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.

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:

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: huzongxiang@yahoo.com
Wechat: voodoozx2015

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README.md

README.md

MatDGL(Material Deep Graph Learning)

Table of Contents

Hightlights

Installation

Usage

Quick start

Understanding trainning script

Framework

Implemented-models

Contributors

References

Contact

Files

README.md

Latest commit

History

README.md

File metadata and controls

MatDGL(Material Deep Graph Learning)

Table of Contents

Hightlights

Installation

Usage

Quick start

Understanding trainning script

Framework

Implemented-models

Contributors

References

Contact