site with six occupied next-nearest neighbors. However, since the interaction energy varies according to the number of occupied next-nearest neighbors, such an analysis is imprecise. Therefore, the results are reported in time steps rather than in seconds. The Flux During each time step, the number of gas atoms that strike the surface are defined by a flux such that In this study, we compare the evolution of surfaces exposed to the same flux at different temperatures. The flux used here was eight percent (i.e.,the number of gas atoms that were allowed to interact with the surface corresponded to eight percent of the total number of surface sites [B and C]).

METHODOLOGY

Interaction Energy of an Ar Atom with a Specific Surface Site

In this study, two types of interactions are considered:

The interaction between inert gas atoms.
The interaction between inert gas atoms and the metal substrate.

Both forms of interaction have two physical origins--electron-cloud overlap repulsion and van der Waals attraction. As described by Lennard-Jones potentials, if R is the distance between the atoms i and j, the interaction energy, E_ij, is given by

E_ij = a_ijR^-12 - b_ijR^-6

where a and b are constants specific to the atoms i and j. For each potential type, the constant b corresponds to the van der Waals interaction, and the a constant is the electron-cloud repulsion. Details of how the parameters were derived are given in Reference 1.

Using these potentials, it is possible to calculate the interaction energies that define the stability of an argon atom at a specific site on the surface. With calcium atoms located in the A sites of the f.c.c. lattice, argon atoms are prescribed to sit in the ideal B- and C-crystallographic f.c.c. sites. Hence, the interaction energy, E_Ar,Ca, between an argon atom and the calcium substrate is given by

E_Ar,Ca = 3(a_Ar,CaR^-12 - b_Ar,CaR^-6)

where R = r_Ar + r_Ca, the sum of the argon and calcium atom radii, respectively. The interaction energy between two argon atoms on the surface is given by

E_Ar,Ar = a_Ar,ArR^-12 - b_Ar,ArR^-6

where R = 2r_Ca. It should be remembered that the separation of the gas atoms is defined by the substrate. To simplify the analysis further, for each surface, argon-atom interactions other than those between that atom and the three neighboring calcium atoms and the next near-neighbor argon atoms are neglected. Given the above, it is possible to calculate the interaction energy, E_a, of an argon atom at all possible configurations that may occur on the surface:

E_a = 3E_Ar,Ca + nE_Ar,Ar

where n is the number of next-nearest-neighbors sites, which are already occupied by argon atoms. In the case of a rough surface, this expression is somewhat modified because some of the next-nearest-neighbor interactions are between argon and calcium. These energies are then used to determine the probabilities of events occuring on a stochastic basis, which forms the basis for the time evolution of the surface. This link between the local atomic structure and the probability for adsorption or desorption is the key feature of the model.

Adsorption

The incoming atoms are assumed to have a Maxwellian distribution of kinetic energies, e:

n(e) = (1/kT)exp(-e/kT)

where T is the temperature in Kelvin, and k is Boltzmann's constant. The fraction of incoming atoms that adhere to the surface depends on the substrate's ability to dissipate enough kinetic energy for the colliding atom to become trapped in the potential well of the site. Thus, we make the following assumptions:

Any atom with a kinetic energy less than E_a will become trapped.
There is no energy barrier at the surface.

Thus, the probability that an incident gas atom sticks, P_att, in a potential well of depth E_a is

Desorption

Assuming a single-step activated process, the probability, P_des, that desorption occurs between t and t + dt is given by

P_des(R_d,t) = R_dexp(-R_dt)

where the desorption rate, R_d, is given by

R_d = g_df_oexp(E_a/kT)

where g_d is a geometric factor taken to be unity, f_o is the attempt frequency, and E_a is the usual environmentally dependent interaction energy. Thus, the total number of atoms that desorb over a single time step, s_t, is

To a good approximation, the attempt frequency is equivalent to the vibrational frequency of the gas atom on the surface. Thus,

P_des = 1 - exp{(exp - E_a/kT)}

To relate the results to seconds, the characteristic attempt frequency of argon can be taken as 4.45 x 10¹⁰ s^-1, a value calculated by fitting a harmonic function to the energy profile of an argon atom at a site with six occupied next-nearest neighbors. However, since the interaction energy varies according to the number of occupied next-nearest neighbors, such an analysis is imprecise. Therefore, the results are reported in time steps rather than in seconds.

