Direction for the Future - Successive Acceleration of Positive and Negative Ions Applied to Space Propulsion

Electrical space thrusters show important advantages for applications in outer space compared to chemical thrusters, as they allow a longer mission lifetime with lower weight and propellant consumption. Mature technologies on the market today accelerate positive ions to generate thrust. The ion beam is neutralized by electrons downstream, and this need for an additional neutralization system has some drawbacks related to stability, lifetime and total weight and power consumption. Many new concepts, to get rid of the neutralizer, have been proposed, and the PEGASES ion-ion thruster is one of them. This new thruster concept aims at accelerating both positive and negative ions to generate thrust, such that additional neutralization is redundant. This chapter gives an overview of the concept of electric propulsion and the state of the development of this new ion-ion thruster.Comment: 10 pages, contribution to the CAS-CERN Accelerator School: Ion Sources, Senec, Slovakia, 29 May - 8 June 2012, edited by R. Bailey. appears in CERN Yellow Report CERN-2013-007, pp.575-58

Quantum Chemical Topology

Conferencia impartida por P. Popelier el 26 de abril de 2013. Popelier es Profesor de la Universidad de Manchester. Reino UnidoDepartamento de Química Física. Andalucía Tec

Calculation of Raman optical activity spectra for vibrational analysis

By looking back on the history of Raman Optical Activity (ROA), the present article shows that the success of this analytical technique was for a long time hindered, paradoxically, by the deep level of detail and wealth of structural information it can provide. Basic principles of the underlying theory are discussed, to illustrate the technique's sensitivity due to its physical origins in the delicate response of molecular vibrations to electromagnetic properties. Following a short review of significant advances in the application of ROA by UK researchers, we dedicate two extensive sections to the technical and theoretical difficulties that were overcome to eventually provide predictive power to computational simulations in terms of ROA spectral calculation. In the last sections, we focus on a new modelling strategy that has been successful in coping with the dramatic impact of solvent effects on ROA analyses. This work emphasises the role of complementarity between experiment and theory for analysing the conformations and dynamics of biomolecules, so providing new perspectives for methodological improvements and molecular modelling development. For the latter, an example of a next-generation force-field for more accurate simulations and analysis of molecular behaviour is presented. By improving the accuracy of computational modelling, the analytical capabilities of ROA spectroscopy will be further developed so generating new insights into the complex behaviour of molecules

Distinguishing epimers through raman optical activity

The Raman optical activity spectra of the epimers β-d-glucose and β-d-galactose, two monosaccharides of biological importance, have been calculated using molecular dynamics combined with a quantum mechanics/molecular mechanics approach. Good agreement between theoretical and experimental spectra is observed for both monosaccharides. Full band assignments have been carried out, which has not previously been possible for carbohydrate epimers. For the regions where the spectral features are opposite in sign, the differences in the vibrational modes have been noted and ascribed to the band sign changes

The Raman optical activity of β-D-xylose: where experiment and theory meet

Besides its applications in bioenergy and biosynthesis, β-D-xylose is a very simple monosaccharide that exhibits relatively high rigidity. As such, it provides the best basis to study the impact of different solvation shell radii on the computation of its Raman optical activity (ROA) spectrum. Indeed, this chiroptical spectroscopic technique provides exquisite sensitivity to stereochemistry, and benefits much from theoretical support for interpretation. Our simulation approach combines density functional theory (DFT) and molecular dynamics (MD) in order to efficiently account for the crucial hydration effects in the simulation of carbohydrates and their spectroscopic response predictions. Excellent agreement between the simulated spectrum and the experiment was obtained with a solvation radius of 10 Å. Vibrational bands have been resolved from the computed ROA data, and compared with previous results on different monosaccharides in order to identify specific structure–spectrum relationships and to investigate the effect of the solvation environment on the conformational dynamics of small sugars. From the comparison with ROA analytical results, a shortcoming of the classical force field used for the MD simulations has been identified and overcome, again highlighting the complementary role of experiment and theory in the structural characterisation of complex biomolecules. Indeed, due to unphysical puckering, a spurious ring conformation initially led to erroneous conformer ratios, which are used as weights for the averaging of the spectral average, and only by removing this contribution was near perfect comparison between theory and experiment achieved

Quantifying Electron Correlation of the Chemical Bond

The Interacting Quantum Atoms (IQA) method is used to analyze the correlated part of the Møller–Plesset (MP) perturbation theory two-particle density matrix. Such an analysis determines the effects of electron correlation within atoms and between atoms, which covers both bonds and nonbonded through-space atom–atom interactions within a molecule or molecular complex. Electron correlation lowers the energy of the atoms at either end of a bond, but for the bond itself, it can be stabilizing or destabilizing. Bonds are described in a two-dimensional world of exchange and charge transfer, where covalency is not the opposite of ionicity

Calibration of uncertainty in the active learning of machine learning force fields

FFLUX is a machine learning force field that uses the maximum expected prediction error (MEPE) active learning algorithm to improve the efficiency of model training. MEPE uses the predictive uncertainty of a Gaussian process (GP) to balance exploration and exploitation when selecting the next training sample. However, the predictive uncertainty of a GP is unlikely to be accurate or precise immediately after training. We hypothesize that calibrating the uncertainty quantification within MEPE will improve active learning performance. We develop and test two methods to improve uncertainty estimates: post-hoc calibration of predictive uncertainty using the CRUDE algorithm, and replacing the GP with a student- t process. We investigate the impact of these methods on MEPE for single sample and batch sample active learning. Our findings suggest that post-hoc calibration does not improve the performance of active learning using the MEPE method. However, we do find that the student- t process can outperform active learning strategies and random sampling using a GP if the training set is sufficiently large