Applying Machine Learning to local structure in metallic systems for structural applications

将机器学习应用于金属系统的局部结构以实现结构应用

• 批准号: 2903293
• 金额: --
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2903293
• 关键词: Applying; Machine; Learning; local; structure

Machine learning is rapidly becoming one of the most useful tools and highly sought-after skills in computational science. In this project, the student will learn how to use machine learning algorithms and work to apply them to a key real-world problem in materials science. Understanding structure-property relationships is fundamentally important for novel materials design. Recently, high-entropy alloys, one of the most active fields in metallurgy, have demonstrated the importance of local structure (i.e. interactions on the atomic scale) to alloy physical properties. Similarly, alloys already in service in the aerospace industry, have demonstrated phenomena suggesting local structural effects on their strength. If these properties are to be successfully industrially exploited, this link between local-structure and properties needs to be fully understood. This requires the calculation of the underlying energetics driving the formation of local structure.Computer simulations are commonly used to predict order occurring within a system from an input potential, making it fairly easy to generate arbitrary large data of local structure. However, this problem is difficult to invert due to the complexity of multi-shell interactions and resulting mathematical probabilities. This makes the problem ideal for the application of machine learning techniques that can learn the mapping between energetics and local structure.This project will use machine learning models for the calculation of energetic parameters. This will enable us to estimate the energetics driving the short-range ordering observed in metallic systems. X-ray and Neutron scattering data from structural alloys will be obtained at large scale facilities (Diamond Light Source, ISIS Neutron Source), and the local structure extracted, and used to validate the machine learning models. Mechanical testing will also be carried out at the properties correlated with the models created.

机器学习正在迅速成为计算科学中最有用的工具和备受追捧的技能之一。在这个项目中，学生将学习如何使用机器学习算法并将其应用于材料科学中的关键现实世界问题。了解结构 - 陶艺关系对于新颖的材料设计至关重要。最近，冶金中最活跃的磁场之一的高渗透合金证明了局部结构（即在原子尺度上的相互作用）与合金物理特性的重要性。同样，已经在航空航天行业使用的合金表明了现象，表明了局部结构性影响其强度。如果要成功地利用这些属性，则需要充分理解本地结构和属性之间的这种联系。这需要计算驱动驱动局部结构的基础能量的计算。computer仿真通常用于预测系统内部从输入电位中发生的顺序，从而很容易生成局部结构的任意大数据。但是，由于多壳相互作用的复杂性和由此产生的数学概率，因此很难反转此问题。这使问题是应用机器学习技术的理想选择，该技术可以学习能量学和本地结构之间的映射。本项目将使用机器学习模型来计算能量参数。这将使我们能够估计驱动金属系统中观察到的短程顺序的能量学。将在大规模设施（钻石光源，ISIS中子源）和提取的局部结构中获得来自结构合金的X射线和中子散射数据，并用于验证机器学习模型。机械测试还将在与创建的模型相关的属性下进行。