Universal and efficient graph neural networks with dynamic attention for machine learning interatomic potentials

Abstract

The core of molecular dynamics simulation fundamentally lies in the interatomic potential. Traditional empirical potentials lack accuracy, while first-principles methods are computationally prohibitive. Machine learning interatomic potentials (MLIPs) promise near-quantum accuracy at linear cost, but existing models still face challenges in efficiency and stability. We presents Machine Learning Advances Neural Network (MLANet), an efficient and robust graph neural network framework. MLANet introduces a dual-path dynamic attention mechanism for geometry-aware message passing and a multi-perspective pooling strategy to construct comprehensive system representations. This design enables highly accurate modeling of atomic environments while achieving exceptional computational efficiency, making high-fidelity simulations more accessible. Tested across a wide range of datasets spanning diverse systems, including organic molecules (e.g., QM7, MD17), periodic inorganic materials (e.g., Li-containing crystals), two-dimensional materials (e.g., bilayer graphene, black phosphorus), surface catalytic reactions (e.g., formate decomposition), and charged systems, MLANet maintains competitive prediction accuracy while its computational cost is markedly lower than mainstream equivariant models, and it enables stable long-time molecular dynamics simulations. MLANet provides an efficient and practical tool for large-scale, high-accuracy atomic simulations.

Figures (6)

• Figure 1: The architecture of MLANet.a Overall Model Architecture. b Internal Structure of MLANet.c Message Passing Layer of MLANet.
• Figure 2: Learning Curves and Model Efficiency of MLANet on QM7.a Histogram of molecular masses in the QM7 dataset, b histogram of atomization energies. c Log–log learning curve of energy prediction MAE versus training set size, d memory usage and training time per epoch for MLANet with maximum rotation order $l_{max} \in \{1, 2, 3\}$.
• Figure 3: Learning curves and computational efficiency for MLANet on the lithium-containing subset of Mptrj.a, b, c Log-log plots show the scaling of energy, force, and stress prediction errors (MAE) with training set size for MLANet variants with maximum rotation order $l_{max} \in \{1, 2, 3\}$. d The panel compares memory usage and training speed per epoch across model variants.
• Figure 4: Exfoliation of phosphorene. a, b Exfoliation curves of black phosphorene predicted by the MLANet model without (a) and with (b) the long‑range interaction term (red lines), compared with DFT + MBD reference data (black dashed lines). c, d Exfoliation curves of Hittorf’s phosphorus (P2/c and P2/n structures) predicted by the MLANet model including the long‑range interaction term compared with DFT + MBD reference data points The plots show the energy evolution with interlayer distance.
• Figure 5: Performance comparison of force prediction accuracy (MAE), MD stability, and computational speed for small molecule systems. Metrics are normalized to their maximum values for visualization. See Table. S3 in the SI for numerical data. Speed tests used a single NVIDIA V100 GPU.