crysnet 0.2.8

Labelled Graph Networks for machine learning of crystal.

CrysNet

GrysNet is a neural network package that allows researchers to train custom models for crystal modeling tasks. It aims to accelerate the research and application of material science. It provides user a series of state-of-the-art models and supports user's innovative researches.

Table of Contents

• Hightlights
• Installation
• Usage
• Framework
• Implemented-models
• Contributors
• References
• Contact

Hightlights

• Easy to installation.
• Three steps to fast testing.
• Flexible and adaptive to user's trainning task.

Installation

CrysNet can be installed easily through anaconda! As follows:

• Create a new conda environment named "crysnet" by command, then activate environment "crysnet":

  conda create -n crysnet python=3.8  
  conda activate crysnet 
  

  It's necessary to create a new conda environment to aviod bugs causing by version conflict.

• Configure dependencies of crysnet:

  conda install -c conda-forge tensorflow-gpu==2.6.0
  

• Install pymatgen:

  conda install --channel conda-forge pymatgen  
  

• Install other dependencies:

  conda install --channel conda-forge mendeleev  
  conda install --channel conda-forge graphviz  
  conda install --channel conda-forge pydot  
  conda install --channel conda-forge sklearn
  

• Install crysnet:

  pip install crysnet  
  

Usage

Quick start

CrysNet is very easy to use!
Just three steps can finish a fast test using crysnet:

• download test data
  Get test datas from https://github.com/huzongxiang/CrystalNetwork/tree/main/datas/
  There are three json files in datas: dataset_classification.json, dataset_multiclassification.json and dataset_regression.json.
• prepare workdir
  Download datas and put it in your trainning work directory, test.py file should also be put in the directory
• run command
  run command:

   python test.py  
  

You have finished your testing multi-classification trainning! The trainning results and model weight could be saved in /results and /models, respectively.

Understanding trainning script

You can use crysnet by provided trainning scripts in user_easy_trainscript only, but understanding script will help you custom your trainning task!

• get datas
  Get current work directory of running trainning script, the script will read datas from 'workdir/datas/' , then saves results and models to 'workdir/results/' and 'workdir/models/'

   from pathlib import Path
   ModulePath = Path(__file__).parent.absolute() # workdir
  

• fed trainning datas
  Module Dataset will read data from 'ModulePath/datas/dataset.json', 'task_type' defines regression/classification/multi-classification, 'data_path' gets path of trainning datas.

   from crysnet.data import Dataset
   dataset = Dataset(task_type='multiclassfication', data_path=ModulePath)
  

• generator
  Module GraphGenerator feds datas into model during trainning. The Module splits datas into train, valid, test sets, and transform structures data into labelled graphs and gets three generators. BATCH_SIZE is batch size during trainning, DATA_SIZE defines number of datas your used in entire datas, CUTOFF is cutoff of graph edges in crystal.

   from crysnet.data.generator import GraphGenerator
   BATCH_SIZE = 128
   DATA_SIZE = None
   CUTOFF = 2.5
   Generators = GraphGenerator(dataset, data_size=DATA_SIZE, batch_size=BATCH_SIZE, cutoff=CUTOFF)
   train_data = Generators.train_generator
   valid_data = Generators.valid_generator
   test_data = Generators.test_generator
  
   #if task is multiclassfication, should define variable multiclassifiction
   multiclassification = Generators.multiclassification  
  

• building model
  Module GNN defines a trainning framework that accepts a series of models. Crysnet provides a series of mainstream models as your need.

   from crysnet.models import GNN
   from crysnet.models.gnnmodel import MpnnBaseModel, TransformerBaseModel, CgcnnModel, GraphAttentionModel
  
   gnn = GNN(model=MpnnBaseModel,
   	atom_dim=16
   	bond_dim=64
   	num_atom=118
   	state_dim=16
   	sp_dim=230
   	units=32
   	edge_steps=1
   	message_steps=1
   	transform_steps=1
   	num_attention_heads=8
   	dense_units=64
   	output_dim=64
   	readout_units=64
   	dropout=0.0
   	reg0=0.00
   	reg1=0.00
   	reg2=0.00
   	reg3=0.00
   	reg_rec=0.00
   	batch_size=BATCH_SIZE
   	spherical_harmonics=True
   	regression=dataset.regression
   	optimizer = 'Adam'
   	)
  

• trainning
  Using trainning function of model to train. Common trainning parameters can be defined, workdir is current directory of trainning script, it saves results of model during trainning. If test_data exists, model will predict on test_data.

