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https://github.com/huzongxiang/CrystalNetwork
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.
https://github.com/huzongxiang/CrystalNetwork
deep-learning graph machine-learning massagepassing materials neural-networks pretrain tensorflow transformer
Last synced: 5 days ago
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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.
- Host: GitHub
- URL: https://github.com/huzongxiang/CrystalNetwork
- Owner: huzongxiang
- License: mit
- Created: 2022-01-06T08:50:33.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-07-30T09:21:28.000Z (4 months ago)
- Last Synced: 2024-10-27T18:21:31.672Z (17 days ago)
- Topics: deep-learning, graph, machine-learning, massagepassing, materials, neural-networks, pretrain, tensorflow, transformer
- Language: Python
- Homepage:
- Size: 54.7 MB
- Stars: 63
- Watchers: 9
- Forks: 12
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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](#hightlights)
* [Installation](#installation)
* [Usage](#usage)
* [Framework](#matdgl-framework)
* [Implemented-models](#implemented-models)
* [Contributors](#contributors)
* [References](#references)
* [Contact](#Contact)
## Hightlights
+ Easy to installation.
+ Three steps to fast testing.
+ Flexible and adaptive to user's trainning task.
MatDGL can be installed easily through anaconda! As follows:
+ Create a new conda environment named "matdgl" by command, then activate environment "matdgl":
```bash
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:
```bash
conda install -c conda-forge tensorflow-gpu
```
+ Install pymatgen:
```bash
conda install --channel conda-forge pymatgen
```
+ Install other dependencies:
```bash
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:
```bash
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:
```bash
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/'
```python
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.
```python
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.
```python
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.
```python
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.
```python
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.
```python
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.
```python
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.
```python
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.
```python
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.
```python
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:
```python
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
"""
```
## Implemented-models
We list currently supported GNN models:
* **GCN** from Kipf and Welling: [Semi-Supervised Classification with Graph Convolutional Networks](https://arxiv.org/abs/1609.02907) (ICLR 2017)
* **GAT** from Veličković *et al.*: [Graph Attention Networks](https://arxiv.org/abs/1710.10903) (ICLR 2018)
* **GN** from Battaglia *et al.*: [Relational inductive biases, deep learning, and graph networks](https://arxiv.org/pdf/1806.01261v1)
* **Transformer** from Vaswani *et al.*: [Attention Is All You Need](https://arxiv.org/pdf/1706.03762) (NIPS 2017)
## Contact
Please contact me if you have any questions.
Mail: [email protected]
Wechat: voodoozx2015