Selection of Low-Dimensional 3-D Geometric Descriptors for Accurate Enantioselectivity Prediction

Abstract

This study focuses on building enantioselectivity models using only a few intuitively meaningful descriptors based on the "buried volume" idea. Appropriate dissection of the sphere is used to calculate the buried volume of quadrants and octants, which we name %V BQ and %V BO . Propene polymerization catalysis to isotactic polypropylene (iPP) and 1,1′-bis-2-naphthol (BINOL)phosphoric acid-catalyzed thiol addition to N-acyl imines are used to illustrate the approach. For iPP, only a single steric descriptor derived from the comparison of hindrance in differently occupied octants (Δ%V BO ) is needed, and electronic effects are unimportant. Moreover, the model (mean absolute deviation, MAD, 0.12 kcal/ mol) works for more than a single catalyst class, allowing in silico catalyst design. For thiol addition, the best performance is achieved by comparison of hindrance in octants, and one steric descriptor is needed (Δ%V BO ) in addition to an electronic descriptor, the natural population analysis (NPA) charge on the P atom. In both cases, key ingredients are (a) the use of properly chosen "scanning regions" (e.g., octants or quadrants) and (b) the availability of highly accurate experimental data sets. The low dimensionality of descriptor space and their obvious intuitive meaning naturally provide guidelines for further catalyst optimization.

Manipulating pre-equilibria in olefin polymerization catalysis: backbone-stiffening converts a living into a highly active salan-type catalyst

“…38 A prolonged 1 H, 13 C HSQC NMR experiment (Fig. 4d), lasting several hours from the addition of 1-hexene, allowed The novel indanosalan catalyst rac-3 FF , the parent salan 1 and the ortho-N-carbazolyl salan 2, which is one of the most active salan [ONNO]Zr propene polymerization catalysts presently known and mildly isotactic selective, 6,39 have been tested in propene polymerization at 60 °C using MAO/ BHT 8,[40][41][42][43][44] as activator (Table 1) in a high-throughput experimentation platform (Freeslate parallel pressure reactor, PPR). [45][46][47][48][49] The polymerization procedure is reported in the ESI † and described in more detail in ref.…”

%VBur index and steric maps: from predictive catalysis to machine learning

“…Steric maps serve to highlight the catalyst's exposed surface facing the substrate, which can then undergo a more comprehensive analysis to uncover the physicochemical attributes of the catalytic pocket. [249][250][251] For instance, scrutinizing the catalytic pocket solely through its steric attributes falls short in elucidating the enantioselectivity in the asymmetric 1,4addition of phenylboronic acid to 2-cyclohexenone, resulting in chiral 3-phenylcyclohexanone, driven by Rh-catalysts led by Dorta and coworkers. 187 With two catalysts exhibiting remarkable enantioselectivity, with enantiomeric excesses of 90% and 99%, the great performance of the first was anticipated, however the even higher enantioselectivity of the second was unexpected, and posed a conundrum, considering that the enantioselective induction is expected to be influenced by the small, upward-oriented SQO moieties.…”

End-to-End High-Throughput Approach for Data-Driven Internal Donor Development in Heterogeneous Ziegler–Natta Propylene Polymerization

“…In our laboratories, we recently undertook a collaborative program for implementing advanced HTE tools and methods for the rapid exploration of the variables hyperspace in ZN PP catalysis. Our workflow permits an extensive coverage of the knowledge and value chains by means of unique tools for (pre)­catalyst preparation and analysis, robust protocols of catalyst screening under industrially relevant conditions, ,− and high-end rapid polymer characterizations. , Integration with ML instruments is now in progress.…”