Researchers use machine learning technique to rapidly evaluate new transition metal compounds

phys.org | 7/9/2019 | Staff
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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 Ph.D. '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 - Roles - Areas

Transition metal complexes already play important roles in areas ranging from energy storage to catalysis for manufacturing fine chemicals—for example, for pharmaceuticals. But Kulik thinks that machine learning could further expand their use. Indeed, her group has been working not only to apply machine learning to inorganics—a novel and challenging undertaking—but also to use the technique to explore new territory. "We were interested in understanding how far we could push our models to do discovery—to make predictions on compounds that haven't been seen before," says Kulik.

For the past four years, Kulik and Jon Paul Janet, a graduate student in chemical engineering, have been focusing on transition metal complexes with "spin"—a quantum mechanical property of electrons. Usually, electrons occur in pairs, one with spin up and the other with spin down, so they cancel each other out and there's no net spin. But in a transition metal, electrons can be unpaired, and the resulting net spin is the property that makes inorganic complexes of interest, says Kulik. "Tailoring how unpaired the...
(Excerpt) Read more at: phys.org
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