The prediction of atomistic fracture mechanisms in body-centred cubic (bcc) iron is essential for understanding its semi-brittle nature. Existing atomistic simulations of the crack-tip under mode-I loading based on empirical interatomic potentials yield contradicting predictions and artificial mechanisms. To enable fracture prediction with quantum accuracy, we develop a Gaussian approximation potential (GAP) using an active learning strategy by extending a density functional theory (DFT) database of ferromagnetic bcc iron. We apply the active learning algorithm and obtain a Fe GAP model with a converged model uncertainty over a broad range of stress intensity factors (SIFs) and for four crack systems. The learning efficiency of the approach is analysed, and the predicted critical SIFs are compared with Griffith and Rice theories. The simulations reveal that cleavage along the original crack plane is the atomistic fracture mechanism for {100} and {110} crack planes at T = 0 K, thus settling a long-standing issue. Our work also highlights the need for a multiscale approach to predicting fracture and intrinsic ductility, whereby finite temperature, finite loading rate effects and pre-existing defects (e.g., nanovoids, dislocations) should be taken explicitly into account.

体心立方（bcc）铁的原子断裂机制的预测对于理解其半脆性性质至关重要。现有的基于经验原子间势的 I 型加载下裂纹尖端的原子模拟产生了相互矛盾的预测和人为机制。为了实现具有量子精度的断裂预测，我们通过扩展铁磁 bcc 铁的密度泛函理论 (DFT) 数据库，使用主动学习策略开发高斯近似势 (GAP)。我们应用主动学习算法并获得了 Fe GAP 模型，该模型在广泛的应力强度因子 (SIF) 和四个裂纹系统上具有收敛模型不确定性。分析了该方法的学习效率，并将预测的关键 SIF 与 Griffith 和 Rice 理论进行了比较。模拟结果表明，在T = 0 K时，沿原始裂纹面的解理是{100}和{110}裂纹面的原子断裂机制，从而解决了一个长期存在的问题。我们的工作还强调需要采用多尺度方法来预测断裂和固有延展性，其中应明确考虑有限温度、有限加载率效应和预先存在的缺陷（例如纳米空隙、位错）。