Machine Learning-Accelerated Prediction of Surface Energy in van der Waals Crystals

Abstract

Surface energy is a fundamental physical quantity that governs the stability and properties of van der Waals crystals, yet accurate estimation remains challenging due to the limitations of experimental and first-principles approaches. Herein we developed an efficient framework integrating density functional theory with machine learning methods to predict surface energies in vdW crystals. By combining structural characteristics with elemental properties, we trained several models and found that the generative adversarial network achieved the best performance (R2 = 96.97%, MSE = 1.693). Leveraging this model, we predicted surface energies for ∼800 vdW crystals, ranging from 0.67 to 42.47 meV/Å2. Further feature and bonding analysis revealed surface energy is significantly influenced by interlayer distance, atomic volume, and periodic elemental properties. Our study provides theoretical insights and a cost-effective, high-accuracy pathway for predicting surface energies, facilitating the design of 2D nanosheets and heterostructures.