MODNet，用于材料性能预测的最佳描述网络。
modnet的Python项目详细描述
MODNet：材料最优描述网络
目录
Introduction
This repository contains the python package implementing the Material Optimal Descriptor Network (MODNet). It is a supervised machine learning framework for learning material properties from the crystal structure. The framework is well suited for limited datasets and can be used for learning multiple properties together by using joint transfer learning.
This repository also contains two pretrained models that can be used for predicting the refractive index and vibrational thermodynamics for any crystal structure.
See the MODNet paper for more details:
Machine learning materials properties for small datasets, De Breuck et al. (2020), arXiv:2004.14766。在
在
How to install
MODNet can be installed via pip:
^{pr 1}$Usage
The MODNet package is built around two classes: ^{
The usual workflow is as follows:
^{pr 2}$Example notebooks can be found in the example_notebooks directory.
Pretrained models
Two pretrained models are provided in pretrained/:
- Refractive index
- Vibrational thermodynamics
Download this directory localy to path/to/pretrained/. Pretrained models can then be used as follows:
^{pr 3}$Stored MODData
Three ^{
- Refractive index
- Vibrational thermodynamics
- Formation energy on Materials Project (June 2018)
Download this directory localy to path/to/moddata/. These can then be used as follows:
^{pr 4}$The latter MODData (MP_2018.6) is very usefull for predicting a learned property on all structures from the Materials Project:
^{pr 5}$Documentation
The two main classes, ^{
MODData
A ^{
Arguments:
- ^{
}: List of pymatgen Structures. - ^{
}: optional List of targets corresponding to each structure. When learning on multiple targets this is a ndarray where each column corresponds to a target, i.e. of shape (n_materials,n_targets). - ^{
} (optional): Iterable (e.g list) of names corresponding to the properties. E.g. ^{ } or ^{ } for single target learning. These names are used when building the model. - ^{
} (optional): If the list of structures (^{ }) are from the Materials Project, you can specify the corresponding mpids by providing an Iterable of mpids: ^{ }. This will enable fast featurization (see further).
The next step is to create the features:
^{pr 7}$Arguments:
- ^{
} (optional): If set to True, the algorithm will use the pre-computed features from a database instead of computing them again from scratch. This is recommended (and only possible) when using structures from the Materials Project. Note that the mpids should be provided in the MODData. - ^{
} (optional): When setting fast to True, you also need to provide this argument. Download the file at this figshare link，将其解压缩，然后将此文件的本地路径设置为参数。在
最后，计算出最佳特征：
data.feature_selection(n=300)
参数：
n（可选）：要计算的最优特征的数量，即计算n个排名第一的特征。当设置为-1时，将对所有功能进行排名（推荐，但可能需要时间）。在
MODData可以保存
data.save('path/dataname')
并加载以供以后使用：
frommodnet.preprocessingimportMODDatadata=MODData.load('path/dataname')
save和load方法都使用pandas .read_pickle(...)和{".zip"、".tgz"和{
特征、目标和其他数据的数据帧可以通过以下方法访问：
# dataframe containing the structuresdata.get_structure_df()# dataframe containing the targetsdata.get_target_df()# dataframe containing the featuresdata.get_featurized_df()# List of the optimal features, in ranked orderdata.get_optimal_descriptors()# get_featurized_df limited to the best featuresdata.get_optimal_df()
MODNetModel
The model is created by a MODNetModel instance:
^{pr 12}$Arguments:
^{
}: Specifies how the different targets are organized in the architecture. It is a list of lists of lists, representing the three modular last levels: block 2, 3 and 4 (see Figure 2). Each block gathers properties, which are put inside the same list. For exmaple, in Figure 2, this is [[['S_5,...,S_800'],['U_5,...,U_800'],['C_v_5,...,C_v_800'],['H_5,...,H_800']],[['formation_energy']]]. The same names as given in ^{ } should be used. ^{
}: A dictionary where each key is a property name and the value the corresponding weight to be used in the loss function. The weights are used to scale the different outputs such that the balance between the properties is conserved when training. For example, {'S_5':0.01, 'formation_energy:1'}. ^{
} (optional): Number of neurons as well as the number of layers to be used in the neural network. List of three lists. Each inner list gives respectively the succesive number of neurons of the blocks 2, 3 and 4. For example, in Figure 2, this is given by [[128,128],[64,64],[8]]. ^{
} (optional): Number of optimal features to be used in the model. In Figure 2, this is 330. ^{
}(optional): Loss function of the neural network, see Keras API. ^{
} (optional): Activation function used in the neural network, see Keras API.
The model is then fitted on the data:
^{pr 13}$Arguments:
- ^{
} (optional): Validation fraction to be used while training. - ^{
} (optional): The name of the property used for printing validation MAE. When multiple properties are learned (e.g. ^{ }), setting the key_val (e.g. ^{ }) will only print the MAE of this property for each epoch. - ^{
} (optional): Learning rate. - ^{
} (optional): Number of epochs. - ^{
} (optional): Batch size. - ^{
} (optional): Scaling of the features. Possible values: ^{ } or ^{ }.
You can save and load the model for later usage:
^{pr 14}$ ^{pr 15}$Prediction is done by first creating a MODData instance on the new data:
^{pr 16}$and then using the predict method:
^{pr 17}$A dataframe containing the predictions is returned.
Author
This software is written by Pierre-Paul De Breuck
许可证
MODNet是在MIT许可下发布的。在
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