Predicting Lattice Vibrational Frequencies Using Deep Graph Neural Networks

Nguyen, Nghia; Louis, Steph-Yves M.; Wei, Lai; Choudhary, Kamal; Hu, Ming; Hu, Jianjun

doi:10.1021/acsomega.2c02765

Cited by 3 publications

(3 citation statements)

References 36 publications

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“…Whereas the deeperGATGNN has also been applied to screen potential piezoelectric materials in the ABC 3 materials (Figure 10b). [72] Other material properties such as crystal vibrational frequencies (Figure 10c) [73] and electronic dielectric constants (Figure 10d) [74] can also be predicted based on the crystal structures, using the GATGNN‐based models.…”

Section: Detailed Review Of the Seven Specific Gnn Modelsmentioning

confidence: 99%

“…(c) Performance of deeperGATGNN for vibrational‐frequencies prediction over the Rhombohedron data set. 73 Reproduced under the terms of the Creative Commons CC BY‐NC‐ND 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/). (d) The electronic dielectric constant prediction model performance.…”

Section: Detailed Review Of the Seven Specific Gnn Modelsmentioning

confidence: 99%

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A review on the applications of graph neural networks in materials science at the atomic scale

Shi,

Zhou,

Huang

et al. 2024

Materials Genome Engineering Advances

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In recent years, interdisciplinary research has become increasingly popular within the scientific community. The fields of materials science and chemistry have also gradually begun to apply the machine learning technology developed by scientists from computer science. Graph neural networks (GNNs) are new machine learning models with powerful feature extraction, relationship inference, and compositional generalization capabilities. These advantages drive researchers to design computational models to accelerate material property prediction and new materials design, dramatically reducing the cost of traditional experimental methods. This review focuses on the principles and applications of the GNNs. The basic concepts and advantages of the GNNs are first introduced and compared to the traditional machine learning and neural networks. Then, the principles and highlights of seven classic GNN models, namely crystal graph convolutional neural networks, iCGCNN, Orbital Graph Convolutional Neural Network, MatErials Graph Network, Global Attention mechanism with Graph Neural Network, Atomistic Line Graph Neural Network, and BonDNet are discussed. Their connections and differences are also summarized. Finally, insights and prospects are provided for the rapid development of GNNs in materials science at the atomic scale.

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Section: Detailed Review Of the Seven Specific Gnn Modelsmentioning

confidence: 99%

Section: Detailed Review Of the Seven Specific Gnn Modelsmentioning

confidence: 99%

A review on the applications of graph neural networks in materials science at the atomic scale

Shi,

Zhou,

Huang

et al. 2024

Materials Genome Engineering Advances

View full text Add to dashboard Cite

show abstract

“…The accuracy of the model was suitable for rhombohedral crystals, but it reported high MAE for cubic structures while training on mixed samples revealing the low structural transferability of the model. [ 36 ]…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Material Engineering: A Review on Applications of Artificial Intelligence in Material Engineering

2023

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The role of artificial intelligence (AI) in material science and engineering (MSE) is becoming increasingly important as AI technology advances. The development of high‐performance computing has made it possible to test deep learning (DL) models with significant parameters, providing an opportunity to overcome the limitation of traditional computational methods, such as density functional theory (DFT), in property prediction. Machine learning (ML)‐based methods are faster and more accurate than DFT‐based methods. Furthermore, the generative adversarial networks (GANs) have facilitated the generation of chemical compositions of inorganic materials without using crystal structure information. These developments have significantly impacted material engineering (ME) and research. Some of the latest developments in AI in ME herein are reviewed. First, the development of AI in the critical areas of ME, such as in material processing, the study of structure and material property, and measuring the performance of materials in various aspects, is discussed. Then, the significant methods of AI and their uses in MSE, such as graph neural network, generative models, transfer of learning, etc. are discussed. The use of AI to analyze the results from existing analytical instruments is also discussed. Finally, AI's advantages, disadvantages, and future in ME are discussed.

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Machine learning a universal harmonic interatomic potential for predicting phonons in crystalline solids

Lee,

Xia

2024

Applied Physics Letters

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Phonons, as quantized vibrational modes in crystalline materials, play a crucial role in determining a wide range of physical properties, such as thermal and electrical conductivity, making their study a cornerstone in materials science. In this study, we present a simple yet effective strategy for deep learning harmonic phonons in crystalline solids by leveraging existing phonon databases and state-of-the-art machine learning techniques. The key of our method lies in transforming existing phonon datasets, primarily represented in interatomic force constants, into a force–displacement representation suitable for training machine learning universal interatomic potentials. By applying our approach to one of the largest phonon databases publicly available, we demonstrate that the resultant machine learning universal harmonic interatomic potential not only accurately predicts full harmonic phonon spectra but also calculates key thermodynamic properties with remarkable precision. Furthermore, the restriction to a harmonic potential energy surface in our model provides a way of assessing uncertainty in machine learning predictions of vibrational properties, essential for guiding further improvements and applications in materials science.

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Predicting Lattice Vibrational Frequencies Using Deep Graph Neural Networks

Cited by 3 publications

References 36 publications

A review on the applications of graph neural networks in materials science at the atomic scale

A review on the applications of graph neural networks in materials science at the atomic scale

Artificial Intelligence in Material Engineering: A Review on Applications of Artificial Intelligence in Material Engineering

Machine learning a universal harmonic interatomic potential for predicting phonons in crystalline solids

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