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

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

Work Description

Title: Predicting Crystal Structures using Digital Alchemy Inverse Materials Design and the Random Forest Technique of Machine Learning Open Access Deposited

Attribute	Value
Methodology	The data were generated as a part of a model in predicting crystal structures solely from knowledge about the colloidal particles without the need for simulations or experiments. We used the Digital Alchemy inverse materials design approach to find optimal and near-optimal hard, convex, colloidal, polyhedral shapes for 13 target structures. To construct the predictive model we performed Alchemical Monte Carlo (Alch-MC) simulations on the target structures. We placed a minimum of N = 100 particles in a periodic simulation box, with the exact number chosen to be a multiple of the number of particles in the unit cell of one of the 13 target structures. Particle shapes were initialized with as many as 64 vertices randomly generated to create a convex shape. Monte Carlo (MC) sweeps were performed to allow particle translations, rotations, and shape moves via vertex re-location.
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.
Creator	Geng, Yina Van Anders, Greg Glotzer, Sharon C.
Depositor	yinageng@umich.edu
Contact information	yinageng@umich.edu
Discipline	Science
Funding agency	Other Funding Agency
Keyword	Inverse Design Machine Learning
Resource type	Dataset
Last modified	10/04/2019
Published	01/28/2018
DOI	https://doi.org/10.7302/Z2T72FN9
License	http://creativecommons.org/licenses/by/4.0/

To Cite this Work:
Geng, Y., Van Anders, G., Glotzer, S. C. (2018). Predicting Crystal Structures using Digital Alchemy Inverse Materials Design and the Random Forest Technique of Machine Learning [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/Z2T72FN9

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Files (Count: 3; Size: 1.58 GB)

Title	Original Upload	Last Modified	File Size	Access	Actions
ReadMe_Geng_CrystalStructures.pdf	2018-01-28	2018-01-28	142 KB	Open Access	View Details Download
openDataForest.zip	2018-01-02	2018-01-02	1.58 GB	Open Access	View Details Download
getShapeVerticesFromPos.py	2018-01-28	2018-01-28	1 KB	Open Access	View Details Download

#given a pos file name, return the particle vertices array in all frames
import numpy as np

def getShapeVerticesFromPos(filename):
#particle shape vertices
allVerts = []

with open(filename,'r') as f: #With syntax for using the open command deletes it when scope ends
for line in f:
#line with 'poly3d' has shape vertices information
if 'poly3d' in line:
temp = line.replace('"','');
temp = temp.split()
#number of vertices
N_vertices = int(temp[3]);
#one shape vertices
verts = np.zeros(N_vertices*3, dtype=float);
for i in range(N_vertices*3):
verts[i] = float(temp[i+4]);
#append the shape to result
allVerts.append(verts)

allVerts = np.array(allVerts, float)
return allVerts

#filename = "diamond_seed1_k0.00_phi0.60_N128_V32.pos"
#allVerts = getShapeVerticesFromPos(filename)

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