From: Spudboy100@aol.com
Date: Thu Jan 24 2002 - 06:56:03 MST
http://www.sciencedaily.com/releases/2002/01/020123075920.htm
<<...Central to the method are two types of artificial intelligence software:
hybrid neural networks and genetic algorithms. The software mimics the
thought processes of chemists who create new formulas for everything from
rubber compounds to rocket fuels, and plastic materials to snack foods, said
James M. Caruthers, a professor of chemical engineering. "There is this
crazy-haired scientist, called a formulation chemist, who actually mixes dabs
of this with dabs of that and stirs it up in a pot — cooks it, more or less —
and makes new materials," Caruthers said. "You could say, 'Gee, these guys
make this good stuff, and they are lucky.' Except that the people who are
very good at it are lucky again and again and again, and they are actually
some of the most valuable folks in an organization because they make the new
materials.
"It's part science, it's part intuition." The different types of software
work together in a repeating two-phase cycle of discovery. First, hybrid
neural networks analyze the formulas of the numerous catalysts, or other
materials, created by the parallel technique. The neural networks determine
the properties of the materials, based on their chemical structures. In the
second phase, genetic algorithms cull the best materials and eliminate the
poor performers, just like survival of the fittest. The algorithms also
generate "mutations" of the best materials to create even better versions,
and the software determines the chemical structures of those mutations. The
resulting formulas are returned to the neural network software, and the cycle
starts over again, progressively creating better and better materials, said
Venkat Venkatasubramanian, a professor of chemical engineering who has been
working with Caruthers to develop the software for more than a decade.
Caruthers said he observes how formulation chemists come up with new ideas.
Then he models their trains of thought in software programs. "Most experts
don't think in terms of equations and mathematics," he said. "They think in
terms of pictures. They have a picture of what goes in, and they have a
picture of what comes out. What we should really be trying to do is model
this sort of picture-to-picture reasoning that goes on. "I look at eyes. I
try to see when the eyes are excited, or a little confused or upset, and try
to figure out what is the reasoning behind all of that. We want to learn why
experts make the inferences that they do — why they jump from here to here."
The software isn't quite as smart as the human formulation chemists.
While software programs can't match the creative brain power of people, they
can mimic human thinking while simultaneously computing thousands of
formulations, compared to about half a dozen for a human chemist. "No human
mind could keep all these balls in the air at the same time," Caruthers said.
"Our idea is to reproduce 60, 70, 80 percent of the way these formulation
chemists think, but now the computer can balance all of these balls in the
air at the same time." Neural networks are designed to think more like the
human brain than a conventional computer program. The Purdue approach differs
from previous methods that have used neural networks because it first takes a
reaction's physics and chemistry into account, and then it lets the software
take over, determining the properties of the materials. Because it combines
known physics and chemistry with the software, it is called a hybrid neural
network.
"Other methods assume that you know nothing about the physics and the
chemistry of a process," Venkatasubramanian said. "However, in most cases you
know something about the physics and chemistry governing a reaction, but that
knowledge is not complete enough. "We see how far our fundamental
understanding will take us...>>
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