Atomistic fracture in bcc iron revealed by active learning of Gaussian approximation potential
Lei Zhang, Gábor Csányi, Erik van der Giessen & Francesco Maresca
npj Computational Materials 9: 217 (2023); Published online: 08 Dec 2023
Editorial Summary
Crack-tip deformation mechanism in bcc iron: dislocation emission VS. cleavage? Active learning interatomic potential!
The prediction of atomistic fracture mechanisms in body-centred cubic (bcc) iron is essential for understanding its semi-brittle nature. Current research suggests that the interplay between thermal activation of screw dislocations and crack-tip dislocation emission dominates the brittle-to-ductile transition in BCC metals. Due to the limitation of computational power, DFT is not able to simulate the atomic-scale crack-tip extension. Classical molecular dynamics (MD) with different EAM interatomic potentials yield different crack-tip deformation mechanisms, which contradicts each other. This study proposes an active learning approach to extract crack-tip configurations, applicable across various machine learning frameworks and materials, enabling first-principles accuracy in predicting atomic-scale crack-tip deformation mechanism. Professor Francesco Maresca and PhD student Lei Zhang at the University of Groningen developed a Gaussian approximation potential with near DFT accuracy, revealing that brittle fracture is the dominating mechanism in single-crystal iron with pre-existing cracks at low temperatures. The research demonstrates that the accuracy of machine learning potentials can be improved through specially designed database configurations, and active learning is significantly efficient than manually adding relevant configurations. By comparing the MD predicted fracture toughness with experiments, the study showed that even at low temperatures (77K), the influence of dislocations on fracture toughness cannot be ignored. The research emphasizes the importance of multiscale simulations in predicting fracture toughness, offering new insights for engineering materials using multiscale simulation methods.
编辑概述
BCC铁裂纹尖端变形行为:位错发射VS裂纹扩展?
脆性断裂是BCC过渡态金属的重要失效机制,限制了BCC金属在低温下的应用并且会引起突发结构失效。现有的研究认为螺位错滑移的热激活和裂纹尖端断裂机制的共同作用主导着BCC金属中的脆韧转变。由于DFT计算能力的限制,无法模拟全域原子尺度的裂纹尖端扩展过程,而经典分子动力学所采用的EAM势函数给出了具有争议的结果,即不同的EAM势函数预测的裂纹尖端变形机制不同,这主要是因为EAM势函数是为模拟不同的材料性质进行的拟合。该研究提出了一种提取裂纹尖端信息的主动学习方法,这一方法可应用于不同机器学习框架和不同材料,能够以近第一性原理精度预测原子尺度下裂纹尖端的变形行为。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方法。