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

机器学习正在迅速成为计算科学中最有用的工具和非常受欢迎的技能之一。在这个项目中，学生将学习如何使用机器学习算法，并努力将它们应用于材料科学中的一个关键的现实世界问题。了解结构与性能之间的关系对于新材料设计是至关重要的。近年来，高熵合金作为冶金领域最活跃的领域之一，已经证明了局域结构(即原子尺度上的相互作用)对合金物理性能的重要性。同样，已经在航空航天工业中服役的合金也显示出一些现象，表明其强度受到局部结构效应的影响。如果要成功地在工业上开发这些特性，就需要充分了解局部结构和特性之间的这种联系。这需要计算驱动局部结构形成的潜在能量学。计算机模拟通常用于根据输入势预测系统内发生的顺序，这使得生成任意大量的局部结构数据变得相当容易。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，这个问题很难反转。这使得这个问题非常适合于机器学习技术的应用，这些技术可以学习能量和局部结构之间的映射。这个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在金属系统中观察到的短程有序化的能量。结构合金的X射线和中子散射数据将在大规模设施(钻石光源、ISIS中子源)上获得，并提取局部结构，用于验证机器学习模型。力学测试还将在与所创建的模型相关的性能上进行。

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.