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## Abstract ## Abstract
Sean D. Griesemer<sup>a</sup>, Cheol Woo Park<sup>b</sup>, Logan Ward<sup>c</sup>, Chris Wolverton<sup>a</sup> <br> **Sean D. Griesemer<sup>a</sup>, Cheol Woo Park<sup>b</sup>, Logan Ward<sup>c</sup>, Chris Wolverton<sup>a</sup> <br>**
<sup>a</sup> Northwestern University, Evanston IL 60208 <br> _<sup>a</sup> Northwestern University, Evanston IL 60208 <br>_
<sup>b</sup> Lawrence Livermore National Laboratory, Livermore, CA 94550 <br> _<sup>b</sup> Lawrence Livermore National Laboratory, Livermore, CA 94550 <br>_
<sup>c</sup> Argonne National Laboratory, Lemont, IL 60439 <br> _<sup>c</sup> Argonne National Laboratory, Lemont, IL 60439 <br>_
The computational search for new stable inorganic compounds is faster now than ever before, thanks to the automation of high-throughput density functional theory (DFT). However, stable compound searches remain highly expensive, due to the combinatorically explosive number of hypothetical compounds to consider. To guide the search towards the most likely stable compounds, one must have methods to (a) solve crystal structures from diffraction data where experimental data exists (“structure solution”) and (b) predict unknown crystal structures where no experimental data exists (“structure prediction”). We illustrate methods to tackle both of these challenges: The computational search for new stable inorganic compounds is faster now than ever before, thanks to the automation of high-throughput density functional theory (DFT). However, stable compound searches remain highly expensive, due to the combinatorically explosive number of hypothetical compounds to consider. To guide the search towards the most likely stable compounds, one must have methods to (a) solve crystal structures from diffraction data where experimental data exists (“structure solution”) and (b) predict unknown crystal structures where no experimental data exists (“structure prediction”). We illustrate methods to tackle both of these challenges:
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