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Feature: Overview Vol. 60, No.5 p. 32-37

Open Source Software for Materials
and Process Modeling

Adam C. Powell IV and Raymundo Arroyave

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Figure 1
An SOM magnesium electrolysis schematic diagram.



Figure 2
A single-tube SOM magnesium electrolysis reactor design.



Figure 3
Current densities in a three-tube magnesium SOM reactor as calculated by Julian: light gray = cathodic (magnesium reduction), dark gray = anodic (oxygen flux through SOM).



Figure 4
A schematic of the process to obtain fi nite temperature thermodynamic properties through a combination of DFT codes and ATAT.



Figure 5
Python interface layer for ab initio finite temperature thermodynamics.



Figure 6
Integrated computational materials engineering.17



Figure 7
A schematic of the new paradigm in multiscale materials modeling.19



Figure 3
Eric Raymond’s concept of expansion of open source and proprietary software capabilities.












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© 2008 The Minerals, Metals & Materials Society

Though open source engineering analysis tools have not been widely deployed, several of them have recently reached a point of maturity and usability in industry. This article focuses on the use of open source tools for modeling of materials and materials processes in particular. After defining open source software, it presents two case studies, surveys open source tools aimed at modeling of materials behavior and processes at multiple length and time scales, and discusses future prospects and application areas for open source tools.


…describe the overall significance of this paper?
This paper describes a variety of new high-quality, robust and user-friendly open source tools which are becoming available for use in materials processing applications. For new modeling paradigms in particular, such as integrated computational materials engineering, open source tools are playing an integral role, and their use is likely to increase over time.

…describe this work to a materials science and engineering professional with no experience in your technical specialty?
Modeling and simulation have become an intrinsic part of development of materials and processes for making them. In the past, proprietary tools have dominated this application space, as the licensing revenue stream has funded development of new features and usability. But new open source software is becoming available for these purposes, and the open source advantages of customization, long term maintainability, and freedom from single-vendor lock-in are potentially even more compelling than the absence of licensing fees.
…describe this work to a layperson?
Open source is a relatively new paradigm for creating inexpensive robust software which users/ developers may customize at will. The Firefox web browser is perhaps the best-known example of this paradigm; because its source code is open for anyone to add to, numerous companies have contributed new features and fixed its bugs. This paper describes new open source codes for well established simulation techniques, and other trends in simulation method development, which are likely to increase the prominence of open source code moving forward.

Open source software has become prevalent in many applications, from server operating systems to scientific computing and high-end graphics. This class of software is distributed with source code, and with no restrictions on making and redistributing modified versions. There are therefore no licensing fees involved, and source code access is of great benefit to educators, researchers, and those who need custom modifications. On the other hand, as it is usually written for its authors’ needs, the user interface of open source software is often not as polished as that of proprietary software, and the lack of licensing revenue means that its feature set often lags behind.

In spite of these general drawbacks, several open source software suites have recently grown in sophistication and polish to the point where they are broadly useful for robust engineering simulations. User interfaces have also improved to the point where a small but increasing number of these tools are usable by non-experts. And the price point, ability to customize, freedom from single-vendor lock-in, and long-term maintainability of these tools make them potentially very attractive. In fact, the open source phenomenon has gained sufficient interest in the materials science and engineering community that the symposium “Open Source Tools for Materials Research and Education” is planned for the TMS 2009 Annual Meeting.

This article presents an overview of open source tools for process modeling. Case studies are considered in which the ability to modify an open source code played a key role in the project’s success: boundary element modeling of a new magnesium process, and python scripting of pre-processing, control and post-processing of ab initio calculations of thermo-mechanical properties of crystal structures. Also provided are business models that generate and sustain open source codes, and consideration of future prospects moving forward.


