Finding Right Starting Point for Chemistry's Toughest Molecules
University of Pittsburgh researchers have developed a method to improve the accuracy of quantum chemistry simulations for complex molecules. This advancement could enhance the quality of data used to train machine-learning models in materials science.
Why it matters
Improving simulation accuracy for complex molecules accelerates the discovery of new materials for medicine, batteries, and sustainable manufacturing.
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 PhD 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.
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