Graph convolutional neural networks with global attention for improved materials property prediction

Louis, Steph-Yves M.; Zhao, Yong; Nasiri, Alireza; Wong, Xiran; Song, Yuqi; Liu, Fei; Hu, Jianjun

doi:10.1039/d0cp01474e

Cited by 170 publications

(144 citation statements)

References 17 publications

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“…Secondly, the MatErials Graph Network (MEGNet) created by Chen et al 144 reported a MAE of 0.38 eV on 36,720 inorganic solids which was later improved to 0.33 after transfer learning. A global attention graph neural network (GATGNN) by Louis et al 145 pushed the average error achieved to MAEs of 0.32 eV and 0.31 eV using Open Quantum Materials Database (OQMD) 142 and Materials Project 146 datasets. Lastly, a weighted average smooth overlap of atomic positions (SOAP) based regression model by Olsthoorn et al 147 reported a MAE of 0.39 eV.…”

Section: Machine Learning For Characterisation Of Conductive Mofsmentioning

confidence: 99%

Computational techniques for characterisation of electrically conductive MOFs: quantum calculations and machine learning approaches

et al. 2021

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Section: Machine Learning For Characterisation Of Conductive Mofsmentioning

confidence: 99%

Computational techniques for characterisation of electrically conductive MOFs: quantum calculations and machine learning approaches

et al. 2021

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“…(i) DTC [32]: a rumor detection method that uses decision tree classifiers based on various handcrafted features to obtain information credibility (ii) SVM-TS [33]: a linear SVM classifier that uses handcrafted features to construct a time series model (iii) GRU [34]: a RNN-based model that learns temporal linguistic patterns from user comments (iv) cPTK [35]: a SVM classifier based on propagation tree kernels is proposed based on the propagation structure of rumors (v) RvNN [36]: a rumor detection method based on the tree recurrent neural network with GRU units, which learns rumor representation by the propagation structure (vi) PPC_RNN+CNN [37]: a rumor detection model combining RNN and CNN, which learns rumor representation by the user's features in the rumor propagation path (vii) GAT_GNN [38]: constructs a generalized network rumor detection model based on the GAT and GNN layers using a bidirectional propagation structure For a fair comparison, we randomly divide the dataset into 5 parts and perform a 5-fold cross-test to obtain more stable results. On this dataset, this paper evaluates the accuracy (Acc.)…”

Section: Contrasting Modelsmentioning

confidence: 99%

GCNRDM: A Social Network Rumor Detection Method Based on Graph Convolutional Network in Mobile Computing

Liu

Zhu

et al. 2021

Wireless Communications and Mobile Computing

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Mobile computing is a new technology emerging with the development of mobile communication, Internet, database, distributed computing, and other technologies. Mobile computing technology will enable computers or other information intelligent terminal devices to realize data transmission and resource sharing in the wireless environment. Its role is to bring useful, accurate, and timely information to any customer at anytime, anywhere, and to change the way people live and work. In mobile computing environment, a lot of Internet rumors hidden among the huge amounts of information communication network can cause harm to society and people’s life; this paper proposes a model of social network rumor detection based on convolution networks, the use of adjacency matrix between the nodes represent user and the relationship between the constructions of social network topology. We use a high-order graph neural network (K-GNN) to extract the rumor posting features. At the same time, the graph attention network (GAT) is used to extract the association features of other nodes of the network topology. The experimental results show that the method of the detection model in this paper improves the accuracy of prediction classification compared with deep learning methods such as RNN, GRU, and attention mechanism. The innovation of the paper proposes a rumor detection model based on the graph convolutional network, which lies in considering the propagation structure among users. It has a strong practical value.

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“…[15][16][17][18] These concepts are increasingly being adapted for solid state materials, often involving materials representations that combine properties of the constituent elements with structural features. [19][20][21][22][23][24][25][26][27][28] While these approaches are being deployed to good effect, they are not applicable in the common scenario of experimental materials science wherein a given material is composed of a mixture of phases, or even more so when no knowledge of the phases is available. In exploratory research for materials with specific properties, measurement and interpretation of composition and property data are often far less expensive than measurement and interpretation of structural data.…”

Section: Introductionmentioning

confidence: 99%

Materials Representation and Transfer Learning for Multi-Property Prediction

Kong¹,

Guevarra²,

Gomes³

et al. 2021

Preprint

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The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn the underlying interactions of multiple elements, as well as the relationships among multiple properties, to facilitate property prediction in new composition spaces. To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates (i) prediction using only a material’s composition, (ii) learning and exploitation of correlations among target properties in multitarget regression, and (iii) leveraging training data from tangential domains via generative transfer learning. The model is demonstrated for prediction of spectral optical absorption of complex metal oxides spanning 69 3-cation metal oxide composition spaces. H-CLMP accurately predicts non-linear composition-property relationships in composition spaces for which no training data is available, which broadens the purview of machine learning to the discovery of materials with exceptional properties. This achievement results from the principled integration of latent embedding learning, property correlation learning, generative transfer learning, and attention models. The best performance is obtained using H-CLMP with Transfer learning (H-CLMP(T)) wherein a generative adversarial network is trained on computational density of states data and deployed in the target domain to augment prediction of optical absorption from composition. H-CLMP(T) aggregates multiple knowledge sources with a framework that is well-suited for multi-target regression across the physical sciences.

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Graph convolutional neural networks with global attention for improved materials property prediction

Abstract: One of the major design issues in machine learning (ML) models for materials property prediction(MPP) is how to enable the models to learn property related physicochemical features. While many composition...

Cited by 170 publications

References 17 publications

Computational techniques for characterisation of electrically conductive MOFs: quantum calculations and machine learning approaches

Computational techniques for characterisation of electrically conductive MOFs: quantum calculations and machine learning approaches

GCNRDM: A Social Network Rumor Detection Method Based on Graph Convolutional Network in Mobile Computing

Materials Representation and Transfer Learning for Multi-Property Prediction

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