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SPECTROSCOPY


Aalto University is a pioneering centre for AI-based spectroscopy


predictions instantaneously without the need for arduous experiments or calculations,” he says. Although the project is still in its


infancy, Rinke reveals that the team has already built a prototype to demonstrate that it is possible for an AI to infer the spectrum, in this case a photoemission spectrum, from the structure of a material alone – in this example, relating solely to the chemical elements and ‘xyz’ positions of each atom in a molecule.


NEURAL NETWORK In technical terms, Dr Milica Todorović, research fellow at the Computational Electronic Structure Teory (CEST) group at Aalto University, explains that ARTIST is a Python-based neural network code that has been trained on data relating to 100,000 small organic molecules, and which is capable of making spectra predictions of these types of molecules with 97% accuracy. “We have used it to quickly screen a


new data set of 100,000 molecules. To get the spectra for these molecules took a couple of seconds via imaging measuring in a lab. Ten we screened the output for interesting molecules with interesting spectral properties,” she says. However, according to Todorović, the application domain is ‘not limited to small molecules,’ and can be ‘trained on any substance or materials type, provided there is enough training data.’


Patrick Rinke from Aalto University


Although not limited to such areas, the team also envisages that ARTIST could be particularly useful in application areas that require the spectral properties of materials, such as optoelectronics, plasmonics or photochemistry, for example, photocatalysis. “ARTIST is purely software-based and uses deep neural network architectures. In our initial work, we tested three different deep neural network topologies and found that convolutional neural networks, used in image processing, and deep tensor neural networks, used in language processing, work best,” says Todorović. During set-up, ARTIST is trained


on spectroscopy data relating to pairs of molecules and the corresponding spectrum. In the training, the ‘weights’ of the neural network, as well as its hyper-parameters are determined. Once it is trained, ARTIST then receives a molecule or a material as input, in this case the xyz coordinates of the molecular geometry, and makes a prediction of the spectrum


(intensity as a function of energy) instantly. “Training ARTIST is the time consuming and difficult part that requires expert


Lancor Scientific’s device


knowledge. GPU hardware is optimal for training ARTIST. Once it is trained, a laptop or a web server can make the predictions,” adds Rinke.


www.scientistlive.com 35


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