   gnn.train(train_data, valid_data, test_data, epochs=700, lr=3e-3, warm_up=True, load_weights=False, verbose=1, checkpoints=None, save_weights_only=True, workdir=ModulePath)
  

• prediction
  The simplest method for predicting is using script predict.py in /user_easy_train_scripts.
  Using predict_data funciton to predict.

   gnn.predict_datas(test_data, workdir=ModulePath)    # predict on test datas with labels
   y_pred_keras = gnn.predict(datas)                   # predict on new datas without labels
  

• preparing your custom datas
  If you have your structures (and labels), the Dataset receives pymatgen.core.Structure type. So you should transform your POSCAR or cif to pymatgen.core.Structure type.

   import os
   from pymatgen.core.structure import Structure
   structures = []                                      # your structure list
   for cif in os.listdir(cif_path):
   	structures.append(Structure.from_file(cif))    # for POSCAR too
  
   # construct your dataset
   from crysnet.data import Dataset
   dataset = Dataset(task_type='my_classification', data_path=ModulePath)  # task_type could be my_regression, my_classification, my_multiclassification
   dataset.prepare_x(structures)
   dataset.prepare_y(labels)   # if you have labels used to trainning model, labels could be None in prediction on new datas without labels
  
   # alternatively, you can construct dataset as follow
   dataset.structures = structures
   dataset.labels = labels
  
   # save your structures and labels to dataset in dataset_my*.json
   dataset.save_datasets(strurtures, labels)
  
   # for prediction on new datas without labels, Generators has not attribute multiclassification, should assign definite value
   Generators = GraphGenerator(dataset, data_size=DATA_SIZE, batch_size=BATCH_SIZE, cutoff=CUTOFF)     # dataset.labels is None
   Generators.multiclassification = 5
   multiclassification = Generators.multiclassification  # multiclassification = 5
  

• models provided by crysnet
  We provide GraphModel, MpnnBaseModel, TransformerBaseModel, MpnnModel, TransformerModel, DirectionalMpnnModel, DirectionalTransformerModel and CGCNN model according to your demends. TransformerModel, GraphModel and MpnnModel are different models. TransformerModel is a graph transformer. MpnnModel is a massege passing neural network. GraphModel is a combination of TransformerModel and MpnnModel. MpnnBaseModel and TransformerBaseModel don't take directional informations of crystal into count so them run faster. MpnnBaseModel is the fastest model but accuracy is enough for most tasks. TransformerModel can achieve the hightest accuracy in most tasks. The CGCNN model is the crystal graph convolution neural network model. The GraphAttentionModel is the graph attention neural network.

   from crysnet.models import GNN
   from crysnet.models.gnnmodel import MpnnBaseModel, TransformerBaseModel , DirectionalMpnnModel, DirectionalTransformerModel, MpnnModel, TransformerModel, GraphModel, CgcnnModel, GraphAttentionModel
  

• custom your model and trainning
  The Module GNN provides a flexible trainning framework to accept tensorflow.keras.models.Model type customized by user. Yon can custom your model and train the model according to the following example.

   from tensorflow.keras.models import Model
   from tensorflow.keras import layers
   from crysnet.layers import MessagePassing
   from crysnet.layers import PartitionPadding
  
   def MyModel(
   	bond_dim,
   	atom_dim=16,
   	num_atom=118,
   	state_dim=16,
   	sp_dim=230,
   	units=32,
   	message_steps=1,
   	readout_units=64,
   	batch_size=16,
   	):
   	atom_features = layers.Input((), dtype="int32", name="atom_features_input")
   	atom_features_ = layers.Embedding(num_atom, atom_dim, dtype="float32", name="atom_features")(atom_features)
   	bond_features = layers.Input((bond_dim), dtype="float32", name="bond_features")
   	local_env = layers.Input((6), dtype="float32", name="local_env")
   	state_attrs = layers.Input((), dtype="int32", name="state_attrs_input")   
   	state_attrs_ = layers.Embedding(sp_dim, state_dim, dtype="float32", name="state_attrs")(state_attrs)
  
   	pair_indices = layers.Input((2), dtype="int32", name="pair_indices")
  
   	atom_graph_indices = layers.Input(
   	(), dtype="int32", name="atom_graph_indices"
   	)
  
   	bond_graph_indices = layers.Input(
   	(), dtype="int32", name="bond_graph_indices"
   	)
  
   	pair_indices_per_graph = layers.Input((2), dtype="int32", name="pair_indices_per_graph")
  
