Machine-Learning-Augmented Chemisorption Model for CO2 Electroreduction Catalyst Screening

Journal of Physical Chemistry Letters

Volumn 6, Issue 18, 2015, Pages 3528-3533

  Ma, Xianfeng ^a   Li, Zheng ^a   Achenie, Luke E K ^a   Xin, Hongliang ^a  

^a Virginia Polytechnic Institute and State University   (United States)

Author keywords

alloys; artificial neural networks; carbon dioxide reduction; density functional theory; machine learning; reactivity descriptors

Indexed keywords

ALLOYING; BIOMIMETICS; CARBON DIOXIDE; CATALYSIS; CATALYST SELECTIVITY; CHEMICAL ANALYSIS; CHEMICAL BONDS; CHEMISORPTION; DENSITY FUNCTIONAL THEORY; ELECTROLYTIC REDUCTION; LEARNING SYSTEMS; MACHINE LEARNING; NEURAL NETWORKS; POLLUTION CONTROL;

ADSORPTION ENERGIES; BIMETALLIC SURFACES; BIOLOGICALLY INSPIRED; CARBON DIOXIDE REDUCTION; ELECTROCHEMICAL REDUCTIONS; HIGH THROUGHPUT CATALYST SCREENINGS; NONLINEAR INTERACTIONS; REACTIVITY DESCRIPTORS;

LEARNING ALGORITHMS;

EID: 84941781142     PISSN: None     EISSN: 19487185     Source Type: Journal    
DOI: 10.1021/acs.jpclett.5b01660     Document Type: Article

References (59)

