This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: New materials and catalysts have the potential to transform everything from food production and medicine to batteries and stainless steel. Computer simulations help scientists narrow down promising candidates before testing them in a lab, but those simulations can be unreliable, especially for molecules that contain multiple metals.
A team at the University of Pittsburgh has developed a practical fix that scientists can use to get better results. The work was led by first author Abdulrahman Zamani, a postdoctoral associate in Kay Carter-Fenk's group in the Department of Chemistry in the Kenneth P. Dietrich School of Arts and Sciences, along with undergraduate researcher Andrew Ricciuti.
They collaborated with John Keith and his Ph.D. student Barbaro Zulueta in the Swanson School of Engineering. The work could immediately improve the data used to train machine-learning and artificial intelligence (AI) models. The team's research, "Improving coupled cluster theory for strongly correlated molecules with Kohn-Sham density encoding," is published in the journal Nature Communications.
"To predict how atoms interact and undergo reactions, we use techniques like coupled cluster [CC] theory and density functional theory [DFT]," said Keith, professor of chemical and petroleum engineering. "Both are useful, but they model problems differently." CC begins with a picture in which each electron moves through the average field created by all the others. CC then refines that picture step by step by adding back the missing energetics of how pairs of electrons interact, then groups of three, and so on.
For many molecules, CC is considered the "gold standard" of quantum chemistry, but its use is restricted because its computational cost rises steeply as the molecule gets bigger. DFT is a more efficient approach because it begins with a simpler picture of the overall density of the electrons. However, DFT makes several cost-cutting approximations and uses parameters that researchers have not yet perfected, sometimes making results inaccurate.
Unfortunately, both CC and DFT perform poorly with abundant transition metals such as iron, cobalt and chromium. Chromium (Cr), an essential ingredient in stainless steel, is famously difficult to model accurately. "Chromium is a fairly small metal atom.
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