journal article·2018-07-05Open access

Theoretical Investigations of Transition Metal Surface Energies under Lattice Strain and CO Environment

Michael T. Tang, Zachary W. Ulissi, Karen Chan

The Journal of Physical Chemistry C· Volume 122· Issue 26

Abstract

An understanding of the relative stability of surface facets is crucial to develop predictive models of catalyst activity and to fabricate catalysts with a controlled morphology. In this work, we present a systematic density functional theory study of the effect of lattice strain and CO environment on the surface formation energies of Cu, Pt, and Ni. First, we show that both compressive and tensile lattice strains favor the formation of stepped versus low-index terraces such as (111) and (100). Then, we investigate the effect of the CO environment using configurations of CO at various coverages, determined using a greedy, systematic approach, inspired by forward stepwise feature selection. We find that the CO environment favors stepped facets on Ni, Cu, and Pt. These trends are illustrated with the corresponding equilibrium Wulff shapes at various strains and CO pressures. In general, the surface energies of the studied transition metals are highly sensitive to strain and CO coverage, which should be considered when rationalizing trends in the catalytic activity.
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Research context

OpenAlex topics: Machine Learning in Materials Science · nanoparticles nucleation surface interactions · Catalytic Processes in Materials Science

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Affiliations

SUNCAT Center for Interface Science and Catalysis, Department of Chemical Engineering, Stanford University, Stanford, California 94305, United States

SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory, Menlo Park, California 94025, United States

Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States

Funding

Division of Graduate Education · Office of Science

References (41)

Show source-provided references
1. 10.1103/physrevlett.93.15680110.1103/physrevlett.93.156801 ↗
2. 10.1103/physrevlett.81.281910.1103/physrevlett.81.2819 ↗
3. 10.1016/j.apcata.2012.11.01510.1016/j.apcata.2012.11.015 ↗
4. 10.1002/anie.20130662010.1002/anie.201306620 ↗
5. 10.1021/jz300192b10.1021/jz300192b ↗
6. 10.1021/nn305987x10.1021/nn305987x ↗
7. 10.1039/b919982a10.1039/b919982a ↗
8. 10.1021/acs.nanolett.5b0115410.1021/acs.nanolett.5b01154 ↗
9. 10.1016/j.apcata.2012.03.03410.1016/j.apcata.2012.03.034 ↗
10. 10.1126/science.aaf768010.1126/science.aaf7680 ↗
11. 10.1039/c2ee22172a10.1039/c2ee22172a ↗
12. 10.1016/j.apsusc.2004.07.02010.1016/j.apsusc.2004.07.020 ↗
13. 10.1021/ja503017210.1021/ja5030172 ↗
14. 10.1002/cssc.20130044710.1002/cssc.201300447 ↗
15. 10.1002/crat.20080040210.1002/crat.200800402 ↗
16. 10.1103/physrevlett.71.435010.1103/physrevlett.71.4350 ↗
17. 10.1103/physrevb.41.1023310.1103/physrevb.41.10233 ↗
18. 10.1016/0039-6028(83)90258-310.1016/0039-6028(83)90258-3 ↗
19. 10.1021/nl404661u10.1021/nl404661u ↗
20. 10.1021/ja904033t10.1021/ja904033t ↗
21. 10.1039/c0cp00659a10.1039/c0cp00659a ↗
22. 10.1088/0953-8984/21/39/39550210.1088/0953-8984/21/39/395502 ↗
23. 10.1088/1361-648x/aa680e10.1088/1361-648x/aa680e ↗
24. 10.1103/physrevb.85.23514910.1103/physrevb.85.235149 ↗
25. 10.1103/physrevlett.77.386510.1103/physrevlett.77.3865 ↗
26. 10.1038/sdata.2016.8010.1038/sdata.2016.80 ↗
27. 10.1103/physrevb.13.518810.1103/physrevb.13.5188 ↗
28. Cramer C. · 2004
29. 10.1016/j.commatsci.2012.10.02810.1016/j.commatsci.2012.10.028 ↗
30. 10.1038/nmat313410.1038/nmat3134 ↗
31. 10.1016/j.susc.2013.05.01610.1016/j.susc.2013.05.016 ↗
32. 10.1088/0953-8984/8/36/00510.1088/0953-8984/8/36/005 ↗
33. 10.1021/jp404569m10.1021/jp404569m ↗
34. 10.1002/crat.20150016310.1002/crat.201500163 ↗
35. 10.1021/acs.jpclett.5b0174610.1021/acs.jpclett.5b01746 ↗
36. Caruana, R.; Freitag, D. Greedy Attribute Selection. Proceedings of the Eleventh International Conference on Machine Learning, 1994; pp 28–36.10.1016/B978-1-55860-335-6.50012-X ↗
37. 10.1063/1.172481610.1063/1.1724816 ↗
38. 10.1021/acs.oprd.6b0010310.1021/acs.oprd.6b00103 ↗
39. 10.1103/physrevlett.66.303210.1103/physrevlett.66.3032 ↗
40. 10.1103/physrevb.86.08545710.1103/physrevb.86.085457 ↗
41. 10.1021/acs.jpcc.7b0391010.1021/acs.jpcc.7b03910 ↗