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Computational Materials Science and Engineering
Education: A Survey of Trends and Needs

K. Thornton, Samanthule Nola, R. Edwin Garcia, Mark Asta, and G.B. Olson

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Survey results on the importance of two specific aspects of CMSE education from the computational faculty members’ view and that of the department chairs and program heads.



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Survey responses on how CMSE should be implemented into undergraduate MSE education, obtained from the same two groups. *“Equally useful” option was omitted for the computational faculty survey, but some respondents wrote in this option (shown in light red).



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Survey responses about awareness and utilization of selected NSF-sponsored web-based resources.



The list of responding organizations is as follows:

    • Auburn University
    • Boise State University
    • CA Polytechnic State Univ.
    • Carnegie Mellon University
    • University of Delaware
    • Georgia Institute of Tech.
    • Iowa State University
    • Missouri University of Science & Technology
    • Massachusetts Institute of Technology
    • Northwestern University
    • Ohio State University
    • Pennsylvania State Univ.
    • Rensselaer Polytechnic Institute
    • Stanford University
    • Rutgers
    • Texas A&M
    • University of Alabama
    • University of Alberta
    • Univ. of California Berkeley
    • Univ. of California Davis
    • Univ. of California
    Los Angeles
    • Univ. of California Santa Barbara
    • University of Florida
    • Univ. of Illinois Urbana Champaign
    • Univ. of Massachusetts
    • Univ. of Michigan Ann Arbor
    • University of Pennsylvania
    • University of Tennessee
    • Univ. of Wisconsin Madison
    • University of Virginia
    • QuesTek Innovations
    • Opennovation
    • Sandia National Laboratory
    • Air Force Research Laboratory (Wright Patterson)
    • Naval Surface Warfare Center
    • National Institute of Standards and Technology
    • Ford Motor Company
    • General Electric
    • GM Corporation
    • Medtronic
    • Aleris International
    • Littelfuse
    • Thermart










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© 2009 The Minerals, Metals & Materials Society

Results from a recent reassessment of the state of computational materials science and engineering (CMSE) education are reported. Surveys were distributed to the chairs and heads of materials programs, faculty members engaged in computational research, and employers of materials scientists and engineers, mainly in the United States. The data was compiled to assess current course offerings related to CMSE, the general climate for introducing computational methods in MSE curricula, and the requirements from the employers’ viewpoint. Furthermore, the available educational resources and their utilization by the community are examined. The surveys show a general support for integrating computational content into MSE education. However, they also reflect remaining issues with implementation, as well as a gap between the tools being taught in courses and those that are used by employers. Overall, the results suggest the necessity for a comprehensively developed vision and plans to further the integration of computational methods into MSE curricula.


Materials science and engineering (MSE) encompasses metallurgy, semiconductors, ceramic engineering, and polymer science. It is a multidisciplinary field that enables new technologies required to address a wide variety of critical challenges facing society, such as clean energy production. While traditionally viewed as an experimental discipline, many researchers have begun to take advantage of rapidly growing computing resources and associated algorithmic and theoretical developments, and the capabilities of integrated computational approaches are increasingly being utilized to accelerate materials design and development. Recent National Research Council (NRC) reports1,2 indicate that successful integration of computational tools has also begun to be demonstrated in industrial settings, comparing its potential impact to that of bioinfomatics. The reports summarized recommendations that include incorporation of computational modules into a broad range of materials science courses in order to train the next generations of materials engineers with the abilities required to exploit these tools. However, the degree to which such efforts are already under way, and what steps must still be taken to address these NRC recommendations remain unclear. Therefore, we have undertaken a survey of the field to assess the current status of computational materials science and engineering (CMSE) education. A summary is presented below, which serves as an update to a previously published report3 based on similar surveys performed in 2003–2004. See the sidebars for a survey description, the list of respondents, and CMSE resources.

