arXiv subjects

Ursula Röthlisberger

Publications and source records attributed to Ursula Röthlisberger.

3 recordsLinked to original sources

arXiv2022

Hydration Dynamics and IR Spectroscopy of 4-Fluorophenol

Halogenated groups are relevant in pharmaceutical applications and potentially useful spectroscopic probes for infrared spectroscopy. In this work, the structural dynamics and infrared spectroscopy of $para$-fluorophenol (F-PhOH) and phenol (PhOH) is investigated in the gas phase and in water using a combination of experiment and molecular dynamics (MD) simulations. The gas phase and solvent dynamics around F-PhOH and PhOH is characterized from atomistic simulations using empirical energy functions with point charges or multipoles for the electrostatics, Machine-Learning (ML) based parametrization and with full $\textit{ab initio}$ (QM) and mixed Quantum Mechanical/Molecular Mechanics (QM/MM) simulations with a particular focus on the CF- and OH-stretch region. The CF-stretch band is heavily mixed with other modes whereas the OH-stretch in solution displays a characteristic high-frequency peak around 3600 cm$^{-1}$ most likely associated with the -OH group of PhOH and F-PhOH together with a characteristic progression below 3000 cm$^{-1}$ due to coupling with water modes which is also reproduced by several of the simulations. Solvent and radial distribution functions indicate that the CF-site is largely hydrophobic except for simulations using point charges which renders them unsuited for correctly describing hydration and dynamics around fluorinated sites.

physics.chem-ph↗

arXiv2021

Photo de-mixing in Dion-Jacobson two-dimensional mixed halide perovskites

Two-dimensional (2D) halide perovskites feature a versatile structure, which not only enables the fine-tuning of their optoelectronic properties but also makes them appealing as model systems to investigate the fundamental properties of hybrid perovskites. In this study, we analyzed the changes in the optical absorption of 2D Dion-Jacobson mixed halide perovskite thin films (encapsulated) based on (PDMA)Pb(I0.5Br0.5)4 (PDMA: 1,4-phenylenedimethanammonium spacer) exposed to a constant illumination. We demonstrate that these 2D mixed-halide perovskites undergo photo de-mixing with direct transformation from the pristine phase to the de-mixed phases. Almost complete re-mixing of these phases occurs when the sample is left in the dark, showing that the process is reversible in terms of optical properties. On the other hand, exposure to light appears to induce structural changes in the thin film that are not reversible in the dark. We have further investigated temperature-dependent absorption measurements under light to extract the photo de-mixed compositions and to map the photo-miscibility-gap. This work thereby reveals that photo de-mixing occurs in Dion-Jacobson two-dimensional hybrid perovskites and provides strategies to address the role of light in the thermodynamic properties of these materials.

physics.chem-ph↗

arXiv2016

Genetic optimization of training sets for improved machine learning models of molecular properties

The training of molecular models of quantum mechanical properties based on statistical machine learning requires large datasets which exemplify the map from chemical structure to molecular property. Intelligent a priori selection of training examples is often difficult or impossible to achieve as prior knowledge may be sparse or unavailable. Ordinarily representative selection of training molecules from such datasets is achieved through random sampling. We use genetic algorithms for the optimization of training set composition consisting of tens of thousands of small organic molecules. The resulting machine learning models are considerably more accurate with respect to small randomly selected training sets: mean absolute errors for out-of-sample predictions are reduced to ~25% for enthalpies, free energies, and zero-point vibrational energy, to ~50% for heat-capacity, electron-spread, and polarizability, and by more than ~20% for electronic properties such as frontier orbital eigenvalues or dipole-moments. We discuss and present optimized training sets consisting of 10 molecular classes for all molecular properties studied. We show that these classes can be used to design improved training sets for the generation of machine learning models of the same properties in similar but unrelated molecular sets.

physics.comp-ph↗