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1447 Publications visible to you, out of a total of 1447
Scoring epidemiological forecasts on transformed scales
Computational Statistics

Abstract

Not specified

Authors: Nikos I. Bosse, Sam Abbott, Anne Cori, Edwin van Leeuwen, Johannes Bracher, Sebastian Funk

Date Published: 29th Aug 2023

Publication Type: Journal

Optimization of a Drug: Polymer Formulation Exploiting a Computational Approach based on Molecular Dynamics Simulations
Molecular and Cellular Modeling

Abstract

Not specified

Editor:

Date Published: 22nd Aug 2023

Publication Type: Bachelor's Thesis

Collaborative nowcasting of COVID-19 hospitalization incidences in Germany
Computational Statistics

Abstract

Not specified

Authors: Daniel Wolffram, Sam Abbott, Matthias an der Heiden, Sebastian Funk, Felix Günther, Davide Hailer, Stefan Heyder, Thomas Hotz, Jan van de Kassteele, Helmut Küchenhoff, Sören Müller-Hansen, Diellë Syliqi, Alexander Ullrich, Maximilian Weigert, Melanie Schienle, Johannes Bracher

Date Published: 11th Aug 2023

Publication Type: Journal

Unlocking the Potential: Predicting Redox Behavior of Organic Molecules, from Linear Fits to Neural Networks
Computational Carbon Chemistry, SIMPLAIX

Abstract (Expand)

Redox-active organic molecules, i.e., molecules that can relatively easily accept and/or donate electrons, are ubiquitous in biology, chemical synthesis, and electronic and spintronic devices, such as … solar cells and rechargeable batteries, etc. Choosing the best candidates from an essentially infinite chemical space for experimental testing in a target application requires efficient screening approaches. In this Review, we discuss modern in silico techniques for predicting reduction and oxidation potentials of organic molecules that go beyond conventional first-principles computations and thermodynamic cycles. Approaches ranging from simple linear fits based on molecular orbital energy approximation and energy difference approximation to advanced regression and neural network machine learning algorithms employing complex descriptors of molecular compositions, geometries, and electronic structures are examined in conjunction with relevant literature examples. We discuss the interplay between ab initio data and machine learning (ML), i.e., whether it is better to base predictions on low-level quantum-chemical results corrected with ML or to bypass first-principles computations entirely and instead rely on elaborate deep learning architectures. Finally, we list currently available data sets of redox-active organic molecules and their experimental and/or computed properties to facilitate the development of screening platforms and rational design of redox-active organic molecules.

Authors: Rostislav Fedorov, Ganna Gryn’ova

Date Published: 8th Aug 2023

Publication Type: Journal

Molecular Dynamics Simulations of the Human Ecto-5′-Nucleotidase (h-ecto-5′-NT, CD73): Insights into Protein Flexibility and Binding Site Dynamics
Molecular and Cellular Modeling

Abstract

Not specified

Authors: Lucas G. Viviani, Daria B. Kokh, Rebecca C. Wade, Antonia T.-do Amaral

Date Published: 2nd Aug 2023

Publication Type: Journal

Unprecedented early flux excess in the hybrid 02es-like type Ia supernova 2022ywc indicates interaction with circumstellar material
Physics of Stellar Objects

Abstract

Not specified

Authors: Shubham Srivastav, T. Moore, M. Nicholl, M. R. Magee, S. J. Smartt, M. D. Fulton, S. A. Sim, J. M. Pollin, L. Galbany, C. Inserra, A. Kozyreva, Takashi J. Moriya, F. P. Callan, X. Sheng, K. W. Smith, J. S. Sommer, J. P. Anderson, M. Deckers, M. Gromadzki, T. E. Müller-Bravo, G. Pignata, A. Rest, D. R. Young

Date Published: 1st Aug 2023

Publication Type: Journal

Impact of central mixing scheme and nuclear reaction network on the extent of convective cores
Theory and Observations of Stars

Abstract (Expand)

Convective cores are the hydrogen reservoirs of main sequence stars that are more massive than around 1.2 solar masses. The characteristics of the cores have a strong impact on the evolution and structure … of the star. However, such results rely on stellar evolution codes, in which simplistic assumptions are often made on the physics in the core. Indeed, mixing is commonly considered to be instantaneous and the most basic nuclear networks assume beryllium at its equilibrium abundance. Those assumptions lead to significant differences in the central composition of the elements for which the timescale to reach nuclear equilibrium is lower than the convective timescale. In this work, we show that those discrepancies impact the nuclear energy production and, therefore, the size of convective cores in models computed with overshoot. We find that cores computed with instantaneous mixing are up to 30% bigger than those computed with diffusive mixing. Similar differences are found when using basic nuclear networks. Additionally, we observed an extension of the duration of the main sequence due to those core size differences. We then investigated the impact of those structural differences on the seismic modeling of solar-like oscillators. Modeling two stars observed by Kepler, we find that the overshoot parameter of the best models computed with a basic nuclear network is significantly lower, compared to models computed with a full nuclear network. This work is a necessary step in improving the modeling of convective cores, which is key to determining accurate ages in the framework of future space missions such as Plato.

Authors: Anthony Noll, Sébastien Deheuvels

Date Published: 1st Aug 2023

Publication Type: Journal

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