Catalysis and Chemical Reactivity
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)
Thin film deposition by atomic layer deposition (ALD) is under widespread development for semiconductor device fabrication. ALD affords uniform, conformal thin films with thickness control at the atomic level through the cycling of self-limiting surface reactions. The initial deposition of Al2O3 on hydrogen-terminated silicon by ALD could require nucleation through exposure to either the Al- or O-precursor (Al(CH3)3 or H2O, respectively. To determine the differential reactivity for Al2O3 nucleation on H/Si, the kinetic barrier for initial reaction between Al(CH3)3 and H2O with H/Si was calculated with DFT using Jaguar.
The calculated kinetic barriers for Al(CH3)3 and H2O with H/Si indicate significant preference for Al(CH3)3. The ∆G‡ for the Al-precursor reaction is predicted to be lower than the O-precursor reaction by 7.7 kcal/mol (using M06-L/6-31G** at 300 K and 1 atm), transition state shown in Figure 3 (right). The kinetic selectivity for the Al reaction over the O reaction on H/Si is presented in Figure 3 (left), over a range of temperatures (1 atm); showing the kinetic preference for Al(CH3)3 in excellent agreement with experimental observations. Chabal and co-workers4 carried out an in situ infrared study of the ALD nucleation of Al2O3 in H/Si using Al(CH3)3 and H2O. They observed only reaction with Al(CH3)3 and not with H2O and the silicon substrate, remarking, “Contrary to common belief, we find that the metal precursor, not the oxidizing agent, is the key factor to control Al2O3 nucleation on hydrogen-terminated silicon.”
Figure 3. Al(CH3)3:H2O relative kinetic selectivity for reaction with H/Si(111) across a range of temperatures (left). Transition state structure for TMA + H/Si (right). (-∆∆G‡in kcal/mol; computed with M06-L/LACVP** at 100 - 500 K, 1 atm)
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.
- T.J.L. Mustard, H.S. Kwak, A. Goldberg, J.L. Gavartin, T. Morisato, D. Yoshidome, and M.D. Halls, “Quantum Mechanical Simulation for the Analysis, Optimization and Accelerated Development of Precursors and Processes for Atomic Layer Deposition (ALD)”, Special Issue: Recent Advances in Computational Materials Science; Journal of the Korean Ceramic Society, 53(3), 317 (2016).
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
- Professor William A. Goddard, California Institute of Technology, USA
- Professor Charles H. Winter, Wayne State University, USA
- 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).
- M.M Frank, Y.J. Chabal and G.D. Wilk, “Nucleation and Interface Formation Mechanisms in Atomic Layer Deposition of Gate Oxides”, Appl. Phys. Lett., Appl. Phys. Lett., 82, 4758 (2003).