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Pages 58-60

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From page 58...
... In this case the approach the team used was to apply density functional theory (DFT) to compute the con figurations of the materials of interest, then calculate the X-ray absorption and emission spectra using various first principles and multiplet-based approaches and codes, and then use those spectra to train a machine learning model to move backward from the spectra to the atomic structure.
From page 59...
... It works by taking whatever characterization data are available, generating possible atomic structures with machine learning, then simulating the experimental signals from those candidate atomic structures and looking for a match. DFT is used to constrain the solutions to those that are physi cally reasonable.
From page 60...
... The first four of these challenges relate to the ultra-high-temperature regime, that is, above around 2,000°C, while for the last two, even data are lower temperatures are important. After a brief overview of the state of the art in situ characterization of materials in extreme environments, Misture raised the question of what researchers should do with their data once they have collected information from various sources about a material at extreme conditions.


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