Zobrazeno 1 - 10
of 143
pro vyhledávání: '"Donati, Luca"'
We have investigated how Langevin dynamics is affected by the friction coefficient using the novel algorithm ISOKANN, which combines the transfer operator approach with modern machine learning techniques. ISOKANN describes the dynamics in terms of an
Externí odkaz:
http://arxiv.org/abs/2402.00211
Modern potential energy surfaces have shifted attention to molecular simulations of chemical reactions. While various methods can estimate rate constants for conformational transitions in molecular dynamics simulations, their applicability to studyin
Externí odkaz:
http://arxiv.org/abs/2312.12948
Estimating the rate of rare conformational changes in molecular systems is one of the goals of Molecular Dynamics simulations. In the past decades, a lot of progress has been done in data-based approaches towards this problem. In contrast, model-base
Externí odkaz:
http://arxiv.org/abs/2311.09779
Autor:
Donati, Luca, Weber, Marcus
Extracting the kinetic properties of a system whose dynamics depend on the pH of the environment with which it exchanges energy and atoms requires sampling the Grand Canonical Ensemble. As an alternative, we present a novel strategy that requires sim
Externí odkaz:
http://arxiv.org/abs/2307.04439
Dynamical reweighting methods permit to estimate kinetic observables of a stochastic process governed by a target potential $\tilde{V}(x)$ from trajectories that have been generated at a different potential $V(x)$. In this article, we present Girsano
Externí odkaz:
http://arxiv.org/abs/2209.10544
Autor:
Donati, Luca, Weber, Marcus
We present a method to estimate the transition rates of molecular systems under different environmental conditions which cause the formation or the breaking of bonds and require the sampling of the Grand Canonical Ensemble. For this purpose, we model
Externí odkaz:
http://arxiv.org/abs/2207.06195
Gradient-based attention modeling has been used widely as a way to visualize and understand convolutional neural networks. However, exploiting these visual explanations during the training of generative adversarial networks (GANs) is an unexplored ar
Externí odkaz:
http://arxiv.org/abs/2108.07466
Publikováno v:
Journal of Chemical Physics; 3/14/2024, Vol. 160 Issue 10, p1-15, 15p
Molecular dynamics are extremely complex, yet understanding the slow components of their dynamics is essential to understanding their macroscopic properties. To achieve this, one models the molecular dynamics as a stochastic process and analyses the
Externí odkaz:
http://arxiv.org/abs/2010.03407
Image synthesis is currently one of the most addressed image processing topic in computer vision and deep learning fields of study. Researchers have tackled this problem focusing their efforts on its several challenging problems, e.g. image quality a
Externí odkaz:
http://arxiv.org/abs/1912.02494