Explainable predictions of multi-component oxides enabled by attention-based neural networks

Multicomponent oxides (MCOs) have attracted considerable attention due to their wide range of applications. However, the extensive search space of MCO components and the scarcity of MCO crystal structures in existing literature have promoted the use of deep machine learning methods for predicting MCO properties. Despite these advances, accurately predicting the thermal expansion of MCOs across wide temperature and composition ranges remains a complex task. An innovative attention-based deep learning model was introduced in this study. The two proposed self-attention modules greatly improved the model's performance, achieving an 86.88% reduction in root mean square error for predicting thermal expansion coefficients of multicomponent oxides. Additionally, the model demonstrates impressive adaptability and interpretability. Its training results can further aid in comprehending the thermal expansion coefficient variations of multicomponent oxide materials. In summary, judiciously crafted self-attention models overcome tradeoffs between performance and interpretability for materials discovery.

Automated summary

Multicomponent oxides (MCOs) have wide applications and accurately predicting their thermal expansion remains challenging. This study introduces an innovative attention-based deep learning model, which achieves improved performance by using two self-attention modules and demonstrates adaptability and interpretability.