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

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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.