Ivan A. Konstantinov

Regression formulas for density functional theory calculated 1H and 13C NMR chemical shifts in toluene-d8.

This study aimed at investigating the performance of a series of basis sets, density functional theory (DFT) functionals, and the IEF-PCM solvation model in the accurate calculation of (1)H and (13)C NMR chemical shifts in toluene-d(8). We demonstrated that, on a test set of 37 organic species with various functional moieties, linear scaling significantly improved the calculated shifts and was necessary to obtain more accurate results. Inclusion of a solvation model produced larger deviations from the experimental data as compared to the gas-phase calculations. Moreover, we did not find any evidence that very large basis sets were necessary to reproduce the experimental NMR data. Ultimately, we recommend the use of the BMK functional. For the (1)H shifts the use of the 6-311G(d) basis set gave linearly scaled mean unsigned (MU) and root-mean-square (rms) errors of 0.15 ppm and 0.21 ppm, respectively. For the calculation of the (13)C chemical shifts the 6-31G(d) basis set produced MUE of 1.82 ppm and RMSE of 3.29 ppm.

Assessment of a Cost-Effective Approach to the Calculation of Kinetic and Thermodynamic Properties of Methyl Methacrylate Homopolymerization: A Comprehensive Theoretical Study.

In this work, we carried out a comprehensive density functional theory (DFT) study on the basis of a trimer-to-tetramer radical reaction model to assess a cost-effective approach to perform the calculation of kinetic and thermodynamic properties of methyl methacrylate (MMA) free-radical homopolymerization. By comparing results from several different functionals (PBE, M06-2X, wB97XD, KMLYP, and MPW1B95), in conjunction with a series of basis sets (6-31G(d,p), 6-31+G(d,p), 6-31G(2df,p), 6-311G(d,p), 6-311+G(d,p), 6-311+G(2df,p), 6-311+G(2df,2p)), we show that calculations using M06-2X/6-311+G(2df,p)//B3LYP/6-31G(2df,p) provide an activation energy of 5.25 kcal mol(-1) for the homopropagation step, which is within 1 kcal mol(-1) of the experimental value. However, this method predicts a heat of polymerization of 17.37 kcal mol(-1) that is larger than the experimental value by 3.5 kcal mol(-1). MPW1B95/6-311+G(2df,p) on the B3LYP/6-31G(2df,p) geometries produces a heat of polymerization value within 1 kcal mol(-1) of experimental data, yet overestimates the activation energy by 3 kcal mol(-1). In addition, we evaluated the performance of ONIOM MO:MO calculations on the geometry optimization of species comprising our MMA polymerization model and found that ONIOM(B3LYP/6-31G(2df,p):B3LYP/6-31G(d)) is capable of producing geometries in very good agreement with the full B3LYP/6-31G(2df,p) calculations. Subsequent calculations of energies using M06-2X/6-311+G(2df,p) based on the ONIOM geometries provided an activation energy value comparable to that based on the full B3LYP/6-31G(2df,p) geometries.

Reaction free energies in organic solvents: comparing different quantum mechanical methods

A variety of quantum mechanical (QM) methods, basis sets and solute cavity descriptions using the conductor-like polarisable continuum model for the modelling of reaction free energies in solution have been evaluated and compared. In order to test the performance of each QM level of theory and cavity model, five common types of organic reactions in different solvents have been considered. The study shows that the hybrid metageneralised-gradient approximation M05-2X functional, in conjunction with the MG3S basis set and UA0 or universal force field cavities, produces results in the best overall agreement with experimental data. In addition, within the UA0 formalism, explicit cavities should be built on the hydrogen atoms that undergo transformation during chemical reaction.

Accurate density functional theory (DFT) protocol for screening and designing chain transfer and branching agents for LDPE systems

In this work, a density functional theory (DFT) methodology was developed and validated against experimental data for relative hydrogen abstraction (Cs) and monomer reactivity ratio (r1) parameters associated with free radical polymerization. For hydrogen abstraction, we considered ethane, cyclohexane, 2-butanone, propylene, isobutene, isobutane and propanal while methyl methacrylate, vinyl acetate, 1-butene, propylene and isobutene were the molecules of choice for benchmarking r1. It was shown that the M06-2X/6-311+G(3df,2p)//B3LYP/6-31+G(d,p) level of theory along with the counterpoise correction for the basis set superposition error (BSSE) produced estimated values in excellent agreement with experimental data. The calculated parameters were within a factor of 1.5 from the experimental values. This translated into a maximum error of 0.32 kcal mol−1 in Gibbs free energy of activation difference. The only exception was Cs for ethane with an experimental-to-calculated ratio of 3.0. Even then, the DFT estimate was within the experimental error. Furthermore, the approach managed to capture a wide range of empirical parameters as well as distinguish between monomers with close values. This robust and computationally inexpensive method can be applied to elucidate the reactivity of much larger species of industrial importance and rationally design the next generation of branching and chain-transfer agents for low density polyethylene (LDPE) systems.

Application and comparison of derivative-free optimization algorithms to control and optimize free radical polymerization simulated using the kinetic Monte Carlo method

Abstract The diversity of the potential arrangements of multiple monomers along the length of polymer chains and their impact on polymer properties spark interest in the design of polymer sequence characteristics for particular applications. Kinetic Monte Carlo (KMC) is a technique that can track the explicit arrangement of monomers in the polymer chains, yet it is difficult to integrate with conventional gradient-based optimization algorithms that are typically invoked to design polymer properties. In this work, we applied and compared derivative-free optimization algorithms to incorporate KMC simulations and find synthesis conditions for achieving property targets and minimizing reaction time, advancing our ability to carry out the design of polymer microstructures and control polymerization processes.