New publication in AICHE J.

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This paper uses a kernel regression method trained on a large set of DFT calculations from the Materials Project to design new materials. A notable feature of this approach is it opens the door to inverse design, since the mathematical form of the regression is accessible. In the paper we predict electronic properties and elastic constants for a large number of metal oxides. Congratulations Bruno for this work!

See the paper here:

@article {AIC:AIC15251,
  author =       {Calfa, Bruno A. and Kitchin, John R.},
  title =        {Property prediction of crystalline solids from composition and
                  crystal structure},
  journal =      {AIChE Journal},
  issn =         {1547-5905},
  url =          {},
  doi =          {10.1002/aic.15251},
  pages =        {n/a--n/a},
  keywords =     {crystal property prediction, data analytics, kernel
                  regression, crystal composition and structure, exhaustive
                  enumeration algorithm},
  year =         {2016},

Copyright (C) 2016 by John Kitchin. See the License for information about copying.

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