Catalysis and Chemical Reactivity
Background
Advances in the capability and efficiency of quantum mechanics programs and the improvement in computer performance has pushed the applicability of first-principles simulation from the small molecule domain to the study of chemically realistic systems with high accuracy. In addition to furnishing atomistic details for reaction mechanisms, quantum mechanics-based simulation (e.g. density functional theory, DFT) enables the calculation of energetics and properties with an accuracy comparable to experiment. DFT simulation is a critical tool for catalysis and reactivity; improving the understanding of structure-reactivity relationships, providing invaluable details about productive and failure chemistries, and furnishing insight required for process optimization and control. Even more compelling is the in silico design of catalysts and reactive precursors with enhanced or highly differentiated reactivity. Schrödinger’s Materials Science Suite has unique model builders, an extremely efficient DFT engine, Jaguar1,2, automated DFT-based reactivity workflows, and analysis tools for the simulation, optimization, and discovery of effective, efficient, selective catalysts and reactive systems.
For homogeneous catalysis, DFT can provide the fundamental understanding needed to enable the rational modification of a catalyst to achieve desired increases in reactivity and chemo-, regio-, and stereo-selectivity. Despite their importance in a range of applications, fluorinated aromatic molecules are difficult to synthesize; recently a Pd-catalyst to convert aryl bromides and aryl triflates to aryl fluorides was reported.3 Room temperature reaction conditions can now be used due to a novel fluorinated ligand, however the underlying rationale leading to the observed reactivity is not fully understood. The insight provided by DFT analysis for catalytic reactions is illustrated here for this transformation.
The complete reaction pathway for the Pd-mediated fluorination of p-tolyl bromide using the reference ligand 1 was investigated using Jaguar as shown in Figure 1. The catalytic cycle for this process begins with the substrate-catalyst complex I. Oxidative addition TS-II of the Pd center into the C-Br bond leads to the aryl-bromide intermediate III. Transmetalation TS-IV replaces the Br for F to give the aryl fluoride intermediate V. The rate determining reductive elimination TS-VI step releases the Pd center and creates the C-F bond to afford the product-catalyst complex VII. Transfer of the catalyst to another substrate releases the product and regenerates the catalyst.
Figure 1. Complete reaction pathway for the Pd-mediated fluorination of p-tolyl bromide, calculated using DFT. The rate determining step is between the aryl fluoride intermediate V and the reductive elimination TS-VI. (∆G in kcal/mol; computed using B3LYP/LACVP* at 298.15 K, 1 atm)
Once the reaction mechanism is fully elucidated and rate determining TS identified, catalyst derivatives can easily be evaluated for reactivity and selectivity. As shown in Figure 2, the rate determining step was computed for two ligands, 2 and 3, and their kinetic barriers are presented in Figure 2. Addition of an aryl group (2) in the 3` position is found to hinder activity by increasing the internal barrier, whereas the electron withdrawing effect of the perfluorinated 3` aryl group (3) leads to more favorable kinetics with an activation energy 2 kcal/mol lower than the aryl-substituted catalyst; ranking catalyst candidates in agreement with the experimental report by Buchwald and co-workers.3
Figure 2. Comparison of rate determining reductive elimination TS barriers between three ligands, the reference ligand 1, arylated ligand 2, and perfluoroarylated ligand 3. (∆G in kcal/mol; computed with B3LYP/LACVP* at 298.15 K, 1 atm)
Conclusion
The rate of reaction, mechanistic path, and selectivities for a target reaction are directly determined by the free-energies of the critical point structures defining a particular reaction pathway. The diversity and complexity of the chemical mechanisms and pathways reflect the complexity and heterogeneity of the catalyst, substrate, and/or precursor architecture. This chemical diversity provides great opportunity for chemical design to achieve the enhanced reactivity needed for reactive processes with improved performance. Comparison of competing reaction pathways reveals differential reactivity that can be exploited in reactive process engineering. DFT simulation using Schrödinger’s Materials Science Suite is a powerful tool for analysis, optimization, and discovery. Automated reactivity workflows, such as Reaction Energy Enumeration, Reaction Channel Enumeration, and AutoTS strengthen and extend the role of quantum mechanics-based simulation for the optimization and discovery of new metal-ligand architectures and functional co-reactants with enhanced reactivity and selectivity, informing the development of enhanced catalysts and reactive precursors and processes.
Read More
- T. J. L. Mustard, A. D. Bochevarov, L. D. Jacobson, T. F. Hughes, H. S. Kwak, T. Morisato, J. L. Gavartin, S. Pandiyan, and M. D. Halls "In silico reaction and catalyst design for enhanced reactivity and tailored chemo-, regio-, and stereo- selectivity", Poster presented at: NAM25 The North American Catalysis Society Meeting 2017 Jun 4-9; Denver, CO.
See for example:
- A.V. Sberegaeva, W.G. Liu, R.J. Nielsen, W.A. Goddard III, and A.N. Vedernikov, “Mechanistic Study of the Oxidation of a Methyl Platinum(II) Complex with O2 in Water: PtIIMe-to-PtIVMe and PtIIMe-to-PtIVMe2 Reactivity”, J Am Chem Soc, 136(12), 4761-8 (2014).
- M.J. Cheng, R.J. Nielsen, and W.A. Goddard III. “A homolytic oxy-functionalization mechanism: intermolecular hydrocarbyl migration from M–R to vanadate oxo”, Chemical Communications, 50(75), 10994, (2014).
- M. Ahlquist, R.J. Nielsen, R.A. Periana, and W.A. Goddard III, “Product Protection, the Key to Developing High Performance Methane Selective Oxidation Catalysts”, Journal of the American Chemical Society, 131(47), 17110 (2009).
- R.J. Nielsen, J.M. Keith, B.M. Stoltz, and W.A. Goddard, “A Computational Model Relating Structure and Reactivity in Enantioselective Oxidations of Secondary Alcohols by (−)-Sparteine−PdII Complexes”, Journal of the American Chemical Society, 126(25), 7967 (2004).
Collaborators and Advisors
References
- The Jaguar DFT engine is highly computationally efficient for large chemical systems due to use of the pseudospectral (PS) method, a numerical approach to the calculation of the Coulomb and exchange terms, which provides particularly significant advantages for the computation of exact exchange terms; and efficient parallelization over a large number of processors using OpenMP; B.H. Greeley, T.V. Russo, D.T. Mainz, R.A. Friesner, J.‐M. Langlois, W.A. Goddard III, R.E. Donnelly Jr. and M.N. Ringnalda, “New Pseudospectral Algorithms for Electronic Structure Calculations: Length Scale Separation and Analytical Two‐Electron Integral Corrections”, J. Chem. Phys., 101, 4028 (1994).
- Jaguar: A.D. Bochevarov, E. Harder, T.F. Hughes, J.R. Greenwood, D. Braden, D. M. Philipp, D. Rinaldo, M.D. Halls, J. Zhang and R.A. Friesner, “Jaguar: A High-Performance Quantum Chemistry Software Program with Strengths in Life and Materials Sciences”, Int. J. Quantum Chem., 113, 2110 (2013).
- A.C. Sather, H.G. Lee, V.Y. De La Rosa, Y. Yang, P. Müller and S.L. Buchwald, “A Fluorinated Ligand Enables Room-Temperature and Regioselective Pd-Catalyzed Fluorination of Aryl Triflates and Bromides”, J. Am. Chem. Soc., 137(41), 13433 (2015).
