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.

翻译：图神经网络已成为药物分子分类、化学反应预测等多学科任务的有效机器学习工具，因其能够建模不同实体间的非欧几里得关系。颗粒破碎作为土木工程的重要研究领域，描述了在数值模拟建模中颗粒碎片键断裂导致的散体材料破坏，这促使我们通过颗粒碎片的连通性，利用图神经网络表征颗粒破碎的力学行为。然而，由于实验室试验或数值模拟的高昂成本，目前缺乏开源的大规模颗粒破碎数据集用于研究。为此，我们首先生成包含45,000次数值模拟和900种颗粒类型的数据集，以推动机器学习在颗粒破碎领域的研究进展。其次，我们设计基于GNN的混合框架，结合当前最先进的GNN技术，从颗粒碎片视角预测颗粒破碎强度。最后，我们将所提混合框架与传统机器学习方法及普通MLP进行对比，验证其有效性。通过梯度归因解释进一步讨论了不同特征对预测的贡献。我们的数据和代码已开源发布于https://github.com/doujiang-zheng/GNN-For-Particle-Crushing。