Invited Paper MG+EN+MS-WeA7
Educating for High-Impact Computation - Skills vs. Acceptance

Wednesday, October 30, 2013, 4:00 pm, Room 202 B

As an integral part of the Materials Genome Initiative (MGI), the task of materials computation, in concert with experiment and theory, is to help accelerate the discovery and maturation of new materials by at least a factor of two. During the first rounds of MGI-related solicitations, two major groups of challenges that always existed became very evident. For one, the obvious question about the skill set available in the Materials Research community to actually perform the necessary computations. Secondly, and much less apparent on the surface, was the frequent lack of acceptance of computational work as a valid input, maybe foremost in the experimental community, which can lead to awkward situations, missed opportunities, and frustration in collaborative projects. Beginning with the 2012-2013 academic year, The Ohio State University has moved from a quarters-based academic calendar to a semesters-based calendar. As part of this change, the Department of Materials Science and Engineering has elected to revise degree program curricula in a significant manner. A key objective in our revision was to respond to the challenges in skill set and acceptance of computational work from Integrated Computational Materials Engineering and MGI described above. We have developed a curriculum that attempts to integrate congruently database use, visualization, simulation and computational approaches in materials science with other core educational content. At the undergraduate level, our goal was to produce graduates who are cognizant of the broad range of computational tools available to materials engineers and what they can do to solve engineering problems, and who are able to use a number of those tools proficiently to solve problems of practical importance themselves. The MSE core curriculum includes 9 credit hours (four courses), or 20% devoted to these topics. Students may take an additional 4 credit hours (two courses) in elective content on computational methods in materials science. In this presentation, details will be presented on the specific course offerings, course content, exercises, and software packages used. How the courses are postured in the curriculum will also be addressed. The experiences, challenges, and recommendations resulting from the first year of teaching will finally be discussed, where the author was involved in four different courses relying on different combinations of traditional teaching with reverse and peer teaching approaches as well as with significant fractions of active-learning work.