A high-throughput framework for determining adsorption energies on solid surfaces
Source abstract
Abstract
Abstract In this work, we present a high-throughput workflow for calculation of adsorption energies on solid surfaces using density functional theory. Using open-source computational tools from the Materials Project infrastructure, we automate the procedure of constructing symmetrically distinct adsorbate configurations for arbitrary slabs. These algorithms are further used to construct and run workflows in a standard, automated way such that user intervention in the simulation procedure is minimal. To validate our approach, we compare results from our workflow to previous experimental and theoretical benchmarks from the CE27 database of chemisorption energies on solid surfaces. These benchmarks also illustrate how the task of performing and managing over 200 individual density functional theory calculations may be reduced to a single submission procedure and subsequent analysis. By enabling more efficient high-throughput computations of adsorption energies, these tools will accelerate theory-guided discovery of advanced materials for applications in catalysis and surface science.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Machine Learning in Materials Science · Catalytic Processes in Materials Science · Electrocatalysts for Energy Conversion
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (29)
Show source-provided references
1. Nørskov, J. K., Bligaard, T., Rossmeisl, J. & Christensen, C. H. Towards the computational design of solid catalysts. Nat. Chem. 1, 37–46 (2009).10.1038/nchem.121 ↗
2. Studt, F. et al. Discovery of a Ni-Ga catalyst for carbon dioxide reduction to methanol. Nat. Chem. 6, 320–324 (2014).10.1038/nchem.1873 ↗
3. Hinnemann, B. et al. Biomimetic hydrogen evolution: MoS2 nanoparticles as catalyst for hydrogen evolution. J. Am. Chem. Soc. 127, 5308–5309 (2005).10.1021/ja0504690 ↗
4. Jain, A. et al. Commentary: the materials project: a materials genome approach to accelerating materials innovation. APL Mater. 1, 11002 (2013).10.1063/1.4812323 ↗
5. de Jong, M. et al. Charting the complete elastic properties of inorganic crystalline compounds. Sci. Data 2, 150009 (2015).10.1038/sdata.2015.9 ↗
6. Tran, R. et al. Surface energies of elemental crystals. Sci. Data 3, 160080 (2016).10.1038/sdata.2016.80 ↗
7. Peterson, A. A. Global optimization of adsorbate–surface structures while preserving molecular identity. Top. Catal. 57, 40–53 (2013).10.1007/s11244-013-0161-8 ↗
8. Laio, A. & Parrinello, M. Escaping free-energy minima. Proc. Natl. Acad. Sci. U. S. A. 99, 12562–12566 (2002).10.1073/pnas.202427399 ↗
9. Carr, S., Garnett, R. & Lo, C. BASC: applying Bayesian optimization to the search for global minima on potential energy surfaces. In Proceedings of the 33rd International Conference on Machine Learning 898–907 (2016).10.1063/1.4964671 ↗
10. Wellendorff, J. et al. Density functionals for surface science: exchange-correlation model development with Bayesian error estimation. Phys. Rev. B 85, 235149 (2012).10.1103/PhysRevB.85.235149 ↗
11. Mortensen, J. J., Hansen, L. B. & Jacobsen, K. W. Real-space grid implementation of the projector augmented wave method. Phys. Rev. B 71, 035109 (2005).10.1103/PhysRevB.71.035109 ↗
12. Enkovaara, J. et al. Electronic structure calculations with GPAW: a real-space implementation of the projector augmented-wave method. J. Phys. Condens. Matter 22, 253202 (2010).10.1088/0953-8984/22/25/253202 ↗
13. Abild-Pedersen, F. & Andersson, M. P. CO adsorption energies on metals with correction for high coordination adsorption sites—a density functional study. Surf. Sci. 601, 1747–1753 (2007).10.1016/j.susc.2007.01.052 ↗
14. Brown, W. A., Kose, R. & King, D. A. Femtomole adsorption calorimetry on single-crystal surfaces. Chem. Rev. 98, 797–832 (1998).10.1021/cr9700890 ↗
15. Conrad, H., Ertl, G., Koch, J. & Latta, E. E. Adsorption of CO on Pd single crystal surfaces. Surf. Sci. 43, 462–480 (1974).10.1016/0039-6028(74)90270-2 ↗
16. Behm, R. J., Christmann, K., Ertl, G. & Van Hove, M. A. Adsorption of CO on Pd(100). J. Chem. Phys. 73, 2984–2995 (1980).10.1063/1.440430 ↗
17. Ge, Q., Kose, R. & King, D. A. Adsorption energetics and bonding from femtomole calorimetry and from first principles theory. Adv. Catal. 45, 207–259 (2000).10.1016/S0360-0564(02)45015-8 ↗
18. Fiorin, V., Borthwick, D. & King, D. A. Microcalorimetry of O2 and NO on flat and stepped platinum surfaces. Surf. Sci. 603, 1360–1364 (2009).10.1016/j.susc.2008.08.034 ↗
19. Yeo, Y. Y., Vattuone, L. & King, D. A. Calorimetric investigation of NO and CO adsorption on Pd{100} and the influence of preadsorbed carbon. J. Chem. Phys. 106, 1990–1996 (1997).10.1063/1.473306 ↗
20. Bozso, F., Ertl, G., Grunze, M. & Weiss, M. Interaction of nitrogen with iron surfaces: I. Fe(100) and Fe(111). J. Catal. 49, 18–41 (1977).10.1016/0021-9517(77)90237-8 ↗
21. Christmann, K. Interaction of hydrogen with solid surfaces. Surf. Sci. Rep. 9, 1–163 (1988).10.1016/0167-5729(88)90009-X ↗
22. Perdew, J. P., Burke, K. & Ernzerhof, M. Generalized gradient approximation made simple. Phys. Rev. Lett. 77, 3865–3868 (1996).10.1103/PhysRevLett.77.3865 ↗
23. Hammer, B., Hansen, L. B. & Nørskov, J. K. Improved adsorption energetics within density-functional theory using revised Perdew-Burke-Ernzerhof functionals. Phys. Rev. B 59, 7413–7420 (1999).10.1103/PhysRevB.59.7413 ↗
24. Ulissi, Z. W., Singh, A. R., Tsai, C. & Nørskov, J. K. Automated discovery and construction of surface phase diagrams using machine learning. J. Phys. Chem. Lett. 7, 3931–3935 (2016).10.1021/acs.jpclett.6b01254 ↗
25. Calle-Vallejo, F., Loffreda, D., Koper, M. T. M. & Sautet, P. Introducing structural sensitivity into adsorption–energy scaling relations by means of coordination numbers. Nat. Chem. 7, 403–410 (2015).10.1038/nchem.2226 ↗
26. Ong, S. P. et al. Python materials genomics (pymatgen): a robust, open-source python library for materials analysis. Comput. Mater. Sci. 68, 314–319 (2013).10.1016/j.commatsci.2012.10.028 ↗
27. Jain, A. et al. FireWorks: a dynamic workflow system designed for high-throughput applications. Concurr. Comput. Pract. Exp. 27, 5037–5059 (2015).10.1002/cpe.3505 ↗
28. Kresse, G. & Furthmüller, J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. Mater. Sci. 6, 15–50 (1996).10.1016/0927-0256(96)00008-0 ↗
29. Kresse, G. & Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. Rev. B 54, 11169–11186 (1996).10.1103/PhysRevB.54.11169 ↗