极端力学力场的规划与探索方法
A Planning-and-Exploring Approach to Extreme-Mechanics Force Fields
AI总结：
针对极端力学过程，提出结合应变预采样与主动学习的神经网络力场NN-F$^3$，用于准确预测断裂，并在h-BN和扭曲双层石墨烯上验证。
摘要：
极端力学过程，如断裂过程中的强晶格畸变和键断裂，在自然界和工程中普遍存在，常导致结构的灾难性失效。然而，理解裂纹的形核与扩展因其多尺度特性而面临挑战，这些特性涵盖从裂纹尖端的原子级结构到施加载荷的结构特征。分子模拟为解决裂纹前沿的渐进微观结构变化提供了重要工具，并被广泛用于探索其中的过程，如机械能耗散、裂纹路径选择和动态不稳定性（如扭结、分叉）。基于原子位置和键序的局域描述符开发的经验力场，即使对于非线性、各向异性的应力-应变关系和边缘能量密度，也无法对断裂给出令人满意的预测。因此，高保真力场应包含应变的张量性质和断裂过程中稀有事件的能量学，遗憾的是，最先进的经验和机器学习力场均未考虑这些因素。基于第一性原理计算生成的数据，我们开发了一种基于神经网络的断裂力场NN-F$^3$，通过结合应变状态空间的预采样和主动学习技术来探索临界键距处的过渡态。NN-F$^3$的能力通过研究h-BN和扭曲双层石墨烯的破裂作为模型问题得到验证。模拟结果证实了最近的实验发现，并强调了在第一性原理计算中纳入电子结构知识对于预测极端力学过程的必要性。
英文摘要：
Extreme mechanical processes such as strong lattice distortion and bond breakage during fracture are ubiquitous in nature and engineering, which often lead to catastrophic failure of structures. However, understanding the nucleation and growth of cracks is challenged by their multiscale characteristics spanning from atomic-level structures at the crack tip to the structural features where the load is applied. Molecular simulations offer an important tool to resolve the progressive microstructural changes at crack fronts and are widely used to explore processes therein, such as mechanical energy dissipation, crack path selection, and dynamic instabilities (e.g., kinking, branching). Empirical force fields developed based on local descriptors based on atomic positions and the bond orders do not yield satisfying predictions of fracture, even for the nonlinear, anisotropic stress-strain relations and the energy densities of edges. High-fidelity force fields thus should include the tensorial nature of strain and the energetics of rare events during fracture, which, unfortunately, have not been taken into account in both the state-of-the-art empirical and machine-learning force fields. Based on data generated by first-principles calculations, we develop a neural network-based force field for fracture, NN-F$^3$, by combining pre-sampling of the space of strain states and active-learning techniques to explore the transition states at critical bonding distances. The capability of NN-F$^3$ is demonstrated by studying the rupture of h-BN and twisted bilayer graphene as model problems. The simulation results confirm recent experimental findings and highlight the necessity to include the knowledge of electronic structures from first-principles calculations in predicting extreme mechanical processes.
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