New materials lead to new
innovations. Gorilla Glass is a big selling point for smartphones.
Kevlar saves lives and has worked its way into consumer products.
Lithium-ion batteries have enabled a host of energy-storage
applications, from planes to cars to computers. But there's a problem.
Actually creating a new,
game-changing material is a glacially slow process — especially when
compared to the rate at which new products relying on those materials
hit the market. It took just under nine years for the Boeing 787
Dreamliner to go from a concept to commercial flight. The development of
the iPhone began in 2005; the phone was on store shelves by 2007. In
contrast, the creation of new materials moves far more slowly, taking
about 20 years for all of the necessary research and development.
An attempt to gain a deeper understanding of how the elements interact
In an effort to overcome this innovation bottleneck, the White House two years ago announced the Materials Genome Initiative.
The venture aims to halve development time for new materials and slash
the monetary investment required. And if the name sounds familiar, it
should: in the same way the Human Genome Project set out to map the
underlying structure of human genes, the Materials Genome Initiative is
an attempt to gain a deeper understanding of how the elements interact
to give us a diverse set of materials and materials properties. With
that foundation of knowledge, scientists and engineers will hopefully be
able to create new materials tuned to the exact properties needed for a
particular application — and be able to do it much, much faster.
A huge number of atomic
combinations and arrangements may have useful properties. However, most
arrangements won't be useful, or even able to be synthesized. Trying to
explore the vast world of potential materials in a lab would be both
impractical and just plain impossible. So to map out that enormous
number of possible materials, several research groups working on the
Materials Genome Initiative are using computers to model known and
unknown materials. They mine the resulting data to find areas that
deserve a more careful examination.
In the years since its inception, the initiative has brought together several successful ventures. Among them are the Materials Project at MIT and the Harvard Clean Energy Project.
These two projects have similar theoretical underpinnings for different
end goals. MIT's Materials Project is focused on inorganic solids,
especially those for battery materials, while the Clean Energy Project
is examining molecules for solar cell applications. Both are powered by
huge databases that are populated with information gleaned from Density
Functional Theory (DFT) calculations. DFT uses quantum mechanics to
predict many properties of the real, physical substances being modeled.
A dataset of over 100,000 known and theoretical materials
MIT's Materials Project started
about eight years ago, and was catalyzed by the work of Professor
Gerbrand Ceder. As a consultant to several companies, Ceder would screen
a large number of materials for particular applications. But working
with individual companies left the data siloed and locked up. "People
would be able to do really creative things with this if we gave this to
the world, and this became Materials Project," he says. Now, MIT's
dataset consists of over 100,000 known and theoretical materials. To
make sense of the data and design new materials, MIT researchers use a
combination of human intuition and machine learning designed to
understand the laws of chemistry.
Similarly, the Harvard Clean
Energy Project has created a huge database that can be explored by man
and machine for potential solutions to materials problems. The venture
started as a small proof of concept examining potential organic solar
cell materials. Researchers calculated the properties of about 15
compounds to predict how well these new substances might perform in the
real world without having to synthesize them first. These calculations
eventually yielded a new compound with near record-breaking electrical
properties. But that success was just from a few chemicals calculated by
a single graduate student. What could be discovered if you increased
available computing power by distributing the calculations to an army of
volunteers?
Researchers have calculated millions of potential solar cell compounds
Today, the Harvard Clean
Energy Project is doing just that: anyone across the globe can download a
program that performs scientific calculations on their PCs and reports
back the results. With this massive resource at their disposal,
researchers have calculated millions of potential solar cell compounds —
and they're only getting started. "Right now is an interesting time for
the project," says Dr. Hachmann, a research associate involved in the
Harvard project. "We are at the point where we can harvest the fruits of
our hard work and hopefully get some nice results."
Currently, Harvard investigators have released 2.3 million compounds online
for anyone to search through. And while these compounds have been
calculated with solar cells in mind, other scientists who mine the data
will be able to use the information to research other classes of
materials. Similarly, researchers working on MIT's Materials Project
have an online portal for anyone to explore their data.
"You cannot anticipate what people will do with it."
The greater Materials Genome
Initiative aims to reduce costs and time for material development, and
Ceder hopes to see it accomplish that bold goal. In fact, he's already
seen it work: Ceder is in the process of patenting new materials for use
in batteries, a big win for the burgeoning Initiative and the field of
materials discovery. And with heaps of data being shared in online
databases, those successes are likely to keep coming. Ceder hopes
to see the Materials Genome Initiative will lead to big innovations in
materials science in the same, unpredictable way the web transformed
many facets of modern life. "When you make stuff like this available," he says, "you cannot anticipate what people will do with it."
Math is inherently a great precursor to advancement..but man do I hate math so much.
Math is actually really cool. Unlike language or politics, it’s a system where everything makes verifiable sense and behaves in a deterministic way. This is awesome!
The trouble with it is that it builds on itself. If you had a poor teacher in high school and never properly learned algebra, you will have trouble with trigonometry and might as well kiss off anything calculus+ without remediation. If your girlfriend happened to be in that algebra class you’ll probably experience similar results.
Other problems:
The trouble with it is that it builds on itself. If you had a poor teacher in high school and never properly learned algebra, you will have trouble with trigonometry and might as well kiss off anything calculus+ without remediation. If your girlfriend happened to be in that algebra class you’ll probably experience similar results.
Other problems:
- Because it’s deterministic, you do actually have right and wrong answers. In this day and age where everyone gets a medal and nobody can handle criticism, outright wrong answers are verboten. Of course this is a critical part of learning, and you’re going to get it wrong – probably several times – before you get it right. This is no way exclusive to math, it’s just really obvious in math. Result: many people flee to softer non-quantitative fields.
- Dragging all kids through the same exact path is almost definitely the worst way to teach math. There are certainly prerequisites, but there’s a huge field to explore and no need to force people to grind away at things they don’t like. Later they’ll see the uses and come back to learn it. Example: I HATED series expansions in Calc 2, couldn’t understand why they would ever be used. Later on I found out why they’re needed, and went back with actual motivation to learn them right.
There are 22 Comments.
Show speed reading tips and settings