Q&A with materials innovator Dane Morgan

Posted on 27. Sep, 2013 by in Academic Departments, Annual Report, Chemical and Biological Engineering, Issues, Materials Science and Engineering, People, Research

 

Dane Morgan

Dane Morgan

Associate professor, materials science and engineering and engineering physics
Co-Director, Wisconsin Materials Institute

Talk about your research. What, simply, do you study?
I use computational modeling to understand and predict materials properties for a wide range of applications. By solving the fundamental quantum mechanical equations that describe atomic interactions, I can predict how atoms will behave with very limited experimental input. In terms of applications, a major focus of my work is energy technologies, including fuel cells, batteries and nuclear materials, but I also work in the areas of high-pressure and aqueous mineral geoscience and defect properties in semiconductors.

What are the key technical issues you focus on and how do you collaborate with others on campus to develop solutions?
The precise research challenges are specific to different applications, but a theme is the challenge of connecting the length scales of atoms—where I can make very precise predictions—with the much larger length scales of the technologies we use. This is often called the challenge of multiscale modeling. I generally bridge scales using thermodynamic and kinetic theories that are able to connect atomic processes—for example, the energy of a lithium atom in a crystal—to macroscale properties—for example, lithium battery voltages.

I collaborate with many of my UW-Madison colleagues to bring the inter-disciplinary skills needed to solve complex problems. For example, I work with a wonderful team (Chemistry Professor Robert Hamers and Associate Professor Mahesh Mahanthappa, and Chemical Engineering Professor Tom Kuech) with skills in electrochemistry, coatings and polymer chemistry to try to understand how to improve the performance of lithium batteries. I am also part of the UW-Madison Materials Research Science and Engineering Center integrated computational group. Led by Materials Science and Engineering Associate Professor Izabela Szlufarska, the group brings together modelers from all over campus with complimentary simulation skills.

What are some future opportunities or challenges in your research area?
Perhaps the most exciting opportunity to me right now is the recent advances in “first-principles” prediction, in which one can solve the fundamental quantum mechanical equations describing electrons and atoms to predict their properties without experiments. Such first-principles methods are now robust enough to take advantage of the full power of modern computers, which creates an unprecedented ability to generate valuable materials property data. It is no exaggeration to say that for some properties, first-principles calculations can now produce, in only a few years, more data than has been obtained in all of human history. For example, I can predict the stability of compounds and their elastic constants at a rate of dozens a day on a big cluster of computers. However, a key challenge is to connect these first-principles predictions to materials understanding and design. Knowing an elastic constant does not in itself tell me how to make a lighter battery or safer nuclear fuel cladding, but such data can greatly accelerate the design process if we can use it effectively.

What impact, both technically and in application, do you think your work has for the state and the nation?
In Wisconsin, the United States and the world, we face an unprecedented energy challenge to use our carbon fuels more efficiently and develop non-carbon-emitting energy resources. Multiscale materials modeling, grounded in the ability to predict new properties from first-principles atomic calculations, can help us solve these challenges. In my group, we are searching for more active catalysts for fuel cells, more radiation-resistant materials for nuclear reactors, and more stable electrodes for lithium batteries, all of which can help drive new energy technologies.

We are also developing tools to automate steps that can accelerate materials research. For example, I am working with Associate Professor Paul Voyles to integrate optimization methods, atomistic modeling and experimental data to enable the computer to automatically extract the complex structures of amorphous metals. This type of automation will enable researchers to solve materials science problems faster, thereby increasing the rate of development of new materials technologies.

Tell us about what you find promising about the federal Materials Genome Initiative.
The MGI is committed to enabling modern computation to dramatically accelerate development of new materials for advanced technologies. Perhaps not surprisingly, given my area of research, this focus of the MGI is something I very strongly support. In particular, the MGI is pushing people to create the necessary connections among experiments, simulation and data methods to make transformative advances in materials development. Such interdisciplinary integration is technically difficult and requires researchers to step out of their comfort zone, but holds enormous promise.  In fact, the MGI is directly leading to this type of integration on our campus. I am co-director, with Chemical Engineering Professor Tom Kuech, of the Wisconsin Materials Institute (WMI), which was established in 2013 at UW-Madison as part of the university’s role as a partner institution in the MGI. In WMI, we are helping establish relationships among materials researchers, computer scientists, statisticians, mathematicians, bioinformatics experts and others to create integrated approaches to accelerate materials design.

I hope that MGI will help us create a more seamless coupling between experiments, modeling, data and intelligent algorithms. We have not yet arrived at the point where—as on Star Trek—I can just ask my computer to create a new material or device out of thin air. But in the spirit of the MGI, if we can integrate such methods as first-principles design, data mining and optimization algorithms, robot controlled experiments, and 3-D printing, perhaps we are not as far from Star Trek as we might think.

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