Though on only a tiny minority of desktop computers, open source dominates several fields of use. For example, Linux runs major data centers such as those of Google, Yahoo, eBay, Amazon, the New York Stock Exchange, and many of the world’s largest banks. In addition, 85% of the top 500 supercomputers in the world run Linux,1 and Linux is used exclusively by all of the animation studios producing major motion pictures, on both render farms and animator desktops.2 The Apache web server powers over half of websites,3 and the market share of the open source Firefox web browser in Europe is nearly 30%, and is approaching 50% in several countries.4

Broadly speaking, open source software is software for which source code is available, and for which modification and distribution of modified versions are not prohibited by its author. This includes software with source code in the public domain (i.e., not copyrighted), and copyrighted software with licenses that permit such modification and distribution. Indeed, much open source software uses licenses that require that modified versions be distributed under the same terms, with source code available and no restrictions on redistribution of derived works; such licenses are often called “copyleft” licenses. The commonly accepted definition5 of open source software, and a list of licenses which conform with that definition, is maintained by the Open Source Initiative, a nonprofit corporation dedicated to advocating for the benefits of open source products.6

The term “free software” is similar in definition, but those who use it generally have different goals from those who use “open source.” Users of the former term, such as the Free Software Foundation, tend to focus on freedom as a moral imperative. The Free Software Foundation was founded in 1985 to advocate for free, rather than proprietary software. Free software proponents claim that restricting distribution of source code is unkind to one’s fellow programmers. Open source advocates focus on its merits as a methodology for producing robust and inexpensive software.

In addition, copyright restrictions must be distinguished from trademark policies. For example, one can legally distribute derivatives of the complete RedHat Enterprise Linux product, but RedHat does not permit the use of its trademarked name in such derivatives. For the purpose of this article, the authors consider permission to modify and re-distribute as the operating attributes of open source software, while recognizing that the particular characteristics of the licensing scheme need to be addressed on a per-case basis.


Solid-Oxide Membrane Electrolysis of Magnesium
Solid-oxide membrane (SOM) electrolysis, shown in Figures 1 and 2, is a promising method for producing pure magnesium vapor from dolomite ores or other magnesium oxide or hydroxide sources using just 10 kWh of energy per kg of product.7 The magnesium (hydr)oxide dissolves in a molten salt electrolyte (typically CaF2-MgF2 eutectic). At the cathode, Mg2+ ions are reduced to magnesium metal vapor, and at the cathode, the SOM (typically yttria-stabilized zirconia) allows only oxide ions to pass through even at high potential, so the by-product is oxygen.

The magnesium vapor in turn can either condense to produce liquid or solid product, or else can react with other species. For example, reaction with tantalum oxide produces tantalum metal, with titanium oxide produces Ti3O which reduces more readily to metal, and with hydrogen gas produces MgH2 for hydrogen storage. A laboratory scale reactor with a single SOM tube has run at close to 1 A/cm2 for four days without noticeable SOM degradation, so the process appears to be robust.

Process scale-up is proceeding with a three-tube reactor running at very high current. But before building that reactor, it was necessary to first model the process to assess the uniformity of heating of the SOM tubes, as non-uniform heating would lead to breakage. For this purpose, the open source Julian boundary element code8 formed the basis of a three-dimensional (3-D) model of current density in the reactor. Results of that model can be seen in Figure 3.9

Open source was helpful here in two ways. First, Julian did not have sufficient geometric flexibility to represent some features of the SOM tubes, so it was necessary to extend its capabilities, which only the software owner could have done with a proprietary product. (That the author was also an investigator in this project somewhat diminishes this advantage of open source.) Second, as a component of Rachel De Lucas’s graduate research, the ability to see and understand the Julian source code added significantly to the educational value of the modeling task.

Interfaces to Open Source Computational Materials Science Tools
One of the criticisms commonly levied against open source code is that in many cases (although not always), extensive expertise on the part of the user is necessary in order to make maximum use of the tool. This high barrier to entry often hinders the widespread use of these computational tools. Although many of the user-unfriendly “features” of many of the freely available open source materials simulation tools have been recently alleviated, there is still significant room for improvement.

A particularly effective approach to improve the interfacing between nonexperts and sophisticated codes is the development of wrapping interfaces that would control the pre processing, execution, and post-processing of expert-oriented codes through the use of high level gluing programming languages. The Python10 has become very attractive due to its ability to interface not only with operating systems but also to native codes written in Fortran, C, and C++, as well as their variants. The power of Python goes beyond mere gluing or wrapping and in fact, many of the software tools described in this paper (FiPy,11 OOF12) are written in Python, using compiled Fortran and C codes for the numerically intensive components of the programs. To give an example of the power of Python as an interfacing layer, the problem of the calculation of thermodynamic properties of crystals at finite temperatures through ab initio methods is considered here. (Details of the codes mentioned here are provided later.)