1. Yu, W.; Porosoff, M. D.; Chen, J. G. Review of Pt-Based Bimetallic Catalysis: From Model Surfaces to Supported Catalysts Chem. Rev. 2012, 112, 5780-5817 10.1021/cr300096b
2. Rodriguez, J. Physical and Chemical Properties of Bimetallic Surfaces Surf. Sci. Rep. 1996, 24, 223-287 10.1016/0167-5729(96)00004-0
3. Campbell, C. T. Bimetallic Surface Chemistry Annu. Rev. Phys. Chem. 1990, 41, 775-837 10.1146/annurev.pc.41.100190.004015
4. Calle-Vallejo, F.; Koper, M. T. M.; Bandarenka, A. S. Tailoring the Catalytic Activity of Electrodes with Monolayer Amounts of Foreign Metals Chem. Soc. Rev. 2013, 42, 5210-5230 10.1039/c3cs60026b
5. Bandarenka, A. S.; Koper, M. T. M. Structural and Electronic Effects in Heterogeneous Electrocatalysis: Toward a Rational Design of Electrocatalysts J. Catal. 2013, 308, 11-24 10.1016/j.jcat.2013.05.006
6. Nie, Y.; Li, L.; Wei, Z. Recent Advancements in Pt and Pt-free Catalysts for Oxygen Reduction Reaction Chem. Soc. Rev. 2015, 44, 2168-2201 10.1039/C4CS00484A
7. Wu, J.; Yang, H. Platinum-Based Oxygen Reduction Electrocatalysts Acc. Chem. Res. 2013, 46, 1848-1857 10.1021/ar300359w
8. Hammer, B.; Nørskov, J. K. Electronic Factors Determining the Reactivity of Metal Surfaces Surf. Sci. 1995, 343, 211-220 10.1016/0039-6028(96)80007-0
9. Hammer, B.; Nørskov, J. K. Why Gold is the Noblest of All the Metals Nature 1995, 376, 238-240 10.1038/376238a0
10. Xin, H.; Holewinski, A.; Schweitzer, N.; Nikolla, E.; Linic, S. Electronic Structure Engineering in Heterogeneous Catalysis: Identifying Novel Alloy Catalysts Based on Rapid Screening for Materials with Desired Electronic Properties Top. Catal. 2012, 55, 376-390 10.1007/s11244-012-9794-2
11. Inoglu, J. R.; Kitchin, N. Simple Model Explaining and Predicting Coverage-dependent Atomic Adsorption Energies on Transition Metal Surfaces Phys. Rev. B: Condens. Matter Mater. Phys. 2010, 82, 045414 10.1103/PhysRevB.82.045414
12. Xin, H.; Holewinski, A.; Linic, S. Predictive Structure Reactivity Models for Rapid Screening of Pt-Based Multimetallic Electrocatalysts for the Oxygen Reduction Reaction ACS Catal. 2012, 2, 12-16 10.1021/cs200462f
13. Harrison, W. A. Electronic Structure and the Properties of Solids: The Physics of the Chemical Bond; Dover Publications: Mineola, NY, 1989.
14. 2 Utilization via Direct Heterogeneous Electrochemical Reduction J. Phys. Chem. Lett. 2010, 1, 3451-3458 10.1021/jz1012627
15. Costentin, C.; Robert, M.; Savéant, J.-M. Catalysis of the Electrochemical Reduction of Carbon Dioxide Chem. Soc. Rev. 2013, 42, 2423-2436 10.1039/C2CS35360A
16. 2 to Hydrocarbons at Copper J. Electroanal. Chem. 2006, 594, 1-19 10.1016/j.jelechem.2006.05.013
17. Hori, Y. In Modern Aspects of Electrochemistry; Vayenas, C. G.; White, R. E.; Gamboa-Aldeco, M. E., Eds.; Modern Aspects of Electrochemistry 42; Springer: New York, 2008.
18. Lu, Q.; Rosen, J.; Zhou, Y.; Hutchings, G. S.; Kimmel, Y. C.; Chen, J. G.; Jiao, F. A Selective and Efficient Electrocatalyst for Carbon Dioxide Reduction Nat. Commun. 2014, 5, 3242 10.1038/ncomms4242
19. 2 Electroreduction to Methane on Transition-Metal Catalysts J. Phys. Chem. Lett. 2012, 3, 251-258 10.1021/jz201461p
20. Peterson, A. A.; Abild-Pedersen, F.; Studt, F.; Rossmeisl, J.; Nørskov, J. K. How Copper Catalyzes the Electroreduction of Carbon Dioxide into Hydrocarbon Fuels Energy Environ. Sci. 2010, 3, 1311-1315 10.1039/c0ee00071j
21. 2 Species in the Electrochemical Reduction of Carbon Dioxide on Copper Electrodes Chem. Sci. 2011, 2, 1902-1909 10.1039/c1sc00277e