Survey Procedures
Following the procedure of the previous study,3 three sets of surveys were performed. The first was a short survey sent to the chairs and heads of materials programs (hereafter referred to simply as chairs) that participate in the University Materials Council. The second survey was more detailed and sent to faculty members engaged in computational research and education (hereafter referred to as computational faculty), which was initially sent to the chairs and department heads to be forwarded to their faculty and was followed up by the authors. A third survey was sent to employers of materials scientists and engineers.

The surveys were mainly distributed to U.S. universities, national laboratories, and materials-related industries. The chair survey was comprised of five questions, all of which focused on undergraduate education. Information was sought to determine the chairs’ perspective on how important CMSE education is, how this material should be covered in undergraduate curriculum, and how much CMSE education is supported by their faculty. Nineteen responses were returned for this survey. The second survey targeted computational faculty. In addition to questions similar to those in the chair survey, information describing the current and planned course offerings in CMSE, as well as publicly available CMSE education resources, was requested. The survey of computational faculty also included questions pertaining to graduate curriculum in CMSE and the availability and preparedness of graduate students to perform computational research. Twenty-three responses were received from computational faculty. Finally, employers of MSE degree holders were asked to provide input. Four responses from national laboratories and twelve responses from industry were received. Respondents from large organizations generally answered for labs or groups they lead. Some large corporations had two respondents answering for different labs or groups. The questions addressed the importance of computational expertise in their organization, what computational tools are utilized, what background (including degrees) those who utilize them have, and future trends. We also solicited recommendations concerning what aspects of CMSE education may require improvement and how. Percentages are calculated out of the number of respondents to each specific question.
CMSE Education Resources
Table A lists examples of software packages that are being used in CMSE-related courses. Most of the reported packages are not tailored for education. Of the software listed in Table A, nine are commercial packages, while four are free software. Overall, the number of packages used for instructional purposes is relatively low, compared to the amount of research and development software and numerical libraries that are commercially or freely available. A great fraction of these packages (including all of the open source packages) focus on the atomistic aspects of materials simulation, revealing that there is little emphasis on simulations at meso/microscales and macroscopic scales, which may reflect the research expertise of many computational faculty members. The contrast between the tools utilized in teaching and those employed in industry, many of which focus on the continuum scales, was discussed previously and is shown in Table I. Programming languages used in CMSE classes can be divided into two classes: the low-level, yet more computationally efficient, languages like C, C++, and Fortran, versus higher-level languages such as Mathematica, MATLAB, and Python.

A number of computational faculty believe there is a shortage of appropriate software tools that could be integrated into their courses. There have been efforts to make CMSE education modules or MSE education modules based on computational tools freely available to interested educators. However, there has not been an assessment of how well these resources are utilized at large. One of the questions to computational faculty asked about their knowledge and use of four existing National Science Foundation sponsored websites with CMSE education modules. Of the 22 who responded to this question, 45% were aware of the resources at the Materials Technology@TMS ICME Community site (now a part of the Education Community), and of these, half (23%) used the content in their instruction. Forty-five percent were aware of the nanoHUB site although none used the tools from this web site in their courses. Forty-one percent were aware of the UIUC Materials Computation Center software archive, but only 9% used the tools available there in their courses. Twenty-seven percent were aware of the MatDL Digital Library Courseware, but only 5% used it in teaching. Overall, we find that many CMSE researchers/educators are not aware of the available resources, and those who are aware typically do not utilize them. Furthermore, the survey shows a lack of incorporation of the latest developments such as parallel computing and cloud computing (dynamically scalable internet-based computing) into CMSE education (see Figure 3).

Undergraduate Education in CMSE

The status of undergraduate CMSE curriculum was assessed through five survey questions directed to department chairs, as well as corresponding questions included in the survey targeted at computational faculty. Overall, 42% of the surveyed chairs consider integration of CMSE into the required undergraduate curriculum “very important,” versus 53% that consider it “somewhat important,” leaving only 5% (one respondent) stating “not important.” As an elective, however, a majority (56%) report that it is “very important” to have CMSE-related undergraduate courses, as compared to 31% that consider it “somewhat important.” These responses thus indicate a somewhat greater support for providing undergraduates an option (rather than a requirement) to study CMSE. Overall, the chair surveys suggest a majority view that CMSE plays a supporting role in undergraduate materials education, rather than being a focal point in the preparation of new generations of material scientists and engineers. In the responses from computational faculty, 62% consider it “very important” to integrate CMSE into the required curriculum, while 33% consider it only “somewhat important.” Perhaps not surprisingly, integration of CMSE into the required materials undergraduate curriculum is supported by a larger fraction of computational faculty than chairs.

