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Most computers and materials work on scales such that quantum effects can be comfortably ignored. But as we aim to make computers ever smaller, quantum effects will cause difficulties; however, they also provide opportunities. Spin-crossover (SCO) materials are molecules that can “Flip” between two states: either high or low spin binary states, making them essentially the miniaturization goal for electronics. These molecules do not exist in a vacuum, but rather in crystals. When molecules crystallize they can do so in several different ways, called polymorphs that affect the space around the individual molecules and this can make a molecule either show or fail to show SCO activity. It’s hard to make molecules, and to have it then fail to crystallize in a polymorph that is conducive to SCO behaviour would be unfortunate. It is really hard to predict the polymorph that a compound will make; however, it might be possible to do so with a large enough dataset and modern machine learning tools. In this project we are looking to build this model with the help of an international intern so that we can move towards building highly miniaturized logic gates and perhaps eventually, very, very small computers.
John Trant
University of Tabriz
Physics
Quantum Science; Technology; Artificial Intelligence
University of Windsor
Globalink Research Award
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