To a very first approximation, the finite temperature thermodynamic properties of crystals in which configurational degrees of freedom (DOF) can be ignored, are dominated by thermal excitations of vibrational modes. In order to determine the vibrational properties of crystal systems, one can make use of the harmonic lattice dynamics approximation,13 in which the atoms are assumed to oscillate within a harmonic approximation to the real crystal potential. The mass-spring system then oscillates with characteristic frequencies corresponding to the vibrational modes available, which can in turn be determined through conventional lattice dynamic approaches.13 The open source ab initio thermodynamics Alloy Theoretic Automated Toolkit (ATAT) code14,15 implements harmonic lattice dynamic calculations in which the spring constants are calculated through the perturbation of the ground state crystal, and the calculation of the resulting inter-atomic forces. Although the ATAT code does an excellent job at automating many of the procedures necessary to determine the vibrational behavior of crystals, it is necessary to point out that many different steps are necessary to perform such calculations.

For example, once a crystal structure has been optimized with respect to external/internal degrees of freedom through the use of a density functional theory (DFT) code, it is then necessary to perturb the crystal structure (using ATAT) in order to calculate the reactive forces to atomic displacements. If one is to consider the effects of thermal expansion, the quasi-harmonic approximation requires the same procedure to be performed for many different volumes to capture the non-harmonicity of the real crystal potential. In order to extract the thermodynamic properties of the crystal in question one then has to post-process the information generated by the ATAT code, which consists of the vibrational phonon density of states, along with tabulated vibrational free energies. Figure 4 illustrates these steps required for such a calculation. Although the process is relatively straightforward for experts, it goes beyond the interests of a user only interested in extracting this information to adjust parameters of CALPHAD16 models.

Thanks to Python scripting, the whole process can become more streamlined for the casual user. The schematic in Figure 5, for example, represents a very user-friendly Python-based interface developed for the calculation of ab initio finite temperature thermodynamics integrating ATAT with a DFT code. The Python script takes user input such as symmetry and composition of the crystal structure, and then generates the corresponding input files for the ATAT and the DFT codes. The Python script not only controls the interactions between the different computer software, but is also capable of controlling/steering the calculations themselves, interacting with the job scheduling service used in the particular cluster computer used. The post-processing of the calculations can then be performed within the Python environment, with almost no interaction with the end user.


When considering open source tools for process modeling within the emerging paradigm of integrated computational materials engineering (ICME), there exists a very rich and diverse set of open source tools for materials simulation. However, the authors do not make any explicit endorsement of any particular computational tool.

Integrated Computational Materials Engineering
The relatively new field ICME17 constitutes a new paradigm for the efficient design of materials and entails a comprehensive integration of information for all relevant material phenomena, from the atomic through the meso- to the macro-scale. This approach enables “the concurrent analysis of manufacturing, design and materials within a holistic system.”17 Such an integrated paradigm can potentially accelerate the development of novel materials, mainly through the reduction of the time/effort involved in the design→synthesis→evaluation cycle that is achieved by integrating materials process models and property simulations into the design process, as shown in Figure 6.

At the most fundamental, ICME rests on the processing-structure-properties (PSP) relationships that have been the cornerstone of materials engineering for decades. This paradigm has been refined in the past decade, beginning with the pioneering article by G.B. Olson.18 In that work, Olson proposed a systems approach that integrated PSP relations in the conceptual design of materials and in which a multi-level hierarchy of necessary computational tools was established based on the information necessary to make design decisions at all relevant length and time scales. Unfortunately, the ICME paradigm has been limited by the lack of an integrated computational materials toolkit capable of addressing the complex, multi-scale phenomena relevant to materials engineering. J. Allison et al. claim that, apart from the inherent complexity of materials phenomena, the main roadblock to an effective ICME results from the focus on understanding isolated phenomena without paying much attention to linkages between the diverse knowledge base.17 Liu et al.19 have recently proposed the linking of multiple length and time scales in materials phenomena through materials informatics.

Recent proof-of-concept studies20,21 illustrate the usefulness of ICME to reduce the number of iterations in the materials development process, reducing in turn the cost and time associated with finding optimal engineering solutions. Both approaches accomplish the integration of knowledge originating in computational materials tools aimed at multiple scales, ranging from atomistic to the continuum. Widespread implementation of ICME to many more materials design problems has become possible thanks to the recent emergence of a collection of powerful open source computational materials software.

