Understanding the interactions between materials and chemical species is critical for engineers as it helps them determine their best uses for both day-to-day life and global-level applications.
computational modeling
and machine learning to effectively replace the traditional trial-and-error method of scientific research. Their virtual reactivity model accurately predicts what electrochemical conditions result when hydrogen is inserted into different metal oxides. "The larger implications are that models like the one developed in this work—which takes into consideration local and global geometry of a material—can find diverse applications from energy storage to catalysis and the production of chemicals," Mpourmpakis said."Any application that is sensitive to the structure of a material can use this model to develop structure-property relationships and identify materials with desired performance.
"What's more, computational modeling allows us to test and validate a large number of materials safely and effectively at a much lower cost with great success that other researchers can then further pursue." Pitt's Swanson School has previously published research on how materials interact with their chemical environment in the past. In May 2022,
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