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最受欢迎的技能之一。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问题。了解结构-性能关系对于新材料设计至关重要。近年来，高熵合金，冶金学中最活跃的领域之一，已经证明了局部结构（即原子尺度上的相互作用）对合金物理性能的重要性。类似地，已经在航空航天工业中使用的合金已经证明了表明局部结构对其强度的影响的现象。如果要成功地在工业上利用这些性质，就需要充分理解局部结构和性质之间的联系。这需要计算驱动局部结构形成的基本能量学。计算机模拟通常用于从输入势预测系统内发生的有序，这使得生成任意大的局部结构数据相当容易。然而，由于多壳层相互作用的复杂性和由此产生的数学概率，这个问题很难反演。这使得该问题成为机器学习技术应用的理想选择，机器学习技术可以学习能量和局部结构之间的映射。该项目将使用机器学习模型计算能量参数。这将使我们能够估计驱动金属系统中观察到的短程有序的能量。将在大型设施（钻石光源、ISIS中子源）中获得结构合金的X射线和中子散射数据，并提取局部结构，用于验证机器学习模型。还将在与创建的模型相关的属性上进行机械测试。