Data-Driven Spectroscopy Has Potential to Speed Development

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Artificial Intelligence for Spectroscopy (ARTIST) is a new approach to spectroscopy that uses artificial intelligence (AI) to accelerate the spectroscopic analysis of materials and the discovery of new molecules or materials. ARTIST was developed by researchers at Aalto University and the Technical University of Denmark to help speed the development of new technologies, from wearable electronics to flexible solar panels.

ARTIST AI Spectroscopy approach, Aalto University.
ARTIST, which stands for Artificial Intelligence for Spectroscopy, instantly determines how a molecule will react to light. Courtesy of Jari Järvi/Aalto University.

Through the use of AI, ARTIST can quickly determine the spectra of individual molecules. “Normally, to find the best molecules for devices, we have to combine previous knowledge with some degree of chemical intuition,” said researcher Milica Todorovic. “Checking their individual spectra is then a trial-and-error process that can stretch weeks or months, depending on the number of molecules that might fit the job. Our AI gives you these properties instantly.” The team considers ARTIST to be the first step toward building an AI-spectroscopist to harvest the wealth of already available spectroscopic data.

ARTIST is based on custom-made deep neural networks (DNNs) that learn spectra of organic molecules. The DNNs can predict the peak positions of molecular ionization spectra with an average error as low as 0.19 eV and the spectral weight to within 3 percent. The DNNs infer the spectra directly from the molecular structure and do not require auxiliary input. The researchers said that the predictions made by the DNNs are fast (a few milliseconds for a single molecule), which could facilitate their application to large databases and high-throughput screening.

The multidisciplinary team trained the AI for the system in just a few weeks with a data set of more than 132,000 organic molecules. The researchers then used ARTIST to make predictions for a new data set of organic molecules that was not used in training the DNNs. ARTIST was able to make spectra predictions for 10,000 molecules at no additional computational cost.

“Enormous amounts of spectroscopy information sit in labs around the world,” said professor Patrick Rinke. “We want to keep training ARTIST with further large data sets so that it can one day learn continuously as more and more data comes in.” 

ARTIST has the potential to speed up the development of flexible electronics, including LEDs and paper with screen-like capabilities. As a complementary tool to basic research and characterization, ARTIST could also assist in creating new compounds.

The researchers now hope to expand the capabilities of ARTIST by training it with even more data. They aim to release ARTIST on an open science platform in 2019. It is currently available for use and further training upon request.

The research was published in Advanced Science ( 


Published: February 2019
deep learning
Deep learning is a subset of machine learning that involves the use of artificial neural networks to model and solve complex problems. The term "deep" in deep learning refers to the use of deep neural networks, which are neural networks with multiple layers (deep architectures). These networks, often called deep neural networks or deep neural architectures, have the ability to automatically learn hierarchical representations of data. Key concepts and components of deep learning include: ...
artificial intelligence
The ability of a machine to perform certain complex functions normally associated with human intelligence, such as judgment, pattern recognition, understanding, learning, planning, and problem solving.
Research & TechnologyeducationEuropeAalto UniversityTechnical University of Denmarkdeep learningneural networksdeep neural networksDNNartificial intelligenceAIspectroscopyLight SourcesTest & MeasurementTech PulseBioScan

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