![]() Thus, any apparent errors larger than a few kcal mol –1 in any reaction step would indicate significant errors in solvation energies. We first assumed that high-level DLPNO-CCSD(T) theory with a relatively large triple-zeta basis set should provide fairly accurate gas phase reaction energies. We formulated the procedure by calibrating to previously reported studies on the MBH reaction in order to understand how to navigate modeling pitfalls that face static models for reaction mechanisms in solvents. The paramedic and static quantum chemistry procedure will be more computationally demanding than static studies using a CSM with no explicit solvent, but it can also be expected to require less computational effort than many dynamics-based schemes (see below). ![]() These two important studies have explained the elementary steps of the acid catalyzed MBH reaction mechanism, demonstrated the importance of critically evaluating computational theory to experiment, and discussed the extent that computational modeling can be predictive.īuilding from those studies, we now show how one can model such a mechanism with an automatable and paramedic 16 modeling procedure that is enhanced with chemical intuition but also lessens the need for it. (They used a CSM treatment in systems where molecular mechanics was not possible). 11 Their calculations used the high-level correlated wavefunction method DLPNO-CCSD(T) 12– 15 for electronic energies and usually an explicit solvation treatment with molecular mechanics. Harvey and Sunoj have since evaluated various quantum chemistry modeling schemes and assembled a mechanistic picture that agrees well with Plata and Singleton's reported mechanism. However, Plata and Singleton's detailed study of the Morita–Baylis–Hillman (MBH) reaction 10 has underscored poor performances of CSM-based quantum chemistry modeling without explicit solvation. Recent developments of CSMs under periodic boundary conditions 7– 9 have excitingly opened avenues for efficient atomic scale studies of reaction mechanisms at solid/liquid interfaces as well. In contrast, many prefer using computationally inexpensive static quantum chemistry schemes with continuum solvation models (CSMs), 4 e.g. While such efforts can be very insightful, they can also bring very large computational costs and/or technical challenges that restrict their use. metadynamics, 1 transition path sampling, 2 or umbrella sampling 3 schemes that involve large numbers of electronic structure, QM/MM, or molecular mechanics calculations. The most reliable and robust schemes usually involve dynamics-based treatments with explicit solvation models, e.g. ![]() Thus, it should be a useful and computationally cost-effective approach for modeling solvent mediated reaction mechanisms when dynamics simulations are not possible.Ĭomputationally modeling atomic scale chemical reaction mechanisms in solvents is often not trivial. ![]() This new paramedic approach can promisingly capture essential physical chemistry of the complicated and multistep MBH reaction mechanism, and the energy profiles found with this model appear reasonably insensitive to the level of theory used for energy calculations. In doing so, we explain unphysical pitfalls that obfuscate computational modeling that uses microsolvated reaction intermediates. Testing this approach on the acid-catalyzed Morita–Baylis–Hillman (MBH) reaction in methanol, we found a reaction mechanism that is consistent with both recent experiments and computationally intensive dynamics simulations with explicit solvation. This modeling scheme uses a global optimization procedure to identify low energy intermediate states with different numbers of explicit solvent molecules and then the growing string method to locate sequential transition states along a reaction pathway. We report a static quantum chemistry modeling treatment to study how solvent molecules affect chemical reaction mechanisms without dynamics simulations.
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