A machine learning model has been developed that makes optical spectroscopy data easier and quicker to interpret. Researchers from Rice University (TX, USA) have developed a new machine learning ...
image: Researchers at the Institute of Industrial Science, The University of Tokyo, use artificial intelligence to help interpret data generated by material science spectroscopy experiments, which can ...
A method to interpret artificial intelligence (AI) models used in materials discovery by analyzing their learned features has been developed by researchers from Japan. The method extracts key features ...
Manufacturing better batteries, faster electronics, and more effective pharmaceuticals depends on the discovery of new materials and the verification of their quality. Artificial intelligence is ...
This webinar will discuss advanced techniques in NMR spectroscopy, providing descriptions of one-dimensional and two-dimensional NMR experiment types and data interpretation techniques, with examples ...
The proposed method combines a graph neural network with hierarchical clustering to extract key features linking crystal structure to optical spectra, and then groups materials with similar structural ...
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