A surface emphasized multi-task learning framework for surface property predictions: A case study of magnesium intermetallics

• A multi-task learning framework for predicting surface properties from crystal structure graphs is presented. • The model is trained on 3526 surface structures of binary Mg intermetallics labeled with surface energies and work functions. • The ability to leverage different target features are demonstrated when trained from scratch or fine-tuned. • The facet-dependent work functions and corresponding Wulff shapes are predicted for 246 binary Mg intermetallics. Surface properties of crystals are critical in many fields, including electrochemistry and photoelectronics, the efficient prediction of which can expedite the design and optimization of catalysts, batteries, alloys etc. However, we are still far from realizing this vision due to the rarity of surface property-related databases, especially for multicomponent compounds, due to the large sample spaces and limited computing resources. In this work, we present a surface emphasized multi-task crystal graph convolutional neural network (SEM-CGCNN) to predict multiple surface properties simultaneously from crystal structures. The model is evaluated on a dataset of 3526 surface energies and work functions of binary magnesium intermetallics obtained through first-principles calculations, and obvious improvements are observed both in efficiency and accuracy over the original CGCNN model. By transferring the pre-trained model to the datasets of pure metals and other intermetallics, the fine-tuned SEM-CGCNN outperforms learning from scratch and can be further applied to other surface properties and materials systems. This study could be a paradigm for the end-to-end mapping of atomic structures to anisotropic surface properties of crystals, which provides an efficient framework to understand and screen materials with desired surface characteristics.