Density Functional Theory and Alloy Theory
In order to understand/manipulate the macroscopic materials properties, it is first necessary to rely on an accurate description of the relationships between such properties and the materials’ electronic structure.22 Determination of the latter requires, in principle, the solution of the many-body Schroedinger equation, an insurmountable task which has been made tractable thanks to the development of approximate theories. Among them, DFT23 has become the most widely used within the materials science community.22,24 In just a few years, a very powerful, open-source DFT code, ABINIT,25 has become one of the dominant computational tools to investigate the electronic structure of both molecules and periodic crystalline solids. Thanks to first-principles DFT calculations, a wide-range of physical properties can be calculated.20,22

Through DFT, one can investigate the properties of solids at the ground state. However, one needs to investigate the effects of thermal excitations on the degrees of freedom available to a given physical system if one is to understand the phase stability of materials at finite temperatures. Thermally excited effects, such as phonons, electron excitations in systems with finite e-dos at the Fermi level, and configurational DOF in crystals with compositional disorder can have significant effects on the thermodynamics of complex materials systems. Calculations of excitations of these DOF are complicated for everyone other than experts. Very recently, this process has become much simpler thanks to the ATAT code.15 The ATAT code incorporates lattice dynamics13 to take into account thermally excited phonon DOF. The effects of configurational DOF are taken into account through the cluster expansion formalism.26 Lattice Monte-Carlo simulations can in turn be used to investigate finite temperature phase stability. Through this tool, it is possible to calculate, within accuracy limits set by the precision of DFT itself, the phase diagrams of binary systems of technical importance and, by extension, their thermodynamic properties.19,27

Despite their promise, first-principles methods are limited by the inherent inaccuracy of the approximations necessary within DFT. At the industrial level, for example, it is necessary to establish the phase stability of systems within a few degrees, which would require unrealistic accuracy levels in electronic structure calculations. Moreover, first-principles methods are incapable of treating multi-component systems relevant to practical applications. To bridge the gap28 between first-principles and practical applications, the so-called CALPHAD16 approach can be used. The CALPHAD approach consists of the description of the Gibbs energy of phases in a system through simple phenomenological models. The parameters of such models can then be determined through experimental data as well as through first-principles calculations. Although at the moment there are no open source computational thermodynamics tools, complete models for technologically important systems are available to the general public.

Microstructure Evolution
In order to make sensible predictions about the evolution of a material system as it approaches equilibrium, it is first necessary to have an accurate description of its phase stability. Once this is available, one can use microstructural modeling approaches to link local contributions to the free energy to microstructure-dependent contributions. A very successful method for modeling microstructural evolution is the phase-field approach.29 Despite its success, the phase-field has not been incorporated into large-scale ICME efforts, in part due to the lack of easy-to-use, open source phase-field modeling approaches. Very recently, this has been alleviated through the FiPy code,11 a Finite–Volume solver for partial differential equations, written in Python10 and based on an object-oriented programming model. Thanks to this impressive tool, it has now become possible to focus on the development of sophisticated phase field models to investigate complex materials phenomena without focusing too much on the actual implementation of the numerical simulations. This tool can thus be used to develop higher-level simulations, in the spirit of ICME.

Macroscopic Property Prediction
After microstructures have been simulated either through the open source FiPy or other open/close computational tools, one would like to examine the response of such microstructure to external stimuli. A relatively new approach to model the response of microstructure is the OOF code.12 This code combines graphical microstructure data (real or virtual) with material properties (scalar or tensorial) databases for each of the constituent phases of the microstructure to model the behavior of the material under external boundary conditions. Although the code is currently limited to two-dimensional geometries and simple material responses, it is to be expected that further development will allow materials researchers to make use of OOF and other codes similar to it as computational microstructural engineering tools.

Crystal plasticity has also emerged as a means of calculating macroscopic mechanical properties by simulating the motion of individual dislocations through a polycrystalline domain with many precipitates. Two proprietary codes (MSC.Marc200x and ABAQUS/ Standard) implement this algorithm, and are very successful at simulating single crystal deformation, but have problems with even bicrystals depending on the orientation of the grains.30 The authors do not know of open source software in this domain.

