Gintaris Reklaitis: I would like to add to that point from the perspective of the software tools. The question that I have is, How do you teach people to use the computing tools that are getting increasingly more complex? I really think we need a lot of new ideas in this area. I am struck by the fact that we have tools like process simulators that are very time consuming to master. If you look at the user's manual for complex software tools, the first thing the user is asked to do is to spend 2 hours going through all the menus and clicking on all of the commands. Have you tried this with a group of undergraduates? It lasts about 5 minutes. This is absolutely not the way anyone wants to learn to use a software tool. We have to come up with intelligent ways around this training problem.

Specifically, I think that there is a lot of creative work that we need to do with our education colleagues to devise new models for learning and intelligently using complex software tools that have many options and possibilities. This is particularly relevant for the casual user, which is what most industrial users are. Most engineers are focused on projects rather than tools and will only revisit the tools periodically as the need arises. Such a practitioner surely does not want and does not have the time to reinvestigate all of the menus every time to recall what is available.

Evelyn Goldfield, Wayne State University: I want to follow up on the previous question because I think that a lot of universities, including my own, are rethinking some of the ways that we train graduate students and really do want to focus on some of these interdisciplinary team approaches, particularly in computational science. We found that there were a lot of different people at the university who should be and could be training together and working together but until recently were almost totally isolated. There tends to be duplication of effort, of reinventing the wheel. Also, too many students are not taking certain courses that would benefit them because these courses are offered in a different department or a different part of the university, whereas they are all using basically the same algorithms. And so, I wondered if you had any comments for how that these sorts of team and interdisciplinary approaches at the university would have any beneficial effects on your projects?

Gregory McRae, Massachusetts Institute of Technology: I would like to address that question from an MIT perspective because, in fact, just last week a whole new division has been formed in the Engineering School called the Engineering Systems Division, which is specifically directed at dealing with the issues that you talked about. It is across disciplines. It involves not only interaction with the Engineering School but also with the social sciences as well as with the management sciences.

It is by no stretch of the imagination an easy thing to do in a university, but there are people with vision to basically say this has to be done and they are putting the faculty slots on the table to actually make it happen. So, I think that there are some universities quite committed to doing that.

David Smith: I would just like to comment that the Design Center at Carnegie Mellon University had exactly the same strategy and was very, very effective for those of us who participated in it. It was a very good experience.

Judith Hempel, University of California, San Francisco: I was just going to ask you, the panel, what you see for the year 2020 in the sort of division that we currently see in the chemistry modeling area between what some people call materials research and on the other side, biological materials research. Many of the techniques are very similar when you go all the way from pharmaceutical modeling over to materials modeling. In 2020 will there be a division, do you think, of this kind? Or will it come together?

David Dixon: In principle, one would hope that they would come together. I would not guarantee it at

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