Work Description

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

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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
Depositor
  • yinageng@umich.edu
Contact information
Discipline
Funding agency
  • Other Funding Agency
Keyword
Citations to related material
  • " Yina Geng, Greg van Anders, Sharon C. Glotzer, ""Predicting colloidal crystals from shapes via inverse design and machine learning [pre-print]"" https://arxiv.org/pdf/1801.06219.pdf"
Resource type
Last modified
  • 10/04/2019
Published
  • 01/28/2018
DOI
  • https://doi.org/10.7302/Z2T72FN9
License
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)

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

#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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