Nav: Home

Army researcher uses math to uncover new chemistry

April 26, 2018

ABERDEEN PROVING GROUND, Md. (April 25, 2018) -- In the future, materials scientists will use advanced software to specify the properties they desire and a program will deliver a choice of optimized chemical compounds.

Dr. B. Christopher Rinderspacher, a theoretical chemist with the U.S. Army Research Laboratory, recently published a paper describing the process of using mathematics to design chemical compounds by reducing complexity and taking advantage of machine learning.

"What this does is actually open up the potential number of compounds," Rinderspacher said.

The search for chemical compounds with particularly useful properties is like finding a needle in a haystack, he said. In the past, chemists would search based on an established framework and often find new combinations in a hit or miss fashion.

"The problem with that is you never find anything that's truly new or surprising because what we want is something that breaks the norm," he said. "If we stay within our own thought patterns -- conventional thought patterns -- we're never going to find breakout materials."

Advances in materials science will result in stronger, lighter armor or equipment for a Soldier of the future. This aligns with Army modernization priorities that seek long-range precision fires, next generation of combat vehicles, future vertical lift platforms and Soldier lethality. Materials science will play a huge role in realizing the Army of the future, officials said.

"Science usually works by walking up to the frontier of what we know and poking around," he said. "Where do we find something new and interesting?"

By introducing a new path to discovery, Rinderspacher hopes to point chemists in the right direction using a mathematical approach. Using what's known as nuclear charge distributions, he developed a general theoretical framework for finding chemical compounds he's looking for.

The conventional path for discovering new chemical compounds is "long and tedious," he said. "If we were to go wherever we wanted, in terms of all the possible chemicals without any restrictions that aren't inherent to your problem, you would be able to access everything."

The key, he said, is coming up with a way to optimize what's known as "probability density functions in chemical space."

In the next three to five years, Rinderspacher said he hopes to incorporate machine learning with his algorithms to deliver a solution and narrow the search parameters for new chemical compounds.

The Journal of Mathematical Chemistry, known for its "original, chemically important mathematical results" using non-routine mathematical methodologies, published Rinderspacher's paper.

Rinderspacher has been pursuing this line of research since January 2009. That's when he came to the lab as a post-doctoral fellow after earning his doctorate at the University of Georgia. A self-proclaimed puzzle solver, he said he is driven by finding efficient solutions.

"I know that some people are really driven by the application that will be at the end, but to me getting it to work is fascinating enough," he said. "I like to look at the problem and then figure out, 'How many other problems are like that and can be solved the same way?'"

The activity of math is thinking about generalizing stuff, organizing ideas and showing what does and doesn't work, he said.

"The right math will get you there," he said. "It's mathematical thinking -- outside of the box -- that I'm trying to enable."

Read the paper at

U.S. Army Research Laboratory

Related Math Articles:

Smokers good at math are more likely to want to quit
For smokers who are better at math, the decision to quit just adds up, a new study suggests.
Not a 'math person'? You may be better at learning to code than you think
New research from the University of Washington finds that a natural aptitude for learning languages is a stronger predictor of learning to program than basic math knowledge.
Speak math, not code
Writing algorithms in mathematics rather than code is not only more elegant but also more efficient, says 2013 Turing Award winner Leslie Lamport.
Math that feels good
Mathematics and science Braille textbooks are expensive and require an enormous effort to produce -- until now.
Using math to blend musical notes seamlessly
MIT researchers have invented an algorithm that produces a real-time portamento effect, gliding a note at one pitch into a note of another pitch, between any two audio signals, such as a piano note gliding into a human voice.
Novel math could bring machine learning to the next level
In recent years, a theory called 'Topological Data Analysis,' stemmed from a branch of Mathematics so abstract that it did not seem to have any application whatsoever in the real world, has been making computers much better at recognizing meaningful structure inside all kinds of large datasets (a.k.a.
Study shows we like our math like we like our art: Beautiful
A beautiful landscape painting, a beautiful piano sonata -- art and music are almost exclusively described in terms of aesthetics, but what about math?
Phase transitions: The math behind the music
Physics Professor Jesse Berezovsky contends that until now, much of the thinking about math and music has been a top-down approach, applying mathematical ideas to existing musical compositions as a way of understanding already existing music.
IQ a better predictor of adult economic success than math
IQ in childhood is a better indicator of adult wealth than math for very preterm and very low-weight babies, according to a new study in PLOS ONE.
Math + good posture = better scores
A San Francisco State University study finding that students perform better at math while sitting with good posture could have implications for other kinds of performance under pressure.
More Math News and Math Current Events

Trending Science News

Current Coronavirus (COVID-19) News

Top Science Podcasts

We have hand picked the top science podcasts of 2020.
Now Playing: TED Radio Hour

Warped Reality
False information on the internet makes it harder and harder to know what's true, and the consequences have been devastating. This hour, TED speakers explore ideas around technology and deception. Guests include law professor Danielle Citron, journalist Andrew Marantz, and computer scientist Joy Buolamwini.
Now Playing: Science for the People

#576 Science Communication in Creative Places
When you think of science communication, you might think of TED talks or museum talks or video talks, or... people giving lectures. It's a lot of people talking. But there's more to sci comm than that. This week host Bethany Brookshire talks to three people who have looked at science communication in places you might not expect it. We'll speak with Mauna Dasari, a graduate student at Notre Dame, about making mammals into a March Madness match. We'll talk with Sarah Garner, director of the Pathologists Assistant Program at Tulane University School of Medicine, who takes pathology instruction out of...
Now Playing: Radiolab

What If?
There's plenty of speculation about what Donald Trump might do in the wake of the election. Would he dispute the results if he loses? Would he simply refuse to leave office, or even try to use the military to maintain control? Last summer, Rosa Brooks got together a team of experts and political operatives from both sides of the aisle to ask a slightly different question. Rather than arguing about whether he'd do those things, they dug into what exactly would happen if he did. Part war game part choose your own adventure, Rosa's Transition Integrity Project doesn't give us any predictions, and it isn't a referendum on Trump. Instead, it's a deeply illuminating stress test on our laws, our institutions, and on the commitment to democracy written into the constitution. This episode was reported by Bethel Habte, with help from Tracie Hunte, and produced by Bethel Habte. Jeremy Bloom provided original music. Support Radiolab by becoming a member today at     You can read The Transition Integrity Project's report here.