Editorial Summary

Structure-aware graph neural network: Enhanced prediction of material properties

Accurate materials property prediction using crystal structure occupies a primary and often critical role in materials science. Upon identification of a candidate material, one has to go through either a series of hands-on experiments or intensive density functional theory calculations which can take hours to days to even months depending on the complexity of the system. Hence, the ability to accurately predict the properties of interest of the material prior to synthesis can be extremely useful to prioritize available resources for simulations and experiments. Although composition-only based predictive models can be helpful for screening and identifying potential material candidates without the need for structure as an input, they are by design not capable of distinguishing between structure polymorphs of a given composition. Further, composition-only based models could potentially have substantial errors in the predicted values as compared to ground truth, as different structure polymorphs of a given composition can have drastically different properties. These shortcomings can be mitigated by incorporating structure-based inputs, and hence structure-based modeling presents bigger opportunities than composition-based modeling to advance the discovery process in the field of materials science. In this work, Vishu Gupta et al. from the Department of Electrical and Computer Engineering, Northwestern University, presented a framework for materials property prediction tasks that combines advanced data mining techniques with a structure-aware graph neural network (GNN) to improve the predictive performance of the model for materials properties with sparse data. They first applied a structure-aware GNN-based deep learning architecture to capture the underlying chemistry associated with the existing large data containing crystal structure information. The resulting knowledge learned was then transferred and used during training on the sparse dataset to develop reliable and accurate target models. The researchers evaluated the proposed framework in cross-property and cross-materials class scenarios using 115 datasets to find that transfer learning models outperform the models trained from scratch in 104 cases, i.e., ≈90%, with additional benefits in performance for extrapolation problems. The significant improvements gained by using the proposed framework are expected to be useful for materials science researchers to more gainfully utilize data mining techniques to help screen and identify potential material candidates more reliably and accurately for accelerating materials discovery. 

结构感知图神经网络：加速材料属性预测