Another question addressed the types of skills in CMSE that are considered important, specifically programming skills vs. skills to utilize tools (see Figure 1). More than 63% of surveyed chairs chose the ability to use computational tools over programming skills. Similarly, 62% of the surveyed faculty selected the development of skills to use computational tools, while only 14% considered programming skills a critical priority in the education of materials scientists and engineers. (The remainder considered the two skills equally important, and amongst these faculty some stated that programming was one of the skills required to effectively use computational methods.) Overall, the responses indicate a general preference toward teaching students to use computational tools in problem solving, rather than the skills required to develop computational tools.

Another important question addressed the mechanism for including CMSE in the undergraduate curriculum (see Figure 2). In response to the question of whether CMSE should be implemented into existing core courses or as a stand-alone course, only 11% of the chairs chose a stand-alone course, compared to 53% preferring implementation into the existing core courses; 36% chose “equal in usefulness.” A similar trend was observed in the computational faculty survey. Some respondents (including computational faculty and chairs) pointed out that CMSE has been used as a “virtual laboratory” to deliver materials science concepts that are difficult to demonstrate in a laboratory or illustrate in a traditional classroom setting. The role of CMSE as a design tool was also discussed. In addition, practical issues associated with implementation of CMSE were discussed in the respondents’ comments. A respondent questioned whether elective CMSE courses would be sufficiently subscribed. Others were concerned whether there is space in the required curriculum to incorporate CMSE topics. Additional barriers to implementing CMSE education more broadly were discussed, including the availability of funding and a lack of computational faculty in the department.

Graduate Education in CMSE

Five questions were included in the computational faculty survey to assess the status of graduate CMSE education. When asked about current offerings of CMSE courses, 39% of the 18 responding departments indicated no current offerings. Twenty-eight percent of the departments offer multiple computational courses, most of which were offered sequentially in alternating years. Seventeen percent described a pair of courses consisting of a course on quantum-mechanical, first-principles methods and another focusing on atomistic simulation (molecular dynamics and Monte-Carlo) methods. In one case, three different courses are offered in a single department, including a course on computational thermodynamics, one on continuum and mesoscale methods, and another on molecular scale modeling. In one case, where the department hosts both chemical engineering and materials science programs, two courses are offered: one focused on molecular scale simulation and another on computational thermodynamics and Monte-Carlo simulation. Eleven percent of the respondents described single course offerings that cover multiple methods and length scales, while in 17% of the cases only a single course is offered in the department focusing exclusively on atomistic- simulation techniques. In addition to the graduate courses devoted exclusively to computational methods, 36% of the respondents reported integration of computational approaches in courses covering more traditional topics. In two of these cases, computational thermodynamics software was used in a graduate-level thermodynamics course. In another case, a Monte-Carlo assignment is included in a course on phase transformations.

The computational faculty surveys also probed the availability of courses that focus on computational methods that are offered outside the department but are nevertheless frequently subscribed to by materials science graduate students. Twenty-seven percent mentioned related courses covering aspects of numerical methods, atomistic simulations, or electronic structure methods in chemical engineering, mechanical engineering, physics, and chemistry departments. Another survey question requested the number of courses on topics related to CMSE taken by a typical Ph.D. student pursuing a computational thesis; the number ranged from zero to six, with a typical range between one and three.

When asked what aspects of CMSE education require improvement, 36% of the 11 who responded identified the need for better software tools that could be integrated into their courses. In light of this, it is interesting that most computational faculty were aware of at least one web-based resource on CMSE education, but the majority did not utilize them.

