Patrick Taylor

Research

Ni superalloys are an extremely technologically important class of material. However, the development of new superalloys by tradtional methods is a lengthy and experimentally intensive process. I investigate the use of machine learning and atomistic tools to be used in tandem to design new superalloys. My current focus is on the optimisation of thermomechanical properties, and most importantly the creep strength, of single crystal superalloys. As precipitation strengthened alloys, a crucial first step to modelling their properties is predicting their phase composition. To this end, I have developed a new Gaussian Process Regression model of phase microchemistry. My current work is on extending this machine learning approach to make use of physical representations of the alloys' input features.