   	x = MessagePassing(message_steps)(
   	[atom_features_, edge_features, state_attrs_, pair_indices,
   		atom_graph_indices, bond_graph_indices]
   	)
  
   	x = PartitionPadding(batch_size)([x[0], atom_graph_indices])
   	x = layers.BatchNormalization()(x)
   	x = layers.GlobalAveragePooling1D()(x)
   	x = layers.Dense(readout_units, activation="relu", name='readout0')(x)
   	x = layers.Dense(1, activation="sigmoid", name='final')(x)
  
   	model = Model(
   	inputs=[atom_features, bond_features, local_env, state_attrs, pair_indices, atom_graph_indices,
   				bond_graph_indices, pair_indices_per_graph],
   	outputs=[x],
   	)
   	return model
  
   from crysnet.models import GNN
   gnn = GNN(model=MyModel,     
   	atom_dim=16,
   	bond_dim=64,
   	num_atom=118,
   	state_dim=16,
   	sp_dim=230,
   	units=32,
   	message_steps=1,
   	readout_units=64,
   	batch_size=16,
   	optimizer='Adam',
   	regression=False,
   	multiclassification=None,)
   gnn.train(train_data, valid_data, test_data, epochs=700, lr=3e-3, warm_up=True, load_weights=False, verbose=1, checkpoints=None, save_weights_only=True, workdir=ModulePath)  
  

  You can set edge as your model output.

   from crysnet.layers import EdgeMessagePassing
   def MyModel(
   	bond_dim,
   	atom_dim=16,
   	num_atom=118,
   	state_dim=16,
   	sp_dim=230,
   	units=32,
   	message_steps=1,
   	readout_units=64,
   	batch_size=16,
   	):
   	atom_features = layers.Input((), dtype="int32", name="atom_features_input")
   	atom_features_ = layers.Embedding(num_atom, atom_dim, dtype="float32", name="atom_features")(atom_features)
   	bond_features = layers.Input((bond_dim), dtype="float32", name="bond_features")
   	local_env = layers.Input((6), dtype="float32", name="local_env")
   	state_attrs = layers.Input((), dtype="int32", name="state_attrs_input")   
   	state_attrs_ = layers.Embedding(sp_dim, state_dim, dtype="float32", name="state_attrs")(state_attrs)
  
   	pair_indices = layers.Input((2), dtype="int32", name="pair_indices")
  
   	atom_graph_indices = layers.Input(
   	(), dtype="int32", name="atom_graph_indices"
   	)
  
   	bond_graph_indices = layers.Input(
   	(), dtype="int32", name="bond_graph_indices"
   	)
  
   	pair_indices_per_graph = layers.Input((2), dtype="int32", name="pair_indices_per_graph")
  
   	x = EdgeMessagePassing(units,
   				edge_steps,
   				kernel_regularizer=l2(reg0),
   				sph=spherical_harmonics
   				)([bond_features, local_env, pair_indices])
  
   	x = PartitionPadding(batch_size)([x[1], bond_graph_indices])
   	x = layers.BatchNormalization()(x)
   	x = layers.GlobalAveragePooling1D()(x)
   	x = layers.Dense(readout_units, activation="relu", name='readout0')(x)
   	x = layers.Dense(readout_units//2, activation="relu", name='readout1')(x)
   	x = layers.Dense(1, name='final')(x)
  
   	model = Model(
   	inputs=[atom_features, bond_features, local_env, state_attrs, pair_indices, atom_graph_indices,
   				bond_graph_indices, pair_indices_per_graph],
   	outputs=[x],
   	)
   	return model
  

  The Module GNN has some basic parameter necessary to be defined but not necessary to be used:

   class GNN:
       def __init__(self,
   	model: Model,
   	atom_dim=16,
   	bond_dim=32,
   	num_atom=118,
   	state_dim=16,
   	sp_dim=230,
   	batch_size=16,
   	regression=True,
   	optimizer = 'Adam',
   	multiclassification=None,
   	**kwargs,
   	):
   	"""
   	pass
   	"""  
  

Framework

CrysNet

Implemented-models

We list currently supported GNN models:

• GCN from Kipf and Welling: Semi-Supervised Classification with Graph Convolutional Networks (ICLR 2017)
• GAT from Veličković et al.: Graph Attention Networks (ICLR 2018)
• GN from Battaglia et al.: Relational inductive biases, deep learning, and graph networks
• Transformer from Vaswani et al.: Attention Is All You Need (NIPS 2017)

Contributors

Zongxiang Hu

References

Contact

Please contact me if you have any questions.
Mail: [e-mail]
Wechat: voodoozx2015