The Flux

During each time step, the number of gas atoms that strike the surface are defined by a flux such that

In this study, we compare the evolution of surfaces exposed to the same flux at different temperatures. The flux used here was eight percent (i.e.,the number of gas atoms that were allowed to interact with the surface corresponded to eight percent of the total number of surface sites [B and C]).

The CA simulation is implemented in three stages per time step. The first stage randomly assigns energies to atoms present on the surface and removes them according to their desorption probability. The second stage randomly assigns lattice locations where incoming gas atoms interact with the surface and determines whether or not each incident gas atom adsorbs. The third stage updates the periodic boundary conditions of the edges of the surface.

Flux Stage

During the time step of duration (t), N atoms strike the surface where

N = f_N x (number of surface sites) x t

N lattice sites are randomly selected, representing locations where the atoms strike. A specific location is identified by the particular CA cell in which it is located and by the label B or C, indicating the lattice site within the cell. Rather than report the N used for a simulation, f_N is given because it scales with the number of surface sites (defined as the number of CA cells).

Adsorption Stage

Two possibilities are considered for each lattice site assigned in the flux stage, depending on the occupancy of the lattice site. If the position is already occupied, the incoming atom striking that position does not adhere to the surface. If the position is empty, the probability of sticking depends on the neighborhood of the site. For example, if there are one or more gas atoms already located in near-neighbor positions to the site under consideration, the incoming atom will not stick. Otherwise, the atom sticks with a probability, P_ads, which is a function of the number of next-nearest neighbors, n, prior to the adsorption stage. Note that the considered neighborhood is that which existed before any atoms stick during the current adsorption stage.

Desorption Stage

For the desorption stage, each atom on the surface prior to the adsorption stage was considered for desorption. The probability that each atom desorbs is given by P_des(n), where n is the number of next-nearest neighbors prior to the adsorption stage.

RESULTS

In this paper's four supplements, experimental results are presented graphically for four test groups. The first group of results concerns evolution on a perfectly flat (111) surface. Subsequent results consider various types of surface roughness; however, in each case, the degree of roughness, characterized by the number of additional calcium atoms on the (111) surface, is constant at 10% coverage. Thus, when it is assumed that the additional calcium atoms are randomly distributed, one in every 20 surface sites contains a calcium atom (the maximum number of surface sites that could be occupied is 50%, which would correspond to 100% coverage). Since the distribution is random, no possible aggregation of surface calcium atoms to form islands has been allowed. The opposite scenario was also considered, in which all additional calcium atoms aggregated to form a hexagonal (i.e., roughly spherical) island occupying only B sites. Nevertheless, such an island still has a very small radius, on the scale of a typical microstructure. Therefore, in the final group of results, the additional atoms form a canyon-like structure that has an effectively infinite radius of curvature. Hence, the hexagon feature of the third group should be considered as an intermediate radius structure.

The richly illustrated, video-enhanced supplements present results associated with

A Perfect Surface
Distributed Surface Roughness
A Hexagonal Surface Feature

A Canyon Surface Feature

DISCUSSION

Starting with an empty surface on a perfectly flat surface, the coverage initially increases as a function of time as empty surface sites become occupied. Once the majority of sites are occupied (at an 8% flux, this occurs in a matter of a few tens of time steps), the surface begins to evolve. This is characterized by domain growth, with the smaller domains being consumed. The rate of domain growth can be quantified by considering the coverage after 10,000 time steps (Figure 3). This shows that below 115 K the surface is 90% covered by gas atoms, but above this temperature there is an abrupt reduction in the coverage. Indeed, the reduction is so abrupt that it might be described as a critical phenomenon.

The generally constant coverage below 115 K is the result of an interplay between two processes. At the lowest temperatures, there are still a number of growth faults after 10,000 time steps, which, given their lower site density, reduce the overall coverage. In addition, there are a small number of missing gas atoms in the interior of the gas-atom domains (i.e., point defects). This is apparent in Figure Ac of Supplement I: Results Associated with a Perfect Surface. As the temperature rises, there are fewer growth faults but more point defects (see, for example, Figure Bc and Figure Cc in Supplement I). Nevertheless, the surface is effectively stable since each gas atom is surrounded by a majority complement of its next-nearest neighbors.

