Machine Learning: Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra (Adv. Sci. 9/2019)
Source abstract
Abstract
With artificial intelligence (AI), we learn the relationship between molecular structure and properties. In article number 1801367, Patrick Rinke and co-workers build a deep learning AI spectroscopist that can make predictions for molecular spectra instantly and at no further cost for the end user. AI spectroscopy will greatly accelerate the way in which science is done and aid materials discovery and design.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Chemical Sensor Technologies
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Affiliations
Department of Computer Science Aalto University P.O. Box 15400 Aalto FI‐00076 Finland
Department of Applied Physics Aalto University P.O. Box 11100 Aalto FI‐00076 Finland
Department of Applied Mathematics and Computer Science Technical University of Denmark Richard Petersens Plads, 2800 Kgs. Lyngby Denmark
Chair for Theoretical Chemistry and Catalysis Research Center Technische Universität München Lichtenbergstr. 4, D‐85747 Garching Germany
References
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