annual report 2011 - Office for Research - Northwestern University

Bryce Meredig
McCormick School of Engineering
and Applied Science
Using Algorithms to Organize Materials
As a computational materials scientist, Bryce Meredig, in
the research group of Chris Wolverton, materials science
and engineering, uses computer algorithms to study and
predict the properties of many materials—from alloys to
solar cells—much as a meteorologist forecasts the weather.
In this field today, ever-expanding computational capability
is a double-edged sword. With access to
faster computers, more potentially useful
materials data can be generated than ever
before. Unfortunately, finding meaning
in those data becomes more difficult the
more they accumulate. In other words, we
are drowning in information.
Fortunately, computer scientists have
already been grappling with the problem
of data overload for some time. As people
in other fields seek patterns in massive
databases—the stock market, Twitter
posts, and Google search terms, for
example—their innovations can benefit
computational materials, as Meredig
describes in the following example.
Cubic zirconia, the faux-diamond
gemstone, has a flawed reputation. Yet it
46 Annual Report 2011 | Excellence in Research
Andrew Campbell
turns out to be an enormously
useful technological material
with applications in automobile
three-way catalysts, fuel
cells, and thermal coatings.
Interestingly, zirconia’s most
valuable properties emerge
when one adds other elements
to it. The question is, what can
we mix with zirconia, and what
will happen when we do?
This mystery is perfect for
a computational materials
scientist to solve, says Meredig.
By inserting much of the
periodic table into zirconia in
a “virtual experiment” on the
computer, he can then examine
the resulting data for trends.
Data on so many di�erent
elements, however, are nearly
impossible to analyze by hand.
Instead, he can leverage data-mining techniques and apply
clustering algorithms to his results.
Clustering methods attempt to group data into mutually like
sets, or clusters. Meredig says that a beautiful thing happens
when he clusters the zirconia data: the trends that emerge
match our chemical intuition. The elements in the middle of
the periodic table, or transition metals, are most similar to each
other when in zirconia, while elements that surround them
form another group. Once he has his clusters, he can consider
each separately and build sensible models for their behavior,
something he considers a triumph in the face of data overload.
The properties of elements added to the material zirconia follow our chemical intuition:
the transition metals (the middle of the periodic table) belong to one similarly behaving
cluster and other elements belong to a second cluster.