Singlet carbenes exhibit a divalent carbon atom whose valence shell contains only six electrons, four involved in bonding to two other atoms and the remaining two forming a non-bonding electron pair. These features render singlet carbenes so reactive that they were long considered too short-lived for isolation and direct characterization. This view changed when it was found that attaching the divalent carbon atom to substituents that are bulky and/or able to donate electrons produces carbenes that can be isolated and stored. N-heterocyclic carbenes are such compounds now in wide use, for example as ligands in metathesis catalysis. In contrast, oxygen-donor-substituted carbenes are inherently less stable and have been less studied. The pre-eminent case is hydroxymethylene, H-C-OH; although it is the key intermediate in the high-energy chemistry of its tautomer formaldehyde, has been implicated since 1921 in the photocatalytic formation of carbohydrates, and is the parent of alkoxycarbenes that lie at the heart of transition-metal carbene chemistry, all attempts to observe this species or other alkoxycarbenes have failed. However, theoretical considerations indicate that hydroxymethylene should be isolatable. Here we report the synthesis of hydroxymethylene and its capture by matrix isolation. We unexpectedly find that H-C-OH rearranges to formaldehyde with a half-life of only 2 h at 11 K by pure hydrogen tunnelling through a large energy barrier in excess of 30 kcal mol(-1).

The correct representation of solute-water interactions is essential for the accurate simulation of most biological phenomena. Several highly accurate quantum methods are available to deal with solvation by using both implicit and explicit solvents. So far, however, most evaluations of those methods were based on a single conformation, which neglects solute entropy. Here, we present the first test of a novel approach to determine hydration free energies that uses molecular mechanics (MM) to sample phase space and quantum mechanics (QM) to evaluate the potential energies. Free energies are determined by using re-weighting with the Non-Boltzmann Bennett (NBB) method. In this context, the method is referred to as QM-NBB. Based on snapshots from MM sampling and accounting for their correct Boltzmann weight, it is possible to obtain hydration free energies that incorporate the effect of solute entropy. We evaluate the performance of several QM implicit solvent models, as well as explicit solvent QM/MM for the blind subset of the SAMPL4 hydration free energy challenge. While classical free energy simulations with molecular dynamics give root mean square deviations (RMSD) of 2.8 and 2.3 kcal/mol, the hybrid approach yields an improved RMSD of 1.6 kcal/mol. By selecting an appropriate functional and basis set, the RMSD can be reduced to 1 kcal/mol for calculations based on a single conformation. Results for a selected set of challenging molecules imply that this RMSD can be further reduced by using NBB to reweight MM trajectories with the SMD implicit solvent model.

Machine learning (ML) techniques with the genetic algorithm (GA) have been applied to explore a polarizable force field parameters using only ab initio data from quantum mechanics (QM) calculations of molecular clusters at the MP2/6-31G(d,p), DFMP2(fc)/jul-cc-pVDZ, and DFMP2(fc)/jul-cc-pVTZ levels to predict experimental condensed phase properties (i.e., density and heat of vaporization). The performance of this ML/GA approach is demonstrated on 4,943 dimers electrostatic potentials and 1,250 clusters interaction energies for methanol. Excellent agreement between the training dataset from QM calculations and the optimized force field model can be achieved. Better results are achieved by introducing an offset factor during the machine learning process to compensate for the discrepancy of the QM calculated energy and the energy reproduced by optimized force field, where the offset factor maintain the local “shape” of the QM energy surface. Throughout the machine learning process, experimental observables were not involved in the objective function, but were only used for model validation. The best model, optimized from the QM data at the DFMP2(fc)/jul-cc-pVTZ level, appears to perform even better than the original AMOEBA force field (amoeba09.prm), which was optimized empirically to match liquid properties. The present effort shows the possibility of using machine learning techniques to develop descriptive polarizable force field using only QM data. The ML/GA strategy to optimize force field parameters described here could easily be extended to other molecular systems.