Abstract

Platinum, palladium have multiple and critical uses as catalysts, these metals play a key role in breaking down nitric oxide and other pollutants in car exhausts and tend to deliver the best results across a broad range of chemical reactions. They are not needed in enormous quantities for any single reaction, but more applications for these materials are being found. Even today, recycling accounts for a quarter of the platinum and palladium supply, because mining simply cannot deliver enough of these metals. The search is on for substitute materials that can do the jobs of the platinum-group metals, but which use far more abundant and readily available raw materials. The traditional tool was trial and error, which is an expensive, time-consuming process. Recently the process has shifted into the virtual domain, with chemists building digital twins of potential catalysts and target molecules. Another method that chemists employ in catalyst research is density functional theory (DFT). Despite its far greater simplicity compared to more accurate methods, DFT still takes hours to compute on today's hardware in catalytic scenarios like these. Other researchers are looking at using machine learning for a faster approximation of DFT and similar quantum-behaviour calculations.

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