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Filtering by: Keyword Inverse Design Discipline Science

Predicting Crystal Structures using Digital Alchemy Inverse Materials Design and the Random Forest Technique of Machine Learning

Creator:

Geng, Yina, Van Anders, Greg, and Glotzer, Sharon C.

Description:

The data are the 13 target structures used in developing our model for predicting colloidal crystal structures from the geometries of particular shapes. The target structures are: simple cubic (SC), body-centered cubic (BCC), face-centered cubic (FCC), simple chiral cubic (SCC), hexagonal (HEX-1-0.6), diamond (D), graphite (G), honeycomb (H), body-centered tetragonal (BCT-1-1-2.4), high-pressure Lithium (Li), Manganese (beta-Mn), Uranium (beta-U), Tungsten (beta-W). At least nine simulations were run on each of the target structures. All of the data are formatted as .pos files.

Keyword:

Inverse Design and Machine Learning

Discipline:

Science