One question addressed the level of interest of MSE graduate students in pursuing computational research. Of the 15 who responded to this question, 40% mentioned difficulty in recruiting students for computational research, while 27% stated that they have no such difficulty. Twenty percent mentioned a strategy of recruiting students from outside their departments (e.g., from physics or chemistry departments). Thirteen percent mentioned that students with an undergraduate degree in MSE do not generally have a background well suited for computational research.

Employers’ Perspective

Employer responses represented organizations ranging from small businesses to global corporations. The number of MSE graduates working at these organizations ranged from 1 to 1,000, with some predominantly at the BS/MS level and some predominantly Ph.D. level. The number of employees making significant use of computational modeling and simulation ranged from 1 to 50, with a greater representation at the Ph.D. level than BS/MS level. The typical number of MSE graduates hired per year at each organization was roughly 1 to 3 at the level of Ph.D., and 1 to 5 for the BS/MS levels. In addition to MSE graduates, these organizations also employ graduates from mechanical engineering, physics, and chemical engineering for the type of work performed by computationally inclined MSE graduates. More than half of the respondents (57%) stated that the number of MSE graduates hired, or the desired skill set, had changed over the past decade. The current percentage of hires with a CMSE background is now 37% on average, with an expressed desired future percentage of 50% on average. While there was a large range in the responses (less than 5% to 90%), this change in the future percentage of CMSE-trained hires appears to be significant.

Table 1

Categories of CMSE software tools and specific codes are listed in Table I, ranked by the frequency they were cited by employers as used in their organizations. Finite element analysis (FEA) tools such as ABAQUS and DEFORM were by far the most frequently cited tools. Together with casting simulators such as PROCAST and MAGMA, these tools predict macroscopic material behavior during processing and service and are closely linked to activities of other engineering disciplines that need to be integrated. Control of material structures is represented predominantly by CALPHAD computational thermodynamics tools such as ThermoCalc and Pandat. Atomistic-level tools based on density functional theory (e.g., VASP) were found to be widely utilized, often to support the thermodynamic tools, while other atomistic models such as molecular dynamics (e.g., LAMMPS) and Monte-Carlo methods were cited much less frequently. Microstructure evolution simulators such as DICTRA, PrecipiCalc, or JMatPro were cited less frequently. Half of the respondents also cited significant use of in-house developed computational tools. These organizations also develop software, in addition to using available tools. Other specific tools cited by more than one respondent include MATLAB, Materials Studio, and process integration and design optimization software such as iSIGHT.

Table A

Several suggestions were offered for improvements in CMSE education, citing a need at the BS/MS level to evaluate problems quantitatively and understand what tools exist and how they can be meaningfully applied to practical engineering problems such as design. Experience in applying the tools in an interdisciplinary collaborative setting was also recommended. There were comments on early experimental validation, the need for ability to utilize visualization techniques, the relative utility of different numerical methods in different situations, and a desire for students’ familiarity with the tools most commonly used in industry. All these needs were also cited at the Ph.D. level, with the addition of more extensive knowledge of various methods to guide appropriate application choices. There was general consensus for a strong base in thermodynamics and mechanics tools, with an expressed need for “a much better view of real-world engineering” as well as “a strong additional bank of knowledge in statistical mechanics, quantum mechanics and computer science, and software development.” Some recommended teaching the tools in the same courses where the underlying science is taught.

The majority of respondents felt that CMSE would play an even greater role in their organizations in the future. It was broadly expressed that moving away from slow and costly “trial and error” empirical development was vitally important for the future competitiveness of the MSE profession. About one third of the respondents emphasized the future importance of these tools in enabling computer-aided design of new application-specific materials.


The surveys summarized above indicate that the majority of chairs and computational faculty view that CMSE is an important aspect of MSE that should be implemented into undergraduate curricula. However, to modify the undergraduate curriculum, an endorsement from faculty is critical. We inquired of the chairs whether there is a general support amongst the faculty within their departments for such modifications. Only 13% of the respondents answered “no,” and an additional 19% responded “not sure.” The remainder, 69%, reported that there is support to integrate CMSE into the undergraduate curriculum; of these, 28% responded that integration into the required curriculum is supported. Integration of CMSE as elective courses appears to be most supported, with 41% of the respondents choosing this answer.


