Combining density functional theory (DFT) and collision cross-section (CCS) calculations to analyze the gas-phase behaviour of small molecules and their protonation site isomers.

Electrospray ion mobility-mass spectrometry (IM-MS) data show that for some small molecules, two (or even more) ions with identical sum formula and mass, but distinct drift times are observed. In spite of showing their own unique and characteristic fragmentation spectra in MS/MS, no configurational or constitutional isomers are found to be present in solution. Instead the observation and separation of such ions appears to be inherent to their gas-phase behaviour during ion mobility experiments. The origin of multiple drift times is thought to be the result of protonation site isomers ('protomers'). Although some important properties of protomers have been highlighted by other studies, correlating the experimental collision cross-sections (CCSs) with calculated values has proven to be a major difficulty. As a model, this study uses the pharmaceutical compound melphalan and a number of related molecules with alternative (gas-phase) protonation sites. Our study combines density functional theory (DFT) calculations with modified MobCal methods (e.g. nitrogen-based Trajectory Method algorithm) for the calculation of theoretical CCS values. Calculated structures can be linked to experimentally observed signals, and a strong correlation is found between the difference of the calculated dipole moments of the protomer pairs and their experimental CCS separation.

On the weakly C-H···π hydrogen bonded complexes of sevoflurane and benzene.

A vibrational assignment of the anaesthetic sevoflurane, (CF(3))(2)CHOCH(2)F, is proposed and its interaction with the aromatic model compound benzene is studied using vibrational spectroscopy of supersonic jet expansions and of cryosolutions in liquid xenon. Ab initio calculations, at the MP2/cc-pVDZ and MP2/aug-cc-pVDZ levels, predict two isomers for the 1 : 1 complex, one in which the near-cis, gauche conformer of sevoflurane is hydrogen bonded through its isopropyl-hydrogen atom, the other in which the same conformer is bonded through a bifurcated hydrogen bond with the fluoromethyl hydrogen atoms. From the experiments it is shown that the two isomers are formed, however with a strong population dominance of the isopropyl-bonded species, both in the jet and liquid phase spectra. The experimental complexation enthalpy in liquid xenon, ΔH(o)(LXe), of this species equals -10.9(2) kJ mol(-1), as derived from the temperature dependent behaviour of the cryosolution spectra. Theoretical complexation enthalpies in liquid xenon were obtained by combining the complete basis set extrapolated complexation energies at the MP2/aug-cc-pVXZ (X = D,T) level with corrections derived from statistical thermodynamics and Monte Carlo Free Energy Perturbation calculations, resulting in a complexation enthalpy of -11.2(3) kJ mol(-1) for the isopropyl-bonded complex, in very good agreement with the experimental value, and of -11.4(4) kJ mol(-1), for the fluoromethyl-bonded complex. The Monte Carlo calculations show that the solvation entropy of the isopropyl-bonded species is considerably higher than that of the fluoromethyl-bonded complex, which assists in explaining its dominance in the liquid phase spectra.

Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications.

With the rapid adoption of machine learning techniques for large-scale applications in science and engineering comes the convergence of two grand challenges in visualization. First, the utilization of black box models (e.g., deep neural networks) calls for advanced techniques in exploring and interpreting model behaviors. Second, the rapid growth in computing has produced enormous datasets that require techniques that can handle millions or more samples. Although some solutions to these interpretability challenges have been proposed, they typically do not scale beyond thousands of samples, nor do they provide the high-level intuition scientists are looking for. Here, we present the first scalable solution to explore and analyze high-dimensional functions often encountered in the scientific data analysis pipeline. By combining a new streaming neighborhood graph construction, the corresponding topology computation, and a novel data aggregation scheme, namely topology aware datacubes, we enable interactive exploration of both the topological and the geometric aspect of high-dimensional data. Following two use cases from high-energy-density (HED) physics and computational biology, we demonstrate how these capabilities have led to crucial new insights in both applications.