Recently, Liu and others19 have proposed such an integrated approach toward the multi-scale modeling and design of materials. Figure 7 presents a schematic illustrating the implementation. The schematic mainly focuses on the integrated computational modeling of multicomponent, heterogeneous materials through the integration of several existing or in-house computational tools. As mentioned, many of the tools in principle necessary to perform such an integrated computational materials design are already available as open source code. Omitted from the discussion above is the fact that open source-based ICME depends strongly on the availability of open material property databases, or, in absence of this, a way to naturally interface open source code with commercially available databases. A discussion of open vs. proprietary databases would be very worthwhile, but is beyond the scope of this article.

Macroscopic Phenomena
There are many open source tools for solving the partial differential equations involved in modeling macroscopic phenomena such as mechanical deformation and transport phenomena. Numerous available codes for fluid dynamics, heat transfer, and mass transfer grew out of university and government research projects. Of those codes, a few have emerged as leaders. With few exceptions, the tools listed below come with the CAELinux live DVD Linux distribution.31

In mechanics and heat transfer, Code_Aster, CalculiX, and Impact are leading open source tools. Code_Aster33 is a large finite element code which Electricité de France (EDF) has written to solve complex problems in nuclear power. It comes with tools for adaptive remeshing, and is ISO 9001 certified. CalculiX32 is an implicit code for quickly calculating mechanical or thermal steady state solutions, or performing buckling calculations, but without adaptive remeshing. And Impact34 is an explicit mechanics code written in Java whose eventual goal is to simulate automobile collision dynamics. Each has a different input file format, and Code_ Aster and Impact have user friendly pre- and post-processing graphical interfaces. Code_Aster in particular can be controlled by the Salomé graphical simulation environment, which interfaces with numerous meshing and visualization libraries, and will likely be able to control more finite element software in the future.

For computational fluid dynamics (CFD) and coupled heat and mass transfer, leaders include OpenFOAM, libMesh, and Code_Saturne. Open- FOAM35 comes from the OpenCFD Ltd. consulting company, and includes an extensive set of solvers, utilities for pre-and post-processing, and physical model toolbox libraries. It runs in parallel with an efficient iterative implicit time-stepping scheme, and includes several turbulence models. LibMesh36 is a parallel finite element library with implicit timestepping, adaptive remeshing, and dynamic repartitioning across a cluster. Like FiPy, it can solve fourth-order biharmonic equations needed for Cahn–Hilliard phase field simulations. Unfortunately, it has no user interface at all; to generate a new simulation, one must write a short C++ program which calls the library’s functions. Code_ Saturne37 is the EDF fluid dynamics counterpart to Code_Aster, and features magneto-hydrodynamics, incompressible or compressible flows, multi-phase flows (arbitrary Lagrange–Euler mesh deformation), and turbulence models, along with advanced heat transfer capabilities such as radiation and combustion.

There are far too many PDE solver codes for solving continuum problems to list here. Those listed above have comprehensive features, well-developed front ends (except for libMesh), and most importantly, commitment to long-term maintenance.


At this point, open source software might sound too good to be true, one of those passing fads which should have been swept away by the dot-com bust. Why would one distribute source code, the precious “DNA” of a computer program which Microsoft and others work very hard to protect?

One reason for forgoing the revenues from a licensing stream is that such profits are forbidden, or there are more important motivations. Software produced by U.S. government agencies which is not classified cannot be copyrighted, and is thus in the public domain. For academics, the goal of impact or recognition can be at least as important as financial reward. This has always been true of publishing scientific results, and more recently, having one’s operating system research project accepted into the Linux kernel has conferred significant credibility to its author. This type of motivation resulted in the authoring of the Berkeley System Distribution implementation of Unix, the X Window system at Massachussets Institute of Technology, and numerous other open source projects.

Others find a project on the Internet, and are motivated to contribute to it in order to improve its suitability to their needs. Apache began as the University of Illinois National Center for Supercomputing Applications web server research project, and system administrators contributed patches extending its functionality (hence “a patchy web server”). Somewhat later, IBM “discovered” Apache and decided to make it the foundation of all of its web-services-related software, while putting significant resources into its development. This motivation is known as “scratching an itch:” a developer has a need for which there is no code, or a feature not present in a given code. Living with this problem “itches” until the developer “scratches” it by writing a new program or adding the feature to an existing one.

