Finding novel materials for practical devices

In recent years, machine learning has been proving a valuable tool for identifying new materials with properties optimized for specific applications. Working with large, well-defined data sets, computers learn to perform an analytical task to generate a correct answer and then use the same technique on an unknown data set.  While that approach has guided the development of valuable new materials, they’ve primarily been organic compounds, notes Heather Kulik PhD ’09, an assistant professor of chemical engineering. Kulik focuses instead on inorganic compounds — in particular, those based on transition metals, a family of elements (including iron and copper) that have unique and useful properties. In those compounds — known as transition metal complexes — the metal atom occurs at the center with chemically bound arms, or ligands, made of carbon, hydrogen, nitrogen, or oxygen atoms radiating outward.  Transition metal complexes already play important roles in areas ranging from energy storage to Continue reading Finding novel materials for practical devices