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Now scientists have a way to capture the details of chemistry choreography as it happens. The method -- which relies on computers that have learned to recognize hidden signs of the steps -- should help them improve the performance of catalysts to drive reactions toward desired products faster.
The method -- developed by an interdisciplinary team of chemists, computational scientists, and physicists at the U.S. Department of Energy's Brookhaven National Laboratory and Stony Brook University -- is described in a new paper published in the Journal of Physical Chemistry Letters. The paper demonstrates how the team used neural networks and machine learning to teach computers to decode previously inaccessible information from x-ray data, and then used that data to decipher 3D nanoscale structures.
Challenge - Catalysts - Ones - Trial-and-error - Anatoly
"The main challenge in developing catalysts is knowing how they work -- so we can design better ones rationally, not by trial-and-error," said Anatoly Frenkel, leader of the research team who has a joint appointment with Brookhaven Lab's Chemistry Division and Stony Brook University's Materials Science Department. "The explanation for how catalysts work is at the level of atoms and very precise measurements of distances between them, which can change as they react. Therefore it is not so important to know the catalysts' architecture when they are made but more important to follow that as they react."
Trouble is, important reactions -- those that create important industrial chemicals such as fertilizers -- often take place at high temperatures and under pressure, which complicates measurement techniques. For example, x-rays can reveal some atomic-level structures by causing atoms that absorb their energy to emit electronic waves. As those waves interact with nearby atoms, they reveal their positions in a way that's similar to how distortions in ripples on the surface of a pond can reveal the presence of rocks. But the ripple...
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