Once the temperature reaches 125-130 K, the coverage reduces rapidly. The rate at which atoms leave the surface becomes sufficiently high so that each gas atom has a significantly less-than-full complement of next-nearest neighbors. Consequently, the energy required to remove each gas atom, E_a, is smaller, and gas atoms more readily leave the surface. This is the origin of the dramatic reduction in surface coverage rather than exponential decay, which would be expected purely on the basis of increasing temperature.

Figure 3 also presents data for the hexagonal feature. In this case, the variation in coverage is practically identical to that found for the perfect surface. However, in Supplement III: Results for a Hexagonal Surface Feature, it was observed that, to a certain extent, the growth proceeded from the hexagon (pause Movie K and view the frames at the very beginning of the simulation). Hence, it is interesting that this did not have an effect on the overall coverage after 10,000 time steps. The hexagon may affect the initial nucleation and growth, but this is a local process which, after a certain point, becomes unimportant to the slower growth process. Similarly, the small amount of enhanced B site coverage around the hexagon at 140 K did not lead to enhanced coverage, and the surface remains unstable (see the results section in Supplement III).

Figure 3. The coverage at 10,000 time steps for perfect and imperfect 100 x 100 atom surfaces.
In addition to a hexagonal feature, other shapes were investigated. One simulation considered a diamond-shaped feature of B sites (Figure 4). In this case, the random seed of gas atoms formed an initial morphology that was unstable to B-site gas atom growth. Consequently, rather than the eventual formation of a completely B-site-dominated microstructure, the C-site gas atoms were able to contain the B-site gas atoms around the diamond feature. The size of the diamond feature was not sufficient to overcome the curvature imposed by the C-site gas atoms. Presumably, the diamond feature was below the necessary critical radius. The diamond shape was also a possible contributing factor, since this behavior was never observed with the hexagon feature. This raises interesting possibilities of nucleus-shaped effects.

The observations on the canyon feature (see Supplement IV: Results Associated with a Canyon Surface Feature) show that there are a number of characteristics in common with the hexagon (see Supplement III). For example, the existence of the feature causes nucleation and growth of same-site gas atom structures over the first 1,000 time steps adjacent to the feature; this stabilizes the growth of the single-site domains. However, in Figure 3, the canyon simulation shows essentially the same coverage at 10,000 time steps as does the hexagon and perfect surfaces. Again, the existence of a feature has a strong influence on what initially nucleates and grows, but not on the overall coverage after the initial period.

Because the canyon feature is composed of two different site features opposite each other, the result is growth of two different site domains. The 90 K, 100 K, and 120 K simulations after 10,000 time steps yield microstructures that are intergrowths. The remaining 90% of the simulation time (i.e., to 100,000 time steps) is required for a clear bimodal microstructure to develop. By comparing the 90 K, 100 K, and 120 K simulations, it is clear that at the higher temperature, the structure is more developed toward some form of equiilibrium, although there is still a greater proportion of B site (red) gas atoms than C site (blue). Because of the equivalence in the energetics associated with B- and C-site gas atom absorption and the nature of the periodic boundary conditions, such a straight equal-proportioned microstructure might be expected; however, this type of simulation would also allow investigations on the extent of the boundary fluctuation as a function of temperature and gas-atom flux.

Figure 4. (a) A video clip (~2.5 Mb) of the evolution of a diamond feature with 8% argon flux at 90 K (each movie frame represents 500 time steps; the movie shows evolution over 300,000 time steps), and (b) an image of the surface evolution at 90 K after 300,000 time steps.
Finally, the surface on which the 10% roughness is randomly distributed must be considered (see Supplement II: Results Associated with Distributed Surface Roughness). For this surface, the analysis of coverage temperature shows quite a different behavior than that for the perfect, hexagonal, and canyon surfaces. In particular, the total coverage at low temperatures is significantly less, and as the temperature is raised, there is immediately a gradual reduction in coverage. Hence, the dramatic fall in coverage exhibited by the other types of surface is not seen when the roughness is randomly distributed. By 140 K, the coverage for this surface is effectively the same as for the other surfaces, because the gas-atom coverage of all surfaces is highly unstable.