22. Schouten, K. J. P.; Qin, Z.; Gallent, E. P.; Koper, M. T. M. Two Pathways for the Formation of Ethylene in CO Reduction on Single-Crystal Copper Electrodes J. Am. Chem. Soc. 2012, 134, 9864-9867 10.1021/ja302668n
23. Schouten, K. J. P.; Pérez Gallent, E.; Koper, M. T. M. Structure Sensitivity of the Electrochemical Reduction of Carbon Monoxide on Copper Single Crystals ACS Catal. 2013, 3, 1292-1295 10.1021/cs4002404
24. Hori, Y.; Takahashi, I.; Koga, O.; Hoshi, N. Electrochemical Reduction of Carbon Dioxide at Various Series of Copper Single Crystal Electrodes J. Mol. Catal. A: Chem. 2003, 199, 39-47 10.1016/S1381-1169(03)00016-5
25. 2 by Copper Surfaces Surf. Sci. 2011, 605, 1354-1359 10.1016/j.susc.2011.04.028
26. Roberts, F. S.; Kuhl, K. P.; Nilsson, A. High Selectivity for Ethylene from Carbon Dioxide Reduction over Copper Nanocube Electrocatalysts Angew. Chem. 2015, 127, 5268-5271 10.1002/ange.201412214
27. 2 Species on Cu(100) Electrodes Angew. Chem., Int. Ed. 2013, 52, 7282-7285 10.1002/anie.201301470
28. 2 Electroreduction J. Phys. Chem. Lett. 2015, 6, 2032-2037 10.1021/acs.jpclett.5b00722
29. Hori, Y.; Koga, O.; Watanabe, Y.; Matsuo, T. FTIR Measurements of Charge Displacement Adsorption of CO on Poly- and Single Crystal (100) of Cu Electrodes Electrochim. Acta 1998, 44, 1389-1395 10.1016/S0013-4686(98)00261-8
30. Giannozzi, P.; Baroni, S.; Bonini, N.; Calandra, M.; Car, R.; Cavazzoni, C.; Ceresoli, D.; Chiarotti, G. L.; Cococcioni, M.; Dabo, I. et al. QUANTUM ESPRESSO: A Modular and Open-source Software Project for Quantum Simulations of Materials J. Phys.: Condens. Matter 2009, 21, 395502 10.1088/0953-8984/21/39/395502
31. Nørskov, J. K.; Rossmeisl, J.; Logadottir, A.; Lindqvist, L.; Kitchin, J. R.; Bligaard, T.; Jónsson, H. Origin of the Overpotential for Oxygen Reduction at a Fuel-Cell Cathode J. Phys. Chem. B 2004, 108, 17886-17892 10.1021/jp047349j
32. Tracy, J. C. Structural Influences on Adsorption Energy. II. CO on Ni(100) J. Chem. Phys. 1972, 56, 2736-2747 10.1063/1.1677602
33. 2 Electroreduction on Copper Electrodes ChemCatChem 2013, 5, 737-742 10.1002/cctc.201200564
34. 2 Reduction on Copper Electrodes: The Role of the Kinetics of Elementary Steps Angew. Chem., Int. Ed. 2013, 52, 2459-2462 10.1002/anie.201208320
35. 2 Electrochemical Reduction on Cu(111) Determined with Density Functional Theory J. Catal. 2014, 312, 108-122 10.1016/j.jcat.2014.01.013
36. Abild-Pedersen, F.; Greeley, J.; Studt, F.; Rossmeisl, J.; Munter, T. R.; Moses, P. G.; Skulason, E.; Bligaard, T.; Norskov, J. K. Scaling Properties of Adsorption Energies for Hydrogen-Containing Molecules on Transition-Metal Surfaces Phys. Rev. Lett. 2007, 99, 016105-4 10.1103/PhysRevLett.99.016105
37. Truong, C. M.; Rodriguez, J.; Goodman, D. W. CO Adsorption Isotherms on Cu(100) at Elevated Pressures and Temperatures Using Infrared Reflection Absorption Spectroscopy Surf. Sci. 1992, 271, L385-L391 10.1016/0039-6028(92)90896-E
38. Inoglu, N.; Kitchin, J. R. New Solid-state Table: Estimating d-band Characteristics for Transition Metal Atoms Mol. Simul. 2010, 36, 633 10.1080/08927022.2010.481794
39. Hammer, B.; Morikawa, Y.; Nørskov, J. K. CO Chemisorption at Metal Surfaces and Overlayers Phys. Rev. Lett. 1996, 76, 2141 10.1103/PhysRevLett.76.2141
40. Xin, H.; Linic, S. Exceptions to the d-band Model of Chemisorption on Metal Surfaces: The Dominant Role of Repulsion Between Adsorbate States and Metal d-states J. Chem. Phys. 2010, 132, 221101-221101-4 10.1063/1.3437609
41. Xin, H.; Vojvodic, A.; Voss, J.; Nørskov, J. K.; Abild-Pedersen, F. Effects of d-band Shape on the Surface Reactivity of Transition-metal Alloys Phys. Rev. B: Condens. Matter Mater. Phys. 2014, 89, 115114 10.1103/PhysRevB.89.115114
