A Multiscale Guide to Brownian Motion

We revise the Levy's construction of Brownian motion as a simple though still rigorous approach to operate with various Gaussian processes. A Brownian path is explicitly constructed as a linear combination of wavelet-based "geometrical features" at multiple length scales with random weights. Such a wavelet representation gives a closed formula mapping of the unit interval onto the functional space of Brownian paths. This formula elucidates many classical results about Brownian motion (e.g., non-differentiability of its path), providing intuitive feeling for non-mathematicians. The illustrative character of the wavelet representation, along with the simple structure of the underlying probability space, is different from the usual presentation of most classical textbooks. Similar concepts are discussed for fractional Brownian motion, Ornstein-Uhlenbeck process, Gaussian free field, and fractional Gaussian fields. Wavelet representations and dyadic decompositions form the basis of many highly efficient numerical methods to simulate Gaussian processes and fields, including Brownian motion and other diffusive processes in confining domains

Unsupervised landmark analysis for jump detection in molecular dynamics simulations

Molecular dynamics is a versatile and powerful method to study diffusion in solid-state ionic conductors, requiring minimal prior knowledge of equilibrium or transition states of the system's free energy surface. However, the analysis of trajectories for relevant but rare events, such as a jump of the diffusing mobile ion, is still rather cumbersome, requiring prior knowledge of the diffusive process in order to get meaningful results. In this work, we present a novel approach to detect the relevant events in a diffusive system without assuming prior information regarding the underlying process. We start from a projection of the atomic coordinates into a landmark basis to identify the dominant features in a mobile ion's environment. Subsequent clustering in landmark space enables a discretization of any trajectory into a sequence of distinct states. As a final step, the use of the smooth overlap of atomic positions descriptor allows distinguishing between different environments in a straightforward way. We apply this algorithm to ten Li-ionic systems and conduct in-depth analyses of cubic Li$_{7}$La$_{3}$Zr$_{2}$O$_{12}$, tetragonal Li$_{10}$GeP$_{2}$S$_{12}$, and the $\beta$-eucryptite LiAlSiO$_{4}$. We compare our results to existing methods, underscoring strong points, weaknesses, and insights into the diffusive behavior of the ionic conduction in the materials investigated

IST Austria Thesis

In this Thesis, I study composite quantum impurities with variational techniques, both inspired by machine learning as well as fully analytic. I supplement this with exploration of other applications of machine learning, in particular artificial neural networks, in many-body physics. In Chapters 3 and 4, I study quasiparticle systems with variational approach. I derive a Hamiltonian describing the angulon quasiparticle in the presence of a magnetic field. I apply analytic variational treatment to this Hamiltonian. Then, I introduce a variational approach for non-additive systems, based on artificial neural networks. I exemplify this approach on the example of the polaron quasiparticle (Fröhlich Hamiltonian). In Chapter 5, I continue using artificial neural networks, albeit in a different setting. I apply artificial neural networks to detect phases from snapshots of two types physical systems. Namely, I study Monte Carlo snapshots of multilayer classical spin models as well as molecular dynamics maps of colloidal systems. The main type of networks that I use here are convolutional neural networks, known for their applicability to image data

Nucleation of a sodium droplet on C60

We investigate theoretically the progressive coating of C60 by several sodium atoms. Density functional calculations using a nonlocal functional are performed for NaC60 and Na2C60 in various configurations. These data are used to construct an empirical atomistic model in order to treat larger sizes in a statistical and dynamical context. Fluctuating charges are incorporated to account for charge transfer between sodium and carbon atoms. By performing systematic global optimization in the size range 1<=n<=30, we find that Na_nC60 is homogeneously coated at small sizes, and that a growing droplet is formed above n=>8. The separate effects of single ionization and thermalization are also considered, as well as the changes due to a strong external electric field. The present results are discussed in the light of various experimental data.Comment: 17 pages, 10 figure

Spectral modeling of type II supernovae. I. Dilution factors

We present substantial extensions to the Monte Carlo radiative transfer code TARDIS to perform spectral synthesis for type II supernovae. By incorporating a non-LTE ionization and excitation treatment for hydrogen, a full account of free-free and bound-free processes, a self-consistent determination of the thermal state and by improving the handling of relativistic effects, the improved code version includes the necessary physics to perform spectral synthesis for type II supernovae to high precision as required for the reliable inference of supernova properties. We demonstrate the capabilities of the extended version of TARDIS by calculating synthetic spectra for the prototypical type II supernova SN1999em and by deriving a new and independent set of dilution factors for the expanding photosphere method. We have investigated in detail the dependence of the dilution factors on photospheric properties and, for the first time, on changes in metallicity. We also compare our results with two previously published sets of dilution factors by Eastman et al. (1996) and by Dessart & Hillier (2005), and discuss the potential sources of the discrepancies between studies.Comment: 16 pages, 12 figures, 2 tables, accepted for publication in A&