Machine learning for materials

The entire space of materials - including organic and inorganic crystal structures - is huge. Insight into the behavior of materials can be gained by using data-driven tools to reveal hidden physical relationships which are embedded in a high-dimensional feature space, not readily perceived without statistical tools. The goal of this research is multifaceted and includes both knowledge discovery - gaining new physical insight through data analytics methods - and materials discovery. The Kaxiras group is currently exploiting machine learning tools to explore two-dimensional magnetic materials, catalysis on metal surfaces and branched electronic flow in graphene.  

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