9 research outputs found
An efficient multi-scale Green's Functions Reaction Dynamics scheme
Molecular Dynamics - Green's Functions Reaction Dynamics (MD-GFRD) is a
multiscale simulation method for particle dynamics or particle-based
reaction-diffusion dynamics that is suited for systems involving low particle
densities. Particles in a low-density region are just diffusing and not
interacting. In this case one can avoid the costly integration of microscopic
equations of motion, such as molecular dynamics (MD), and instead turn to an
event-based scheme in which the times to the next particle interaction and the
new particle positions at that time can be sampled. At high (local)
concentrations, however, e.g. when particles are interacting in a nontrivial
way, particle positions must still be updated with small time steps of the
microscopic dynamical equations. The efficiency of a multi-scale simulation
that uses these two schemes largely depends on the coupling between them and
the decisions when to switch between the two scales. Here we present an
efficient scheme for multi-scale MD-GFRD simulations. It has been shown that
MD-GFRD schemes are more efficient than brute-force molecular dynamics
simulations up to a molar concentration of . In this paper, we
show that the choice of the propagation domains has a relevant impact on the
computational performance. Domains are constructed using a local optimization
of their sizes and a minimal domain size is proposed. The algorithm is shown to
be more efficient than brute-force Brownian dynamics simulations up to a molar
concentration of and is up to an order of magnitude more
efficient compared with previous MD-GFRD schemes
Efficient multi-scale sampling methods in statistical physics
This thesis deals with the development and formalization of algorithms designed for an efficient simulation of biological systems. This work is separated into two different parts, and in each part a different algorithm is investigated. In the first part of the thesis, an algorithm that is used to simulate biological systems at the mesoscopic scale is outlined. The aforementioned algorithm is studied in detail, and several improvements, theoretical, algorithmic and technical, are presented. In the second part of the thesis, a novel sampling method is outlined, which uses deep-learning to accelerate the computation of equilibrium properties of systems defined with atomistic detail. The two parts lead to applications at different scales, and, in the future, methods and concepts developed in this thesis can be useful for the investigation of biological processes defined with mesoscopic or microscopic detail
An efficient multi-scale Greenâs function reaction dynamics scheme
Molecular Dynamics-Greenâs Function Reaction Dynamics (MD-GFRD) is a
multiscale simulation method for particle dynamics or particle-based reaction-
diffusion dynamics that is suited for systems involving low particle
densities. Particles in a low-density region are just diffusing and not
interacting. In this case, one can avoid the costly integration of microscopic
equations of motion, such as molecular dynamics (MD), and instead turn to an
event-based scheme in which the times to the next particle interaction and the
new particle positions at that time can be sampled. At high (local)
concentrations, however, e.g., when particles are interacting in a nontrivial
way, particle positions must still be updated with small time steps of the
microscopic dynamical equations. The efficiency of a multi-scale simulation
that uses these two schemes largely depends on the coupling between them and
the decisions when to switch between the two scales. Here we present an
efficient scheme for multi-scale MD-GFRD simulations. It has been shown that
MD-GFRD schemes are more efficient than brute-force molecular dynamics
simulations up to a molar concentration of 102 ÎźM. In this paper, we show that
the choice of the propagation domains has a relevant impact on the
computational performance. Domains are constructed using a local optimization
of their sizes and a minimal domain size is proposed. The algorithm is shown
to be more efficient than brute-force Brownian dynamics simulations up to a
molar concentration of 103 ÎźM and is up to an order of magnitude more
efficient compared with previous MD-GFRD schemes
Roadmap on data-centric materials science
Science is and always has been based on data, but the terms âdata-centricâ and the â4th paradigmâ of materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of artificial intelligence and its subset machine learning, has become pivotal in addressing all these challenges. This Roadmap on Data-Centric Materials Science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research
The NOMAD Artificial-Intelligence Toolkit: Turning materials-science data into knowledge and understanding
We present the Novel-Materials-Discovery (NOMAD) Artificial-Intelligence (AI)
Toolkit, a web-browser-based infrastructure for the interactive AI-based
analysis of materials-science findable, accessible, interoperable, and reusable
(FAIR) data. The AI Toolkit readily operates on the FAIR data stored in the
central server of the NOMAD Archive, the largest database of materials-science
data worldwide, as well as locally stored, users' owned data. The NOMAD Oasis,
a local, stand alone server can be also used to run the AI Toolkit. By using
Jupyter notebooks that run in a web-browser, the NOMAD data can be queried and
accessed; data mining, machine learning, and other AI techniques can be then
applied to analyse them. This infrastructure brings the concept of
reproducibility in materials science to the next level, by allowing researchers
to share not only the data contributing to their scientific publications, but
also all the developed methods and analytics tools. Besides reproducing
published results, users of the NOMAD AI toolkit can modify the Jupyter
notebooks towards their own research work
Codice delle Costituzioni - vol. VI.I - Paesi islamici
Codice che racchiude le traduzioni delle Costituzioni di importanti Paesi islamici, precedute da saggi di commento
Roadmap on Data-Centric Materials Science
Science is and always has been based on data, but the terms âdata-centricâ and the â4th paradigmâ of materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of Artificial Intelligence (AI) and its subset Machine Learning (ML), has become pivotal in addressing all these challenges. This Roadmap on Data-Centric Materials Science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy.
While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research