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.