Graph Neural Networks-based Hybrid Framework For Predicting Particle
  Crushing Strength
Tongya Zheng Tianli Zhang Qingzheng Guan Wenjie Huang Zunlei Feng Mingli Song Chun Chen
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.