But today, most open source software is not created by academics or hobbyists, but by for-profit companies. What follows is a brief synopsis of other motivations and business models for authoring and extending open source software as given by Eric Raymond’s essay “The Magic Cauldron.”38
  • Loss-leader/market positioner: use open source software to create or maintain a market position for proprietary software (e.g., an open source client creates a market for a proprietary server).
  • Widget frosting: publish open source drivers for proprietary hardware, both for peer review benefits and also to allow operating system vendors/maintainers to adapt the driver to future changes in system interfaces.
  • Consulting, also known as “give away the recipe, open a restaurant:” use expertise in an open source product to drive revenue for packaging and/or consulting services (e.g., OpenFOAM mentioned previously).
  • Accessorizing: sell books or other accessories to open source products (e.g., O’Reilly publishers).
  • Free the future, sell the present: sell a proprietary product with a license that guarantees open source release after a certain time, in order to guarantee future maintainability to prospective customers (e.g., Alladdin GhostScript).
  • Free the software, sell the brand: charge for the branded, trademarked, tested, and certified version of an open source product (e.g., RedHat).
Raymond’s essay also pointed out that in most cases, open source software does not have as much revenue available to fund its development as is provided by license fees of proprietary software. For this reason, proprietary software often leads the development of end-user software, while open source provides low-end users with an inexpensive alternative, and also provides an open and flexible platform for those who need custom modifications. Proprietary products must therefore keep innovating to push the frontier forward, as its open source competitors catch up behind them. In some areas, open source leads proprietary products. These have tended to either be standardized commodity infrastructure tasks where multiple stake holders drive feature addition and architecture updates (e.g., Linux, Apache, Firefox), or new application fields where open source establishes an early lead (e.g., ABINIT, ATAT). These trends are shown schematically in Figure 8.

In the past, research codes have formed the basis for proprietary products. For example, John Hallquist wrote the DYNA3D finite-element analysis code for simulating deformation of shell structures while working at Lawrence Livermore National Laboratory (LLNL) and released it into the public domain in 1978. In 1989, Hallquist left LLNL to form the Livermore Software Technology Corporation (LSTC), which has released and supported new versions of LS-DYNA since then.

Moving forward, this is less likely to happen because newer codes such as ABINIT mentioned above tend to use copyleft licenses which prohibit proprietary derivatives. In fact, in the commercial world, many potential contributors refuse to submit patches to non-copyleft open source software, because competitors can incorporate their contributions into proprietary products. For this reason, open source software which opens new fields, such as the codes briefly introduced in this article, are likely to remain at the forefront of technology, as re-implementing them would present a substantial task to a prospective proprietary competitor.


Open source software plays a large and growing role in research and engineering for materials processing. Many new tools have recently reached a level of feature completeness and usability that makes them suitable for broader industrial use. Moving forward, the role of open source is likely to continue to expand, as yesterday’s proprietary features enter tomorrow’s open codes, and as new fields open up with open source software in a leading position. On the other hand, the substantial lead of proprietary software in many fields will likely give it an edge in advanced features for some time, particularly in thermodynamics, crystal plasticity, and macroscopic simulations.


Raymundo Arroyave would like to thank Michael E. Williams for creating some of the figures used in this article. Adam Powell would like to thank Rachel DeLucas and Uday Pal for their work on simulating SOM electrolysis of magnesium, and Francesco Poli for pointing out some of the codes described in this article.