42. Vojvodic, A.; Nørskov, J. K.; Abild-Pedersen, F. Electronic Structure Effects in Transition Metal Surface Chemistry Top. Catal. 2014, 57, 25 10.1007/s11244-013-0159-2
43. Pettersson, L. G. M.; Nilsson, A. A Molecular Perspective on the d-band Model: Synergy Between Experiment and Theory Top. Catal. 2014, 57, 2-13 10.1007/s11244-013-0157-4
44. Nørskov, J. K.; Abild-Pedersen, F.; Studt, F.; Bligaard, T. Density Functional Theory in Surface Chemistry and Catalysis Proc. Natl. Acad. Sci. U. S. A. 2011, 108, 937-943 10.1073/pnas.1006652108
45. Andriotis, A. N.; Mpourmpakis, G.; Broderick, S.; Rajan, K.; Datta, S.; Sunkara, M.; Menon, M. Informatics Guided Discovery of Surface Structure-chemistry Relationships in Catalytic Nanoparticles J. Chem. Phys. 2014, 140, 094705 10.1063/1.4867010
46. Rupp, M.; Tkatchenko, A.; Müller, K.-R.; von Lilienfeld, O. A. Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning Phys. Rev. Lett. 2012, 108, 058301 10.1103/PhysRevLett.108.058301
47. Montavon, G.; Rupp, M.; Gobre, V.; Vazquez-Mayagoitia, A.; Hansen, K.; Tkatchenko, A.; Müller, K.-R.; von Lilienfeld, O. A. Machine Learning of Molecular Electronic Properties in Chemical Compound Space New J. Phys. 2013, 15, 095003 10.1088/1367-2630/15/9/095003
48. Landrum, G. A.; Penzotti, J. E.; Putta, S. Machine-learning Models for Combinatorial Catalyst Discovery Meas. Sci. Technol. 2005, 16, 270 10.1088/0957-0233/16/1/035
49. Kusne, A. G.; Gao, T.; Mehta, A.; Ke, L.; Nguyen, M. C.; Ho, K.-M.; Antropov, V.; Wang, C.-Z.; Kramer, M. J.; Long, C. On-the-fly Machine-learning for High-throughput Experiments: Search for Rare-earth-free Permanent Magnets Sci. Rep. 2014, 4, 6367 10.1038/srep06367
50. Hautier, G.; Fischer, C. C.; Jain, A.; Mueller, T.; Ceder, G. Finding Nature Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory Chem. Mater. 2010, 22, 3762-3767 10.1021/cm100795d
51. Dam, H. C.; Pham, T. L.; Ho, T. B.; Nguyen, A. T.; Nguyen, V. C. Data Mining for Materials Design: A Computational Study of Single Molecule Magnet J. Chem. Phys. 2014, 140, 044101 10.1063/1.4862156
52. Schaul, T.; Bayer, J.; Wierstra, D.; Sun, Y.; Felder, M.; Sehnke, F.; Rückstieß, T.; Schmidhuber, J. PyBrain J. Mach. Learn. Res. 2010, 11, 743-746
53. Scott, D. J.; Coveney, P. V.; Kilner, J. A.; Rossiny, J. C. H.; Alford, N. M. N. Prediction of the Functional Properties of Ceramic Materials from Composition Using Artificial Neural Networks J. Eur. Ceram. Soc. 2007, 27, 4425-4435 10.1016/j.jeurceramsoc.2007.02.212
54. Behler, J.; Parrinello, M. Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces Phys. Rev. Lett. 2007, 98, 146401 10.1103/PhysRevLett.98.146401
55. Behler, J. Representing Potential Energy Surfaces by High-dimensional Neural Network Potentials J. Phys.: Condens. Matter 2014, 26, 183001 10.1088/0953-8984/26/18/183001
56. Abu-Mostafa, Y. S.; Magdon-Ismail, M.; Lin, H.-T. Learning From Data; AMLBook.com, S.l., 2012.
57. 0.5Fe Nanoparticle Core Electrocatalyst with High Activity and Stability for the Oxygen Reduction Reaction J. Am. Chem. Soc. 2010, 132, 14364-14366 10.1021/ja1063873
58. Kitchin, J. R.; Nørskov, J. K.; Barteau, M. A.; Chen, J. G. Role of Strain and Ligand Effects in the Modification of the Electronic and Chemical Properties of Bimetallic Surfaces Phys. Rev. Lett. 2004, 93, 156801 10.1103/PhysRevLett.93.156801
59. Engelbrecht, A. P.; Cloete, I.; Zurada, J. M. In From Natural to Artificial Neural Computation; Mira, J.; Sandoval, F., Eds.; Lecture Notes in Computer Science 930; Springer: Berlin Heidelberg, 1995.