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.
Shangbin Wu, Naihua Miao, Yu Shu et al.· Journal of Physical Chemistr...· 0 citations
Machine-learning interatomic potentials (MLIPs) bridge the accuracy of first-principles calculations and the efficiency required for large-scale molecular dynamics (MD) simulations. However, existing MLIP software remains fragmented across different model architectures, making it difficult to establish unified workflows that support flexible model development, efficient training, and scalable MD deployment. Here, we present AI2Pot, a scalable and unified MLIP framework that seamlessly integrates model training, evaluation, and large-scale MD simulations with PyTorch-compatible ecosystem. Instead of relying on generic automatic differentiation for expensive atomistic operators, AI2Pot re-engineers the core computations of Moment tensor potential (MTP) and Neuroevolution potential (NEP) for both training and inference using hand-crafted C++/CUDA code. These specialized operators constitute a unified computational backend shared by training and inference, improving training-inference consistency and reducing memory usage by avoiding large intermediate caches. As a result, AI2Pot enables fast inference for large-scale atomic systems containing millions of atoms on a single GPU, while retaining the flexibility of PyTorch for model construction, training, and evaluation. Trained models can be deployed in ASE and LAMMPS for MD simulations. Furthermore, AI2Pot provides a companion command-line toolkit (AI2Pot-cli) and Python APIs to facilitate practical MLIP workflows. By unifying high-performance atomistic computing with modern machine-learning ecosystems, AI2Pot offers an user-friendly end-to-end framework for the developing, training, and deploying MLIPs for large scale MD.
Hanyu Liu, Linggang Zhu, Xuanguang Zhang et al.· 0 citations