Machine Learning-Guided Development of Trialkylphosphine Ni(I) Dimers and Applications in Site-Selective Catalysis.

Owing to the unknown correlation of a metal's ligand and its resulting preferred speciation in terms of oxidation state, geometry, and nuclearity, a rational design of multinuclear catalysts remains challenging. With the goal to accelerate the identification of suitable ligands that form trialkylphosphine-derived dihalogen-bridged Ni(I) dimers, we herein employed an assumption-based machine learning approach. The workflow offers guidance in ligand space for a desired speciation without (or only minimal) prior experimental data points. We experimentally verified the predictions and synthesized numerous novel Ni(I) dimers as well as explored their potential in catalysis. We demonstrate C-I selective arylations of polyhalogenated arenes bearing competing C-Br and C-Cl sites in under 5 min at room temperature using 0.2 mol % of the newly developed dimer, [Ni(I)(µ-Br)PAd2(n-Bu)]2, which is so far unmet with alternative dinuclear or mononuclear Ni or Pd catalysts.

Solvation Layer of Antifreeze Proteins Analyzed with a Markov State Model.

Three structurally very different antifreeze proteins (AFPs) are studied, addressing the question as to what extent the hypothesized preordering-binding mechanism is still relevant in the second solvation layer of the protein and beyond. Assuming a two-state model of water, the solvation layers are analyzed with the help of molecular dynamics simulations together with a Markov state model, which investigates the local tedrahedrality of the water hydrogen-bond network around a given water molecule. It has been shown previously that this analysis can discriminate the high-entropy, high-density state of the liquid (HDL) from its more structured low-density state (LDL). All investigated proteins, regardless of whether they are an AFP or not, have a tendency to increase the amount of HDL in their second solvation layer. The ice binding site (IBS) of the antifreeze proteins counteracts that trend, with either a hole in the HDL layer or a true excess of LDL. The results correlate to a certain extent with recent experiments, which have observed ice-like vibrational (VSFG) spectra for the water atop the IBS of only a subset of antifreeze proteins. It is concluded that the preordering-binding mechanism indeed seems to play a role but is only part of the overall picture.