Exploration of Chemical Compound, Conformer, and Reaction Space with Meta-Dynamics Simulations Based on Tight-Binding Quantum Chemical Calculations

The semiempirical
tight-binding based quantum chemistry method
GFN2-xTB is used in the framework of meta-dynamics (MTD) to globally
explore chemical compound, conformer, and reaction space. The biasing
potential given as a sum of Gaussian functions is expressed with the
root-mean-square-deviation (RMSD) in Cartesian space as a metric for
the collective variables. This choice makes the approach robust and
generally applicable to three common problems (i.e., conformer search,
chemical reaction space exploration in a virtual nanoreactor, and
for guessing reaction paths). Because of the inherent locality of
the atomic RMSD, functional group or fragment selective treatments
are possible facilitating the investigation of catalytic processes
where, for example, only the substrate is thermally activated. Due
to the approximate character of the GFN2-xTB method, the resulting
structure ensembles require further refinement with more sophisticated,
for example, density functional or wave function theory methods. However,
the approach is extremely efficient running routinely on common laptop
computers in minutes to hours of computation time even for realistically
sized molecules with a few hundred atoms. Furthermore, the underlying
potential energy surface for molecules containing almost all elements
(Z = 1–86) is globally consistent including
the covalent dissociation process and electronically complicated situations
in,
for example, transition metal systems. As examples, thermal decomposition,
ethyne oligomerization, the oxidation of hydrocarbons (by oxygen and
a P450 enzyme model), a Miller-Urey model system, a thermally forbidden
dimerization, and a multistep intramolecular cyclization reaction
are shown. For typical conformational search problems of organic drug
molecules, the new MTD­(RMSD) algorithm yields lower energy structures
and more complete conformer ensembles at reduced computational effort
compared with its already well performing predecessor.