The results of the surveys can be summarized as follows:

  • A general consensus exists in academia, national laboratories, and industry that CMSE is of growing importance and requires attention in the curricula of MSE.
  • The academic surveys reveal a majority opinion that it is more important to teach the skills to use computational tools rather than the programming skills required to develop such tools, although a significant fraction feel that both are equally important.
  • A majority of academic respondents favor integration of CMSE into core courses (e.g., as modules), although a significant fraction support also a stand-alone course.
  • Faculty support appears to exist in many departments, especially for introducing CMSE as an elective portion of the undergraduate curricula.
  • Awareness of web-based resources and software repositories is moderate, but use of these resources for the purpose of teaching is limited so far. This underutilization may unnecessarily raise the barrier to incorporating CMSE into courses.
  • A gap exists between what is typically taught at universities (atomistic methods) and the tools most commonly used in industry (continuum methods such as FEA).
  • The employers responding to the survey indicated their preference in CMSE education for an overall emphasis on use of commercial tools to gain experience in solving practical engineering problems.
  • Employers commonly suggested that undergraduate MSE educators give more attention to the computational tools used in industry. In line with some faculty responses, application in design courses was also suggested in order to provide context closer to employer needs.

The survey revealed critical questions that need to be answered in order to determine the best steps forward. For example, despite the fact that there is a majority consensus that CMSE topics should be included in undergraduate MSE curriculum, the best way to do this remains uncertain. Even though more respondents (both chairs and faculty) preferred implementation of CMSE into required core courses, only 28% of the chairs felt that there was a general support within the department for necessary curriculum changes. Clearly this approach requires signifi cant efforts by multiple faculty members teaching the core courses and cannot be achieved without department-wide support. Another important question is what aspects of CMSE should be taught in undergraduate courses. While the survey results refl ect a majority view that the emphasis should be placed on developing skills to utilize tools, it is unclear exactly what tools should be introduced and how. While continuumlevel tools may be more appropriate for engineering, atomistic tools may be more appropriate for teaching materials science concepts.

All of these are complex issues that require discussions within the materials science and engineering community, including CMSE and non-CMSE researchers, educators, practitioners, and managers. We hope that this article serves as a starting point for active discussions. Case studies from institutions adopting various practices will be a valuable resource that would provide data points in such discussion. We plan to collect such case studies and compile them into a future JOM article. Other possible information could be obtained from recent graduates, who may provide insights into the gaps that may exist between MSE education and the skills required in order to gain employment and to succeed in the profession.


This report was made possible by numerous responses from universities, national laboratories, and industry. The authors acknowledge funding from the National Science Foundation DMR- 0502737 (K.T. & M.A.), DMR-0746424 (K.T.), and CMMI-0507053 (M.A.), as well as the ONR/DARPA D3D Digital Structure Consortium (G.B.O.), and the Materials Design Institute, a Los Alamos– UC Davis Educational Collaboration (M.A.).


1. Committee on Accelerating Technology Transition, National Materials Advisory Board, “Accelerating Technology Transition: Bridging the Valley of Death for Materials and Processes in Defense Systems” (Washington, D.C.: The National Academy Press, 2004).
2. Committee on Integrated Computational Materials Engineering, National Research Council, “Integrated Computational Materials Engineering: A Transformational Discipline for Improved Competitiveness and National Security” (Washington, D.C.: The National Academy Press, 2008).
3. K. Thornton and M. Asta, Modelling and Simulation in Materials Science and Engineering, 13 (2005), pp. R53-R69.

K. Thornton and Samanthule Nola are with the University of Michigan, Ann Arbor, MI 48109; R. Edwin Garcia is with Purdue University, West Lafayette, IN 47907; Mark Asta is with the University of California, Davis, CA, 95616; G.B. Olson is with Northwestern University, Evanston, IL, 60208. Dr. Thornton can be reached at