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.

中文翻译：

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用于表面性质预测的表面强调多任务学习框架：以金属间镁为例


晶体的表面特性在许多领域都至关重要，包括电化学和光电子学，对其的有效预测可以加快催化剂、电池、合金等的设计和优化。然而，由于样品空间大和计算资源有限，与表面特性相关的数据库很少见，尤其是对于多组分化合物，我们离实现这一愿景还很远。在这项工作中，我们提出了一种表面强调的多任务晶体图卷积神经网络 （SEM-CGCNN），用于从晶体结构中同时预测多种表面特性。该模型在通过第一性原理计算获得的二元金属间镁的 3526 个表面能和功函数的数据集上进行了评估，与原始 CGCNN 模型相比，在效率和准确性方面都观察到了明显的改进。通过将预先训练的模型转移到纯金属和其他金属间化合物的数据集中，微调的 SEM-CGCNN 优于从头开始学习，并且可以进一步应用于其他表面特性和材料系统。这项研究可以成为原子结构与晶体各向异性表面特性的端到端映射的范式，这为理解和筛选具有所需表面特性的材料提供了一个有效的框架。