Spanning the Scales: Insights into Dislocation Mobility Provided by Machine Learning and Coarse-Grained Models

跨越尺度：机器学习和粗粒度模型提供的位错迁移率洞察

• 批准号: 2588438
• 金额: --
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2588438
• 关键词: Spanning; Scales; Insights; into; Dislocation

How do metals break? How can we make them stronger? What are the roles of defects and impurities? The strength of materials are ultimately determined by the microscopic interactions on the atomic level, which can be modelled accurately. However, the challenge is that computationally it is not possible to propagate information in one step from the nanometer to the millimeter scale. In this project, you will use combined Quantum Mechanics-Molecular Mechanics and Gaussian Approximation Potentials, a machine learning approach, to develop coarse-grain models of dislocations and to make quantitative predictions of plastic deformations in metals and alloys.The stress generated in a metal resisting plastic deformation is governed by the dislocation mobility, the ease by which dislocations move through the crystal. Dislocation motion is limited by the processes of formation and migration of kinks and pinning by defects. Modelling of these processes on the atomistic scale has been carried out for fast-moving dislocations under large stresses, corresponding to conditions in the rise of a shock wave, but at lower strain-rates, timescales are too long to access using atomistic methods.Coarse grained methods bypass the need to model the atoms explicitly, determining the dislocation mobility from quantities such as the kink-pair activation enthalpy using statistical mechanics. A multiscale approach is needed to reveal the details of the structure and energetics of dislocations, including the dislocation kink structure and dependencies on non-glide stress components, as well as providing inputs for improved coarse-grained models.In collaboration with AWE, this project will explore the link between dislocation energetics and dislocation mobility, through the development of coarse-grained models for screw dislocation mobility in bcc metals, including the effect of non-glide stresses. Machine-learning based interatomic potentials and QM/MM approaches will be used to span the gap between the capabilities of ab initio modelling and the required time and length scales. In addition to studying the pure metal, the effect of impurities will be investigated. Small interstitial impurities and larger substitutional alloying will be considered. Building on prior work, W will be considered initially. There is scope for considering other systems such as Fe and steel, Ta, TaW, or V. Validation of the coarse-grained dislocation model in the strongly driven regime will be sought by comparing with direct molecular dynamics simulations of dislocation mobility. Investigation of how the coarse-grained model will be validated in the weakly driven regime will be pursued.

金属是如何断裂的？我们怎样才能让他们更强大？缺陷和杂质的作用是什么？材料的强度最终取决于原子水平上的微观相互作用，这可以精确建模。然而，挑战在于计算上不可能将信息一步从纳米传播到毫米尺度。在这个项目中，您将使用量子力学-分子力学和高斯近似势相结合的机器学习方法来开发位错的粗晶粒模型，并对金属和合金中的塑性变形进行定量预测。金属中产生的抵抗塑性变形的应力由位错迁移率决定，位错通过晶体移动的容易程度。位错运动受扭结的形成和迁移过程以及缺陷的钉扎限制。在原子尺度上对这些过程的模拟已经在大应力下快速移动的位错中进行了，对应于冲击波上升的条件，但是在较低的应变率下，时间尺度太长，无法使用原子方法进行访问。粗粒度方法绕过了明确建模原子的需要，使用统计力学从诸如扭结对激活焓的量确定位错迁移率。需要多尺度方法来揭示位错的结构和能量学的细节，包括位错扭结结构和对非滑移应力分量的依赖性，以及为改进的粗粒度模型提供输入。本项目与AWE合作，将探索位错能量学和位错迁移率之间的联系，通过发展体心立方金属中螺旋位错运动的粗粒模型，包括非滑移应力的影响。基于机器学习的原子间势和QM/MM方法将用于跨越从头算建模能力与所需时间和长度尺度之间的差距。除了研究纯金属外，还将研究杂质的影响。小间隙杂质和较大的替代合金化将被考虑。在先前工作的基础上，W将被初步考虑。有范围考虑其他系统，如铁和钢，钽，钽钨，或V.验证粗晶粒位错模型在强驱动制度将寻求通过比较与直接分子动力学模拟位错流动性。如何粗粒度的模型将被验证在弱驱动制度的调查将继续进行。