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MatDGL 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.

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MatDGL(Material Deep Graph Learning)

MatDGL is a neural network package that allows researchers to train custom models for material 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

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

Installation

MatDGL can be installed easily through anaconda! As follows:

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

    conda create -n matdgl python=3.8
    conda activate matdgl

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

  • Configure dependencies of matdgl:

    conda install -c conda-forge tensorflow-gpu
  • 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 matdgl:

    pip install matdgl

Usage

Quick start

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

  • download test data
    Get test datas from https://github.com/huzongxiang/MatDGL/tree/main/datas/
    There are four json files in datas: dataset_classification.json, dataset_multiclassification.json, dataset_regression.json
    and dataset_pretrain.json.
  • prepare workdir
    Download datas and put it in your trainning work directory, test.py file should also be put in the directory
     workdir
     │   test.py
     |
     └───datas
     	│   dataset_classification.json
     	│   dataset_multiclassification.json
     	│   dataset_regression.json
     	│   dataset_pretrain.json
    
  • 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 matdgl 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 matdgl.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 matdgl.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. MatDGL provides a series of mainstream models as your need.

     from matdgl.models import GNN
     from matdgl.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 matdgl.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 matdgl
    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 matdgl.models import GNN
     from matdgl.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 matdgl.layers import MessagePassing
     from matdgl.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 matdgl.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 matdgl.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

MatDGL

Implemented-models

We list currently supported GNN models:

Contributors

Zongxiang Hu

References

Contact

Please contact me if you have any questions.
Mail: huzongxiang@yahoo.com
Wechat: voodoozx2015

About

MatDGL 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.

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