Computational Catalyst Screening

LARS C. GRABOW

Chemical & Biomolecular Engineering, University of Houston, Houston, Texas, 77204-4004, USA

Email: grabow@uh.edu

1.1 Introduction

Brute-force attacks are known in cryptography as (typically illegal) attempts to hack into encrypted data by systematically trying all possible key combinations of letters, digits and special characters until the correct access key or password has been found. Although a brute-force attack is guaranteed to be successful, its application is limited to very small problems because of the time required to generate and test all possible key combinations. For example, a standard 128-bit encryption key has 2128 possible permutations. If we simply assume that a typical central processing unit (CPU) can generate 109 bit flips per second (~1 GHz), then the total time that is required to test all possible permutations is 2128/109 = 3.4×1029 seconds or 1022 years! For obvious reasons a brute-force attack is most likely going to fail for this problem and a more targeted strategy is needed.

The above example illustrates the shortcomings of a brute-force attack, but a variation of it is still one of the most widely used strategies for the development of heterogeneous catalysts in practice. By using a combinatorial chemistry approach with completely automated, high-throughput experimentation equipment, one can synthesize and test enormous libraries of catalysts for their catalytic activity for a specific reaction. A good example is the search for advanced water–gas shift catalysts, in which Yaccato et al. have synthesized over 50 000 catalysts and tested them in more than 250 000 experiments for low, medium and high temperature water–gas shift conditions. Their effort led to a proprietary noble metal catalyst that can reduce the reactor volume by an order of magnitude without increasing the reactor cost. Although this trial-and-error approach almost always leads to an acceptable catalyst, the search space is restricted by the amount of time and resources available and many, possibly far better, candidates can be missed. The quickly evolving alternative to experimental high-throughput catalyst testing is computational catalyst screening. This approach relies on the fact that the catalyst activity for many catalytic reactions is usually determined by a small number of descriptors, which can be calculated from first-principles density functional theory (DFT) simulations and stored in a large property database. Populating this property database with DFT data is the most time-consuming step in this process, but the resulting database is applicable to any reaction and only has to be generated once. With a comprehensive database in place, it becomes a very easy task to screen thousands of database records in a short amount of time to identify catalyst candidates that possess descriptor values within the optimal range for a given reaction. Although the computational screening process can still be interpreted as a brute-force attack, the complexity of the problem has been greatly reduced. Hence, the number of materials that can be screened computationally increases drastically when compared with the experimental counterpart. The list of catalysts that fall into the desired range of descriptor values may be narrowed down further by using cost, stability, environmental friendliness, or any other applicable criteria. The remaining materials can then be synthesized and experimentally tested under realistic reaction conditions. In general, not all computationally screened candidates will be good catalysts, but good catalysts will usually be included in the candidate list.

Somorjai and Li have recently reviewed the major advances in modern surface science that only became possible through the successful symbiosis of theory and surface sensitive experimental techniques. The recent literature also contains several examples where a descriptor-based approach, both theoretically and experimentally, has led to the discovery of new catalytic materials. The following list should not be understood as an exhaustive review, but is meant to serve as inspiration to the reader and to demonstrate the wide applicability of this method. Early on, Besenbacher et al. discovered graphite resistant Ni/Au alloy catalysts for steam reforming, Jacobsen et al. found an active Co/Mo alloy for ammonia synthesis by interpolation in the periodic table, and Toulhoat and Raybaud showed that the metal–sulfur bond strength can correctly predict trends in hydro-desulfurization activity on metal–sulfide catalysts. These initial successes were followed by other prominent examples that include CO-tolerant fuel cell anodes, Cu/Ag alloys as selective ethylene epoxidation catalysts, near-surface alloys for hydrogen activation and evolution, Ru/Pt core–shell particles for preferential CO oxidation, Ni/Zn alloys for the selective hydrogenation of acetylene, Sc and Y modified Pt and Pd electrodes and mixed-metal Pt monolayer catalysts for electro-chemical oxygen reduction, and the rediscovery of Pt as the most active and selective catalyst for the production of hydrogen cyanide.

1.1.1 A Walk through a Computational Catalyst Design Process: Methanation

The most comprehensive example of a success story in computational catalyst design comes from the group of Jens Nørskov, who has pioneered the descriptor-based design approach and has applied it to numerous reactions. In several publications his group has studied the methanation reaction (CO + 2H2 -> CH4 + H2O), starting from a detailed electronic structure analysis and leading to the development of a patented technical methanation catalyst based on a Fe/Ni alloy. In the beginning of any descriptor-based design study one must first answer the question: "What is the most suitable reactivity descriptor for the reaction?" This question is typically answered by thoroughly studying the underlying reaction mechanism and identifying the rate-limiting step and most abundant surface intermediates. However, intuition can sometimes replace a detailed mechanistic study and a descriptor can be found through an educated guess. In the case of the methanation reaction, CO dissociation is the most critical step in the reaction mechanism. For weakly interacting metal catalysts, the dissociation is rate limiting,...