Rotationally resolved electronic spectroscopy of the rotamers of 1,3-dimethoxybenzene

Rotamers of p‑isopropylphenol studied by hole-burning resonantly enhanced multiphoton ionization and mass analyzed threshold ionization spectroscopy

The resonance enhanced multiphoton ionization (REMPI), ultraviolet-ultraviolet (UV-UV) hole burning and mass analyzed threshold ionization (MATI) spectroscopy have been applied to investigate the vibrational features of p‑isopropylphenol in its first electronically excited state S₁ and cationic ground state D₀. Two stable conformational structures of p‑isopropylphenol are distinctly found in the supersonic molecular beam and identified as the cis and trans rotamers through REMPI and UV-UV hole burning spectroscopy. The electronic excitation energies of S₁ ← S₀ transition of two rotamers are determined to be 35,578 and 35,593 cm^-1, and the adiabatic ionization energies are 65,331 and 65,350 cm^-1, respectively. The MATI spectra recorded via different intermediate levels of S₁ state indicate the similarity in the molecular geometry between the S₁ state and the D₀ state for each rotamer of p‑isopropylphenol. Geometrical optimizations of p‑isopropylphenol have also been performed using the density functional theory (DFT) for S₀ and D₀ states, and time-dependent density functional theory (TDDFT) for S₁ state. The simulated spectra for S₁ ← S₀ and D₀ ← S₁ transitions of two rotamers are able to reproduce qualitatively the experimental spectral profile, which help us to assign the vibronic modes. Most of the observed vibrations of two rotamers in the S₁ and D₀ states are related to the in-plane ring deformation and some active modes involving isopropyl group.

Phoenics: A Bayesian Optimizer for Chemistry

We report Phoenics, a probabilistic global optimization algorithm identifying the set of conditions of an experimental or computational procedure which satisfies desired targets. Phoenics combines ideas from Bayesian optimization with concepts from Bayesian kernel density estimation. As such, Phoenics allows to tackle typical optimization problems in chemistry for which objective evaluations are limited, due to either budgeted resources or time-consuming evaluations of the conditions, including experimentation or enduring computations. Phoenics proposes new conditions based on all previous observations, avoiding, thus, redundant evaluations to locate the optimal conditions. It enables an efficient parallel search based on intuitive sampling strategies implicitly biasing toward exploration or exploitation of the search space. Our benchmarks indicate that Phoenics is less sensitive to the response surface than already established optimization algorithms. We showcase the applicability of Phoenics on the Oregonator, a complex case-study describing a nonlinear chemical reaction network. Despite the large search space, Phoenics quickly identifies the conditions which yield the desired target dynamic behavior. Overall, we recommend Phoenics for rapid optimization of unknown expensive-to-evaluate objective functions, such as experimentation or long-lasting computations.