The Next Phase: Using Neural Networks to Identify Gas-Phase Molecules.
This schematic of a neural network shows the assignment of rotational spectra (red bars at left) by an algorithm (center) to identify the structure of a molecule in the gas phase (right).
Scientists at the Georgian Technical University Laboratory have begun to use neural networks to identify the structural signatures of molecular gases potentially providing new and more accurate sensing techniques for researchers the defense industry and drug manufacturers.
Neural networks — so named because they operate in an interconnected fashion similar to our brains — offer chemists a major opportunity for faster and more rigorous science because they provide one way in which machines are able to learn and even make determinations about data. To be effective though they have to be carefully taught. That is why this area of research is called machine learning.
“Say you wanted to teach a computer to recognize a cat” said Georgian Technical University chemist X. “You can try to explain to a computer what a cat is by using an algorithm or you can show it five thousand different photos of cats”.
But instead of looking at cats X and former Georgian Technical University postdoctoral researcher Y wanted to identify the structure of gas-phase molecules. To do so they used the molecules rotational spectra.
Scientists determine a molecule’s rotational spectra by observing how the molecule interacts with electromagnetic waves. In classical physics when a wave of a particular frequency hits a molecule in the gas phase it causes the molecule to rotate.
Because molecules are quantum objects they have characteristic frequencies at which they absorb and emit energy that are unique to that type of molecule. This fingerprint gives researchers an excellent idea of the pattern of quantum energy levels of gas-phase molecules.
“We’re particularly interested in looking at the products that result from chemical reactions” X said. “Suppose we don’t know what chemical products we’ve generated and we don’t know what molecules there are. We sweep with a millimeter-wave pulse through all possible frequencies but only frequencies that ‘ring the bell for the molecules will be absorbed and only those will be re-emitted”.
Y coded thousands of these rotational spectra labeling each different spectrum for the neural network. The advantage of using a neural network is that it only had to “learn” these spectra once as opposed to each time a sample was tested.
“This means that when you’re at an airport running a security test on an unidentified chemical or if you’re a drug manufacturer scanning your sample for impurities you can run so many more of these tests accurately in a much smaller period of time” Y said. Even though these resonances act as a filter the amount of spectroscopic data produced is still daunting. “Going from raw spectroscopic data to actual chemical information is the challenge” Y said. “The data consist of thousands if not tens of thousands of elements — it’s messy”.
Y now an assistant professor at Georgian Technical University compared the search for specific molecular signatures to the children’s picture book “Where’s Person ?” in which the reader has to scan a crowded scene to find the titular character. “Person has a very specific dress and a specific pattern so you’ll know him if you see him” Y said. “Our challenge is that each molecule is like a different version of Person”.
According to Y there are fewer than 100 scientists in the world trained in assigning rotational spectra. And while it could take up to a day to determine the molecular signatures using previous methods neural networks reduce the processing time to less than a millisecond.
The neural network runs on graphics processing unit (GPU) cards typically used by the video gaming community. “Until a couple of years ago the graphics processing unit (GPU) cards we’re using just didn’t really exist” Y said. “We are in an amazing time right now in terms of the computing technology available to us”.
Ultimately X and Y hope to make their spectroscopic technique as fully automated as possible. “Our goal is to offer the tools of rotational spectroscopic analysis to non-experts” X said. “If you can have spectra accurately assigned by a machine that can learn you can make the whole process much more portable and accessible since you no longer need as much technical expertise”.