Filter and export

Publications

What is a Publication?
1687 Publications visible to you, out of a total of 1687
Sources of predictability of synoptic‐scale rainfall during the West African summer monsoon

Abstract

Not specified

Authors: Athul Rasheeda Satheesh, Peter Knippertz, Andreas H. Fink, Eva-Maria Walz, Tilmann Gneiting

Date Published: 16th Oct 2023

Publication Type: Journal

A Generalizable Approach for Network Flow Image Representation for Deep Learning

Abstract

Not specified

Authors: Stefan Machmeier, Manuel Trageser, Marcus Buchwald, Vincent Heuveline

Date Published: 16th Oct 2023

Publication Type: Journal

KineticNet: Deep learning a transferable kinetic energy functional for orbital-free density functional theory

Abstract (Expand)

Orbital-free density functional theory (OF-DFT) holds promise to compute ground state molecular properties at minimal cost. However, it has been held back by our inability to compute the kinetic energy …kinetic energy as a functional of electron density alone. Here, we set out to learn the kinetic energy functional from ground truth provided by the more expensive Kohn–Sham density functional theory. Such learning is confronted with two key challenges: Giving the model sufficient expressivity and spatial context while limiting the memory footprint to afford computations on a GPU and creating a sufficiently broad distribution of training data to enable iterative density optimization even when starting from a poor initial guess. In response, we introduce KineticNet, an equivariant deep neural network architecture based on point convolutions adapted to the prediction of quantities on molecular quadrature grids. Important contributions include convolution filters with sufficient spatial resolution in the vicinity of nuclear cusp, an atom-centric sparse but expressive architecture that relays information across multiple bond lengths, and a new strategy to generate varied training data by finding ground state densities in the face of perturbations by a random external potential. KineticNet achieves, for the first time, chemical accuracy of the learned functionals across input densities and geometries of tiny molecules. For two-electron systems, we additionally demonstrate OF-DFT density optimization with chemical accuracy.

Authors: R. Remme, T. Kaczun, M. Scheurer, A. Dreuw, F. A. Hamprecht

Date Published: 14th Oct 2023

Publication Type: Journal

Crafting molecular architectures with guided diffusion

Abstract

Not specified

Author: Ganna Gryn’ova

Date Published: 11th Oct 2023

Publication Type: Journal

AleRax: A tool for gene and species tree co-estimation and reconciliation under a probabilistic model of gene duplication, transfer, and loss

Abstract (Expand)

ABSTRACT Motivation Genomes are a rich source of information on the pattern and process of evolution across biological scales. How best to make use of that information is an active area of research in …at information is an active area of research in phylogenetics. Ideally, phylogenetic methods should not only model substitutions along gene trees, which explain differences between homologous gene sequences, but also the processes that generate the gene trees themselves along a shared species tree. To conduct accurate inferences, one needs to account for uncertainty at both levels, that is, in gene trees estimated from inherently short sequences and in their diverse evolutionary histories along a shared species tree. Results We present AleRax, a software that can infer reconciled gene trees together with a shared species tree using a simple, yet powerful, probabilistic model of gene duplication, transfer, and loss. A key feature of AleRax is its ability to account for uncertainty in the gene tree and its reconciliation by using an efficient approximation to calculate the joint phylogenetic-reconciliation likelihood and sample reconciled gene trees accordingly. Simulations and analyses of empirical data show that AleRax is one order of magnitude faster than competing gene tree inference tools while attaining the same accuracy. It is consistently more robust than species tree inference methods such as SpeciesRax and ASTRAL-Pro 2 under gene tree uncertainty. Finally, AleRax can process multiple gene families in parallel thereby allowing users to compare competing phylogenetic hypotheses and estimate model parameters, such as DTL probabilities for genome-scale datasets with hundreds of taxa Availability and Implementation GNU GPL at https://github.com/BenoitMorel/AleRax and data are made available at https://cme.h-its.org/exelixis/material/alerax_data.tar.gz . Contact Benoit.Morel@h-its.org Supplementary information Supplementary material is available.

Authors: Benoit Morel, Tom A. Williams, Alexandros Stamatakis, Gergely J. Szöllősi

Date Published: 7th Oct 2023

Publication Type: Journal

The role of flow in the self-assembly of dragline spider silk proteins

Abstract (Expand)

Hydrodynamic flow in the spider duct induces conformational changes in dragline spider silk proteins (spidroins) and drives their assembly, but the underlying physical mechanisms are still elusive. … Here we address this challenging multiscale problem with a complementary strategy of atomistic and coarse-grained molecular dynamics simulations with uniform flow. The conformational changes at the molecular level were analyzed for single-tethered spider silk peptides. Uniform flow leads to coiled-to-stretch transitions and pushes alanine residues into β sheet and poly-proline II conformations. Coarse-grained simulations of the assembly process of multiple semi-flexible block copolymers using multi-particle collision dynamics reveal that the spidroins aggregate faster but into low-order assemblies when they are less extended. At medium-to-large peptide extensions (50%–80%), assembly slows down and becomes reversible with frequent association and dissociation events, whereas spidroin alignment increases and alanine repeats form ordered regions. Our work highlights the role of flow in guiding silk self-assembly into tough fibers by enhancing alignment and kinetic reversibility, a mechanism likely relevant also for other proteins whose function depends on hydrodynamic flow.

Authors: Ana M. Herrera-Rodríguez, Anil Kumar Dasanna, Csaba Daday, Eduardo R. Cruz-Chú, Camilo Aponte-Santamaría, Ulrich S. Schwarz, Frauke Gräter

Date Published: 5th Oct 2023

Publication Type: Journal

CO 2 on Graphene: Benchmarking Computational Approaches to Noncovalent Interactions

Abstract

Not specified

Authors: Christopher Ehlert, Anna Piras, Ganna Gryn’ova

Date Published: 3rd Oct 2023

Publication Type: Journal

Powered by
 (v.1.16.0)
About HITS Publication SEEK | About HITS-Publication-SEEK | Funding and Programmes | Credits
Copyright © 2008 - 2024 The University of Manchester and HITS gGmbH