尖端变形行为：位错发射VS裂纹扩展？

来自荷兰格罗宁根大学的Francesco Maresca教授和博士生张磊，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。

作者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。他们的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。

Fig. 3 Main steps of the active learning procedure.

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。相关论文近期发布于npj Computational Materials 9: 217 (2023)。手机阅读原文，请点击本文底部左下角“阅读原文”，进入后亦可下载全文PDF文件。

Editorial Summary

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.This article was recently published in npj Computational Materials 9: 217 (2023).

原文Abstract及其翻译

Atomistic fracture in bcc iron revealed by active learning of Gaussian approximation potential (基于主动学习的高斯近似势函数揭示BCC铁在原子尺度下的断裂机制)

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

Abstract:  

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.

摘要

原子尺度下准确预测铁的断裂机制有助于人们深刻理解bcc铁的脆韧转变行为，现有的几种经典势函数在预测原子尺度断裂机制时结论不尽相同甚至有些相互矛盾。为了解决该问题，本研究开发了基于高斯近似机器学习势函数的主动学习框架，在该框架基础上拓展了bcc铁的第一性原理计算数据库。提出的新机器学习势函数模型能够预测铁裂纹尖端原子尺度断裂机制。主动学习框架确保了机器学习迭代过程中模型不确定性的收敛性，给出了预制裂纹在{100}和{110}面单晶bcc铁在低温下裂纹尖端断裂机制为脆性断裂本质。本工作为多尺度模拟预测断裂提供了理论指导，强调了多尺度模拟（考虑温度和预存在缺陷，如纳米孔，位错等）在断裂韧性预测中的必要性。该研究可为高断裂韧性的Fe基工程材料设计提供理论性指导。