GenStrIde

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Contributors: Ryan DeFever, Colin Targonski, Steven Hall

Sarupria Research Group, Smith Research Group

Clemson University

2019 Jun 22

PLEASE CITE OUR WORK IF YOU USE THIS IN YOUR RESEARCH

Chem. Sci., 2019,10, 7503-7515 https://doi.org/10.1039/C9SC02097G

REQUIREMENTS:

• Python 3 [tested with 3.6.6]
• Tensorflow [tested with 1.12.0]
• Cython [tested with 0.29.3]
• MDAnalysis (only required for reading .gro/.xtc files) [tested with 0.19.0]

OVERVIEW:

scripts/ --> Example scripts for training and evaluation
models/ --> PointNet model in tensorflow
utils/ --> Definition of datacontainer for PointNet
datasets/ --> Example training data and example trajectory
mda_custom/ --> Custom edits of MDAnalysis neighbor search

RELATED WORK:

This software would not be possible without the work of previous researchers. The neighbor search functionality is adapted from MDAnalysis (https://mdanalysis.org). The PointNet architecture is taken from arXiv:1612.00593. Also see http://stanford.edu/~rqi/pointnet/ and https://github.com/charlesq34/pointnet.

COMPILING CUSTOM MDANALYSIS NEIGHBOR SEARCH:

cd mda_custom/nsgrid/
python setup.py build_ext --inplace

EXAMPLES:

TRAINING POINTNET:

python scripts/train.py --dataset datasets/lj-r2.0_scaled_shuffled_equal_samples.npy --labels datasets/lj-r2.0_scaled_shuffled_equal_labels.npy --weights PATH_TO_SAVE_WEIGHTS/weights

Comments:

• See prepare_train.py for example of how samples and labels .npy files were prepared
• WARNING, running train.py takes ~4 hours on one NVIDIA V100 GPU

USING POINTNET TO CLASSIFY CRYSTAL STRUCTURES:

python scripts/mda_cluster.py --weights PATH_TO_SAVE_WEIGHTS/weights --nclass 4 --trjpath datasets/lj-seed --cutoff 2.0 --maxneigh 43 --outname example

Comments:

• nclass = 4, the network was trained to recognize four classes (liq,fcc,hcp,bcc)
• maxneigh = 43, each point cloud has 43 points in training set. The number of points in
  each point cloud affects the network architecture.
• cutoff = 2.0, same as used for training

GENERAL PROCEDURE FOR CRYSTAL STRUCTURE IDENTIFICATION:

• Run simulations of pure phases at a range of (T,P) conditions
• Pick a cutoff distance (targeting avg of ~30--50 points appears to give accurate classification)
• Calculate mean and stdev number of points within cutoff distance for all phases
• Choose the max number of points in each point cloud (mu+2*stdev seems to work well)
• Extract training examples from pure phase simulations -- create .npy structures (see prepare_train.py for an example) with training point clouds and labels in the SAME ORDER
  (a) Shape of numpy array for training point clouds should be [NEXAMPLES,NPOINTS,3]
  (b) Labels are one-hot encoded: Shape of numpy array for training labels should be [NEXAMPLES,NCLASSES]
  (c) Central atom for point clouds always translated to (0,0,0)
  (d) Points in point cloud scaled such that closest point to central atom is at a distance of 1.0
• Train PointNet (see train.py)
• Use PointNet for classification (see mda_cluster.py)