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.
中文翻译：
图神经网络已成为一种有效的机器学习工具，适用于药物分子分类和化学反应预测等多学科任务，因为它们可以对不同实体之间的非欧几里德关系进行建模。颗粒破碎作为土木工程的一个重要领域，在数值模拟的建模下描述了颗粒碎片键断裂导致颗粒材料的破碎，这促使我们通过颗粒碎片与颗粒的连通性来表征颗粒破碎的力学行为。图神经网络（GNN）。然而，由于实验室测试或数值模拟的成本昂贵，目前缺乏用于研究的开源大规模颗粒破碎数据集。因此，我们首先生成一个包含 45 的数据集，000个数值模拟和900个颗粒类型，促进颗粒破碎机器学习的研究进展。其次，我们设计了一种基于 GNN 的混合框架，利用最先进的 GNN 的进步来预测粒子碎片视图中的粒子破碎强度。最后，我们将我们的混合框架与传统机器学习方法和普通 MLP 进行比较，以验证其有效性。通过预测的梯度归因解释进一步讨论了不同特征的有用性。我们的数据和代码发布在https://github.com/doujian-zheng/GNN-For-Particle-Crushing。我们设计了一个基于 GNN 的混合框架，利用最先进的 GNN 的进步来预测粒子碎片视图中的粒子破碎强度。最后，我们将我们的混合框架与传统机器学习方法和普通 MLP 进行比较，以验证其有效性。通过预测的梯度归因解释进一步讨论了不同特征的有用性。我们的数据和代码发布在https://github.com/doujian-zheng/GNN-For-Particle-Crushing。我们设计了一个基于 GNN 的混合框架，利用最先进的 GNN 的进步来预测粒子碎片视图中的粒子破碎强度。最后，我们将我们的混合框架与传统机器学习方法和普通 MLP 进行比较，以验证其有效性。通过预测的梯度归因解释进一步讨论了不同特征的有用性。我们的数据和代码发布在https://github.com/doujian-zheng/GNN-For-Particle-Crushing。
