Npj Comput. Mater.: 裂纹扩展vs位错发射—bcc铁裂纹尖端变形机制

海归学者发起的公益学术平台

分享信息，整合资源

交流学术，偶尔风月

来自荷兰格罗来自荷兰格罗宁根大学的Francesco Maresca教授和博士生张磊基于主动学习构建了DFT精度的高斯机器学习势函数，给出了低温下单晶铁的脆性断裂主导的裂纹扩展机制。该研究通过利用高斯回归模型预测的不确定性作为主动学习指标，揭示了高斯模型中预测误差与真实误差之间的线性关系，表明了机器学习势函数可以通过特殊设计的数据库构型来增加精度，且主动学习的效率要远大于人工加入相关构型。该研究与相关实验测得的断裂韧性的对比揭示了即使在低温条件下（77K），位错活动对断裂韧性的影响仍不可忽略，为多尺度方法模拟工程材料的断裂韧性提供了新思路。

图1 （a）主动学习框架 （b）生成含裂纹尖端信息的DFT构型

该文近期发表于npj Computational Materials 9：217(2023)，英文标题与摘要如下，点击左下角“阅读原文”可以自由获取论文PDF。

Atomistic fracture in bcc iron revealed by active learning of Gaussian approximation potential 

Lei Zhang, Gábor Csányi, Erik van der Giessen & Francesco Maresca 

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.