Graph Neural Networks-based Hybrid Framework For Predicting Particle Crushing Strength

• URL: http://arxiv.org/abs/2307.13909v1
• Date: Wed, 26 Jul 2023 02:18:04 GMT
• Title: Graph Neural Networks-based Hybrid Framework For Predicting Particle Crushing Strength
• Authors: Tongya Zheng, Tianli Zhang, Qingzheng Guan, Wenjie Huang, Zunlei Feng, Mingli Song, Chun Chen
• Abstract summary: We use Graph Neural Networks to characterize the mechanical behaviors of particle crushing. We devise a hybrid framework based on GNNs to predict particle crushing strength in a particle fragment view. Our data and code are released at https://github.com/doujiang-zheng/GNN-For-Particle-Crushing.
• Score: 31.05985193732974
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph Neural Networks have emerged as an effective machine learning tool for multi-disciplinary tasks such as pharmaceutical molecule classification and chemical reaction prediction, because they can model non-euclidean relationships between different entities. Particle crushing, as a significant field of civil engineering, describes the breakage of granular materials caused by the breakage of particle fragment bonds under the modeling of numerical simulations, which motivates us to characterize the mechanical behaviors of particle crushing through the connectivity of particle fragments with Graph Neural Networks (GNNs). However, there lacks an open-source large-scale particle crushing dataset for research due to the expensive costs of laboratory tests or numerical simulations. Therefore, we firstly generate a dataset with 45,000 numerical simulations and 900 particle types to facilitate the research progress of machine learning for particle crushing. Secondly, we devise a hybrid framework based on GNNs to predict particle crushing strength in a particle fragment view with the advances of state of the art GNNs. Finally, we compare our hybrid framework against traditional machine learning methods and the plain MLP to verify its effectiveness. The usefulness of different features is further discussed through the gradient attribution explanation w.r.t the predictions. Our data and code are released at https://github.com/doujiang-zheng/GNN-For-Particle-Crushing.

Related papers

• Hybrid Quantum Graph Neural Network for Molecular Property Prediction [0.17747993681679466]
  We develop a free hybrid quantum gradient classical convoluted graph neural network to predict formation energies of perovskite materials. Our study suggests a new pathway to explore how quantum feature encoding and parametric quantum circuits can yield drastic improvements of complex machine learning algorithms.
  arXiv  Detail & Related papers  (2024-05-08T16:43:25Z)
• Graph Neural Network-based surrogate model for granular flows [2.153852088624324]
  Granular flow dynamics is crucial for assessing various geotechnical risks, including landslides and debris flows. Traditional continuum and discrete numerical methods are limited by their computational cost in simulating large-scale systems. We develop a graph neural network-based simulator (GNS) that takes the current state of granular flow and predicts the next state using explicit integration by learning the local interaction laws.
  arXiv  Detail & Related papers  (2023-05-09T07:28:12Z)
• Interpretable Joint Event-Particle Reconstruction for Neutrino Physics at NOvA with Sparse CNNs and Transformers [124.29621071934693]
  We present a novel neural network architecture that combines the spatial learning enabled by convolutions with the contextual learning enabled by attention. TransformerCVN simultaneously classifies each event and reconstructs every individual particle's identity. This architecture enables us to perform several interpretability studies which provide insights into the network's predictions.
  arXiv  Detail & Related papers  (2023-03-10T20:36:23Z)
• Graph neural networks for the prediction of molecular structure-property relationships [59.11160990637615]
  Graph neural networks (GNNs) are a novel machine learning method that directly work on the molecular graph. GNNs allow to learn properties in an end-to-end fashion, thereby avoiding the need for informative descriptors. We describe the fundamentals of GNNs and demonstrate the application of GNNs via two examples for molecular property prediction.
  arXiv  Detail & Related papers  (2022-07-25T11:30:44Z)
• Transformer with Implicit Edges for Particle-based Physics Simulation [135.77656965678196]
  Transformer with Implicit Edges (TIE) captures the rich semantics of particle interactions in an edge-free manner. We evaluate our model on diverse domains of varying complexity and materials.
  arXiv  Detail & Related papers  (2022-07-22T03:45:29Z)
• Fast Simulation of Particulate Suspensions Enabled by Graph Neural Network [21.266813711114153]
  We introduce a new framework, hydrodynamic interaction graph neural network (HIGNN), for inferring and predicting the particles' dynamics in Stokes suspensions. It overcomes the limitations of traditional approaches in computational efficiency, accuracy, and/or transferability. We demonstrate the accuracy, efficiency, and transferability of the proposed HIGNN framework in a variety of systems.
  arXiv  Detail & Related papers  (2022-06-17T14:05:53Z)
• The Preliminary Results on Analysis of TAIGA-IACT Images Using Convolutional Neural Networks [68.8204255655161]
  The aim of the work is to study the possibility of the machine learning application to solve the tasks set for TAIGA-IACT. The method of Convolutional Neural Networks (CNN) was applied to process and analyze Monte-Carlo events simulated with CORSIKA.
  arXiv  Detail & Related papers  (2021-12-19T15:17:20Z)
• A Universal Framework for Featurization of Atomistic Systems [0.0]
  Reactive force fields based on physics or machine learning can be used to bridge the gap in time and length scales. We introduce the Gaussian multi-pole (GMP) featurization scheme that utilizes physically-relevant multi-pole expansions of the electron density around atoms. We demonstrate that GMP-based models can achieve chemical accuracy for the QM9 dataset, and their accuracy remains reasonable even when extrapolating to new elements.
  arXiv  Detail & Related papers  (2021-02-04T03:11:00Z)
• Graph Neural Network for Hamiltonian-Based Material Property Prediction [56.94118357003096]
  We present and compare several different graph convolution networks that are able to predict the band gap for inorganic materials. The models are developed to incorporate two different features: the information of each orbital itself and the interaction between each other. The results show that our model can get a promising prediction accuracy with cross-validation.
  arXiv  Detail & Related papers  (2020-05-27T13:32:10Z)
• Learning to Simulate Complex Physics with Graph Networks [68.43901833812448]
  We present a machine learning framework and model implementation that can learn to simulate a wide variety of challenging physical domains. Our framework---which we term "Graph Network-based Simulators" (GNS)--represents the state of a physical system with particles, expressed as nodes in a graph, and computes dynamics via learned message-passing. Our results show that our model can generalize from single-timestep predictions with thousands of particles during training, to different initial conditions, thousands of timesteps, and at least an order of magnitude more particles at test time.
  arXiv  Detail & Related papers  (2020-02-21T16:44:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.