Thermal dissociation of dipositronium: path integral Monte Carlo approach

Path integral Monte Carlo simulation of the dipositronium "molecule" Ps$_2$ reveals its surprising thermal instability. Although, the binding energy is $\sim 0.4$ eV, due to the strong temperature dependence of its free energy Ps$_2$ dissociates, or does not form, above $\sim 1000$ K, except for high densities where a small fraction of molecules are in equilibrium with Ps atoms. This prediction is consistent with the recently reported first observation of stable Ps$_2$ molecules by Cassidy & Mills Jr., Nature {\bf 449}, 195 (07), and Phys.Rev.Lett. {\bf 100}, 013401 (08); at temperatures below 1000 K. The relatively sharp transition from molecular to atomic equilibrium, that we find, remains to be experimentally verified. To shed light on the origin of the large entropy factor in free energy we analyze the nature of interatomic interactions of these strongly correlated quantum particles. The conventional diatomic potential curve is given by the van der Waals interaction at large distances, but due to the correlations and high delocalization of constituent particles the concept of potential curve becomes ambiguous at short atomic distances.Comment: Submitted to the Physical Review Letter

Finite temperature quantum statistics of H3+_3^+ molecular ion

Full quantum statistical $NVT$ simulation of the five-particle system H$_3^+$ has been carried out using the path integral Monte Carlo method. Structure and energetics is evaluated as a function of temperature up to the thermal dissociation limit. The weakly density dependent dissociation temperature is found to be around $4000$ K. Contributions from the quantum dynamics and thermal motion are sorted out by comparing differences between simulations with quantum and classical nuclei. The essential role of the quantum description of the protons is established.Comment: submitted to the Journal of Chemical Physic

Big Data Preprocessing for Multivariate Time Series Forecast

Big data platforms alleviate collecting and organizing large datasets of varying content. A downside of this is the heavy preprocessing required to analyze their data by conventional analysis techniques. Especially time series data is found challenging to transform from platform-provided raw format into tables of feature and target values, required by supervised machine learning models. This thesis presents an experiment of preprocessing a data-platform-extracted collection of multivariate time series and forecasting it by machine learning models such as neural networks and support vector machines. Reviewed techniques of data preprocessing and time series analysis literature are utilized, but also custom solutions such as log level-based target variable, and valuedistribution-based feature elimination are developed. No significant forecasting accuracies are achieved, which indicates the difficulty of modelling big data. The expected reason for this is the inadequate validation of model parameters and preprocessing decisions, which would require excessive testing to improve.Big data -alustat helpottavat isojen datamäärien talletusta ja hallintaa. Niiden haittapuolena on kuitenkin laaja data-analyysiin vaadittava esikäsittelyn tarve, mikäli halutaan käyttää tavanomaisia analyysimenetelmiä. Erityisen haastavaksi todetaan aikasarjojen muuntaminen alustan tarjoamasta muodosta ohjatun koneoppimisen vaatimaan taulumuotoon, koostuen ennustettavasta kohdemuuttujasta sekä muista ominaisuusmuuttujista. Tässä tutkielmassa tutkitaan usean muuttujan aikasarjadatan esikäsittelyä, sekä käsitellyn datan ennustamista koneoppimismenetelmillä, kuten neuroverkoilla ja tukivektorimallinnuksella. Tutkimusmenetelmät perustuvat kirjallisuuteen datan esikäsittelystä ja aikasarja-analyysistä, mutta myös uusia menetelmiä kehitetään, kuten lokitasoon perustuva kohdemuuttuja sekä muuttujien arvojakaumaan perustuva karsiminen. Ennustustulokset jättävät kuitenkin toivomisen varaa, mikä kertoo big datan mallinnuksen vaikeudesta. Epäiltyinä syinä ovat liian vähäinen malliparametrien ja esikäsittelyvalintojen optimointi, joiden täydentäminen vaatisi resursseihin nähden liian kattavaa testausta

Akuutin haimatulehduksen etiologia

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Few-body reference data for multicomponent formalisms: Light nuclei molecules

We present full quantum statistical energetics of some electron-light nuclei systems. This is accomplished with the path integral Monte Carlo method. The effects on energetics arising from the change in the nuclear mass are studied. The obtained results may serve as reference data for the multicomponent density functional theory calculations of light nuclei system. In addition, the results reported here will enable better fitting of todays electron-nuclear energy functionals, for which the description of light nuclei is most challenging, in particular