Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published July 24, 2019 | Updated
Journal Article Open

Identifying Active Sites for CO₂ Reduction on Dealloyed Gold Surfaces by Combining Machine Learning with Multiscale Simulations

Abstract

Gold nanoparticles (AuNPs) and dealloyed Au_3Fe core–shell NP surfaces have been shown to have dramatically improved performance in reducing CO_2 to CO (CO2RR), but the surface features responsible for these improvements are not known. The active sites cannot be identified with surface science experiments, and quantum mechanics (QM) is not practical for the 10 000 surface sites of a 10 nm NP (200 000 bulk atoms). Here, we combine machine learning, multiscale simulations, and QM to predict the performance (a-value) of all 5000–10 000 surface sites on AuNPs and dealloyed Au surfaces. We then identify the optimal active sites for CO2RR on dealloyed gold surfaces with dramatically reduced computational effort. This approach provides a powerful tool to visualize the catalytic activity of the whole surface. Comparing the a-value with descriptors from experiment, computation, or theory should provide new ways to guide the design of high-performance electrocatalysts for applications in clean energy conversion.

Additional Information

© 2019 American Chemical Society. Received: May 8, 2019; Published: June 18, 2019. This work was supported by the Joint Center for Artificial Photosynthesis, a DOE Energy Innovation Hub, supported through the Office of Science of the U.S. Department of Energy under Award Number DE-SC0004993. This work uses the computational resources of Caltech High Performance Computing Center (HPC). The authors declare no competing financial interest.

Attached Files

Updated - ja9b04956_si_001.pdf

Files

ja9b04956_si_001.pdf
Files (5.8 MB)
Name Size Download all
md5:0f59be1699d93f90b3685f5afa3301f7
5.8 MB Preview Download

Additional details

Created:
August 19, 2023
Modified:
October 20, 2023