1. “Operating System Family Share for 11/2007 | TOP500 Supercomputing Sites,”
2. M. Macedonia, “Linux in Hollywood: A Star is Born,” Computer, 35 (2002), pp. 112–114.
3. Netcraft,
4. “,”
5. “The Open Source Defi nition | Open Source Initiative,”
6. “Open Source Initiative,”
7. A. Krishnan, U.B. Pal, and X.G. Lu, “Solid Oxide Membrane Process for Magnesium Production Directly from Magnesium Oxide,” Metallurgical and Materials Transactions B, 36 (2005), pp. 463–473.
8. A.C. Powell and Y. Lok, “Julian Boundary Element Code,”
9. R.A. DeLucas, A.C. Powell, and U.B. Pal, “Boundary Element Modeling of Solid Oxide Membrane Process,” TMS 2008 Annual Meeting Supplemental Proceedings Volume 2: Materials Characterization, Computation and Modeling (Warrendale, PA: TMS, 2008), pp. 301–306.
10. “Python Programming Language—Official Website,”
11. “FiPy,”
12. S. Langer, E. Fuller, and W. Carter, “OOF: An Image-based Finite-Element Analysis of Material Microstructures,” Computing in Science & Engineering, 3 (2001), pp. 15–23.
13. A. van de Walle and G. Ceder, “The Effect of Lattice Vibrations on Substitutional Alloy Thermodynamics,” Reviews of Modern Physics, 74 (January 2002), p. 11.
14. Axel van de Walle, Gautam Ghosh, and Mark Asta, “Ab initio Modeling of Alloy Phase Equilibria,” Applied Computational Materials Modeling (2007), pp. 1–34;
15. A. van de Walle, “Alloy Theoretic Automated Toolkit (ATAT),”
16. L. Kaufman, “Computational Thermodynamics and Materials Design,” CALPHAD, 25 (2001), pp.141–161.
17. John Allison, Dan Backman, and Leo Christodoulou, “Integrated Computational Materials Engineering: A New Paradigm for the Global Materials Profession,” JOM, 58 (11) (2006), pp. 25–27.
18. G.B. Olson, “Computational Design of Hierarchically Structured Materials,” Science, 277 (August 1997), pp. 1237–1242.
19. Zi-Kui Liu, Long-Qing Chen, and Krishna Rajan, “Linking Length Scales via Materials Informatics,” JOM, 58 (11) (2006), pp. 42–50.
20. Daniel G. Backman et al., “ICME at GE: Accelerating the Insertion of New Materials and Processes,” in Ref. 16, pp. 36–41.
21. J. Allison et al., “Virtual Aluminum Castings: An Industrial Application of ICME,” in Ref. 16, pp. 28–35.
22. J. Hafner, “Atomic-Scale Computational Materials Science,” Acta Materialia, 48 (January 2000), pp. 71–92.
23. W. Kohn and L.J. Sham, “Quantum Density Oscillations in an Inhomogeneous Electron Gas,” Physical Review, 137 (March 1965), p. A1697.
24. J. Hafner, “Materials Simulations Using VASP—A Quantum Perspective to Materials Science,” Computer Physics Communications, 177 (July 2007), pp. 6–13.
25. X. Gonze et al., “First-Principles Computation of Material Properties: The ABINIT Software Project,” Computational Materials Science, 25 (November 2002), pp. 478–492.
26. J.M. Sanchez, “Cluster Expansions and the Confi gurational Energy of Alloys,” Physical Review B, 48 (November 1993), p. 14013.
27. Zi-Kui Liu and Long-Qing Chen, “Integration of First-Principles Calculations, Calphad Modeling, and Phase-Field Simulations,” Applied Computational Materials Modeling (2007), pp. 171–213;
28. P.E.A. Turchi et al., “Interface between Quantum- Mechanical-Based Approaches, Experiments, and CALPHAD Methodology,” CALPHAD, 31 (March 2007), pp. 4–27.
29. J.Z. Zhu et al., “Linking Phase-Field Model to CALPHAD: Application to Precipitate Shape Evolution in Ni-Base Alloys,” Scripta Materialia, 46 (March 2002), pp. 401–406.
30. F. Roters, “The Texture Component Crystal Plasticity Finite Element Method,” Continuum Scale Simulation of Engineering Materials (New York: Wiley, 2004),
31. “CAELinux,”
32. Electricite de France, “Code_Aster,”
33. G. Dhondt and K. Wittig, “CALCULIX: A Three- Dimensional Structural Finite Elemente Program,” CALCULIX,
34. J. Forssell and Y. Mikhaylovski, “Impact Finite Element Program,”
35. “OpenFOAM: The Open Source Computational Fluid Dynamics (CFD) Toolbox,” /openfoam/.
36. “libMesh—C++ Finite Element Library,” http :// 37. Electricite de France, “Code_Saturne,”http://rd.edf .com/code_saturne.
38. E. Raymond, “The Magic Cauldron,” The Cathedral and the Bazaar (Sebastopol, CA: O’Reilly, 1999),

Adam C. Powell IV is with Opennovation, 1170 Chestnut St., Newton, MA 02464-1309; and Raymundo Arroyave is with Texas A&M University, 119 Engineering Physics Building, College Station, TX 77843-3123. Dr. Powell can be reached at