Consider the development of the distributed rough surface at 90 K (see Figures Ea-Ed in Supplement II). After 100 time steps, the distributed rough surface morphology is very similar to that of the perfect surface. Even after 1,000 time steps, these rough and perfect surfaces are fairly similar, although the domains of the perfect surface are now a little larger. After 10,000 time steps, the surfaces are quite different, with the domain size growth of the distributed roughness surface being effectively arrested. It is tempting to state that the domain size has been pinned, but by 100,000 time steps there is some indication that more growth has occurred, although the effect is small considering the number of time steps has increased by an order of magnitude. Therefore, it would be interesting to increase the number of time steps by at least another order of magnitude to see if the effect is truly one of pinning or massively retarding.

An interesting observation is that for the distributed roughness surface, the domain size at 1,000 time steps and beyond is greater than the distance between the additional surface calcium atoms (see, for example, Figure Fc in Supplement II). Consequently, each domain contains a number of additional calcium atoms. Furthermore, we observe that the surface calcium atoms do occupy both B and C sites within each domain; clearly, the additional calcium atoms are not simply acting as classical nucleation points. The reason the microstructure becomes contained in the way that it does is a complex interplay between the specific surface roughness distribution and the initial random deposition process over the first few hundred time steps. It would be interesting to further analyze the relationship between roughness distribution and the resulting microstructure, including varying the amount of surface roughness and the gas-atom flux rate. Finally, other distributions of surface roughness, intermediate between the totally random and condensed features presented in this work, would be possible.

CONCLUSIONS

In this study, we were able to bridge three orders of scale in both the spacial and time dimensions from atomistic to microstructural simulations, resulting in new insights into the growth of surfaces. Despite the apparent simplicity of this model system, qualitative analyses of data, such as in Figure 3, can conceal the complex interplay of physical processes that results in microstructural evolution. As models become more sophisticated and include multiple physical processes, it will become harder to identify the underlying causes responsible for such dramatic effects on surface evolution. Consequently, the analysis tools that interpret large volumes of simulation data will be as important as the simulation methods themselves. However, as simulation methods become more proficient, it will still be important to be able to extract physical understanding.

There is a strong incentive to develop simulation methods that accurately predict physical phenomena independently of experiment. Given the overwhelming complexity of real systems, this approach may not be the most practical. A more practical alternative is to adjust a representative set of atomistically derived parameters for the CA rule set in order that a limited number of experimental microstructures are accurately reproduced. Such a procedure would provide a simple check for the validity of the model and effectively provide a more accurate simulation. Ideally, the parameters would only need fine adjustments to reproduce the experiment, thereby preserving the atomistic interpretation of phenomena associated with the microstructural evolution. Furthermore, this approach would avoid over parameterization and an unnecessarily complex model. Once the parameters are calibrated to a set of well-characterized experimental data (i.e., where process variables, such as temperature and flux, are well known), the process variables could then be varied in the CA simulations to determine the optimal experimental conditions for producing a prescribed microstructure.

ACKNOWLEDGEMENTS

This work was made possible by EOARD contracts F6170896WW029, F6177598WE045, and F170899WW095.

Reference
1. M.O. Zacate et al., Modelling and Simulation in Materials Science and Engineering, 7 (1999), pp. 155-167.

ABOUT THE AUTHORS

K.J.W. Atkinson, Robin W. Grimes, and Matthew O. Zacate are with the Atomistic Simulation Group, Department of Materials, Imperial College. Peter D. Lee is with the Materials Processing Group, Department of Materials, Imperial College. Al G. Jackson is with the Materials Directorate and Steve R. LeClair is with the Materials Process Design Branch, Wright-Patterson Air Force Base.

For more information, contact R.W. Grimes, Department of Materials, Imperial College, Prince Consort Rd, London SW7 2BP; telephone 44-0171-594-6730; e-mail r.grimes@ic.ac.uk.

Copyright held by The Minerals, Metals & Materials Society, 1999

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