Scientists at the U.S. Department of Energy's Argonne National Laboratory and the U.K.'s University of Cambridge have developed a method that generates automatic databases using artificial intelligence and high-performance computing (HPC). The technique can assemble databases via natural language processing and HPC.
The researchers describe their work in "Comparative Dataset of Experimental and Computational Attributes of UV/vis Absorption Spectra," published in Scientific Data.
The team built a database on both material structures and material properties, using the NLP ChemDataExtractor data-mining application. "It's probably the first such compilation of a database on such a massive scale, with 5,380 like-for-like pairs of experimental and calculated data," says Jacqueline Cole at the University of Cambridge. "And because it's such a large amount, it serves as a repository in its own right and really opens the door to predicting new materials."
From Argonne National Laboratory
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