Density functional benchmarks for methylaluminoxane: successes, biases, and transferability limits | JoVE Visualize

Area of Science:

Computational chemistry
Catalysis science
Polymer chemistry

Background:

Methylaluminoxane (MAO) is a vital cocatalyst in olefin polymerization.
The precise molecular structure and reactivity of MAO are not fully understood.
Existing computational methods for MAO lack sufficient benchmarking, hindering accurate predictions.

Purpose of the Study:

To comprehensively benchmark 25 density functional methods for their accuracy in describing methylaluminoxane (MAO) oligomers.
To evaluate the performance of these methods for geometry, electronic energies, vibrational frequencies, and thermodynamic properties.
To identify reliable computational approaches for studying MAO structure and reactivity in olefin polymerization.

Main Methods:

Benchmarking 25 density functional theory (DFT) methods against high-level DLPNO-CCSD(T) and RI-MP2 reference calculations.
Evaluation of MAO oligomers, focusing on geometry, electronic energies, vibrational frequencies, and thermodynamic properties.
Analysis of energetic biases and structural preferences of different DFT functionals.

Main Results:

Ten DFT functionals exhibit significant energetic biases towards specific MAO structural motifs (µ4-O and µ-Me), with errors up to 45 kJ mol⁻¹.
ωB97X-D4 and MN15 functionals show the largest errors and are unsuitable for pure MAO systems.
ωB97X-V and ωB97M-D4 functionals accurately reproduce electronic energies without structural bias for MAO oligomers.
Quasi-harmonic treatment effectively minimizes method-dependent entropy variations for vibrational properties.
Extending calculations to metallocene-MAO ion pairs revealed system-dependent functional behavior.

Conclusions:

Functional performance in computational studies of MAO is highly system-dependent.
ωB97X-V and ωB97M-D4 are recommended for accurate electronic energy calculations of MAO oligomers.
Further investigation is needed for metallocene-MAO systems due to observed distinct behaviors.
These findings have significant implications for selecting appropriate computational methods to study catalyst activation mechanisms in olefin polymerization.