Searching for Next Generation Spintronic Materials Using Machine Learning

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Engineering of materials is an increasingly important aspect of modern technology. Tuning physical material**properties offers incredible flexibility not only to optimize performance and efficiency of devices such as**computer transistors, solar cells, sensors, and many others, but also an avenue to realize fundamentally new**device concepts such as novel quantum qubits. Materials science remains extraordinarily challenging simply**due to the huge range of combinations of atomic composition, structure, and synthesis techniques that can be**applied. Guiding research is incredibly challenging. Lumiant Corporation is a Canadian company developing a**machine learning based artificial intelligence, Xaedra, that can be taught to predict material structures and**compositions that will yield desirable properties. To achieve this, Xaedra must be fed a large amount of**verified experimental data and theoretical calculations relating to any particular physical property. The**proposed project aims to explore Xaedra's applicability to predicting dynamic properties in magnetic materials.**Specifically, the project will teach Xaedra how to predict a property called damping which determines how**much energy is dissipated during a magnetodynamic change. Minimizing damping is crucial in the**actualization of practical 'spintronics' - a computing scheme that aims to use magnetism to replace electronic**charge to create a new breed of ultra-efficient computers. Success in this project will significantly expand**Xaedra's capabilities in predicting magnetic properties and dynamic properties. It will also demonstrate**Xaedra's value as a tool in a very active and commercially prominent area of materials science research.