An efficient coarse-grained approach for the electron transport through large molecular systems under dephasing environment

2016 ◽  
Vol 89 (4) ◽  
Author(s):  
Daijiro Nozaki ◽  
Raul Bustos-Marún ◽  
Carlos J. Cattena ◽  
Gianaurelio Cuniberti ◽  
Horacio M. Pastawski
Keyword(s):  
Electron Transport ◽  
Coarse Grained ◽  
Molecular Systems

Nonequilibrium Steady States and Electron Transport in Molecular Systems

2017 ◽  
pp. 127-150
Author(s):  
I. Deretzis ◽  
S. F. Lombardo ◽  
G. G. N. Angilella ◽  
R. Pucci ◽  
A. La Magna
Keyword(s):  
Electron Transport ◽  
Steady States ◽  
Nonequilibrium Steady States ◽  
Molecular Systems

scholarly journals Electron transport in molecular systems

2005 ◽  
Vol 16 ◽  
pp. 283-286 ◽  
Author(s):  
Vincent Meunier ◽  
Wenchang Lu ◽  
Jerry Bernholc ◽  
Bobby G Sumpter
Keyword(s):  
Electron Transport ◽  
Molecular Systems

scholarly journals Swarm-CG: Automatic Parametrization of Bonded Terms in Coarse-Grained Models of Simple to Complex Molecules via Fuzzy Self-Tuning Particle Swarm Optimization

2020 ◽  
Author(s):  
Charly Empereur-mot ◽  
Luca Pesce ◽  
Davide Bochicchio ◽  
Claudio Perego ◽  
Giovanni M. Pavan
Keyword(s):  
Particle Swarm Optimization ◽  
State Of The Art ◽  
Structural Complexity ◽  
Coarse Grained ◽  
Complex Molecules ◽  
Swarm Optimization ◽  
Molecular Systems ◽  
Self Tuning ◽  
User Friendly ◽  
Python Package

We present Swarm-CG, a versatile software for the automatic parametrization of bonded parameters in coarse-grained (CG) models. By coupling state-of-the-art metaheuristics to Boltzmann inversion, Swarm-CG performs accurate parametrization of bonded terms in CG models composed of up to 200 pseudoatoms within 4h-24h on standard desktop machines, using an AA trajectory as reference and default<br>settings of the software. The software benefits from a user-friendly interface and two different usage modes (default and advanced). We particularly expect Swarm-CG to support and facilitate the development of new CG models for the study of molecular systems interesting for bio- and nanotechnology.<br>Excellent performances are demonstrated using a benchmark of 9 molecules of diverse nature, structural complexity and size. Swarm-CG usage is ideal in combination with popular CG force<br>fields, such as e.g. MARTINI. However, we anticipate that in principle its versatility makes it well suited for the optimization of models built based also on other CG schemes. Swarm-CG is available with all its dependencies via the Python Package Index (PIP package: swarm-cg). Tutorials and demonstration data are available at: www.github.com/GMPavanLab/SwarmCG.

Compatible observable decompositions for coarse-grained representations of real molecular systems

2019 ◽  
Vol 151 (13) ◽  
pp. 134115 ◽  
Author(s):  
Thomas Dannenhoffer-Lafage ◽  
Jacob W. Wagner ◽  
Aleksander E. P. Durumeric ◽  
Gregory A. Voth
Keyword(s):  
Coarse Grained ◽  
Molecular Systems

Metal nanoelectrodes for molecular transistor and investigation of electron transport in molecular systems

2002 ◽  
Author(s):  
D. B. Suyatin ◽  
E. S. Soldatov ◽  
Ivan Maximov ◽  
Lars Montelius ◽  
Lars Samuelson ◽  
...  
Keyword(s):  
Electron Transport ◽  
Molecular Systems ◽  
Molecular Transistor

Parametrizing coarse grained models for molecular systems at equilibrium

2016 ◽  
Vol 225 (8-9) ◽  
pp. 1347-1372 ◽  
Author(s):  
E. Kalligiannaki ◽  
A. Chazirakis ◽  
A. Tsourtis ◽  
M.A. Katsoulakis ◽  
P. Plecháč ◽  
...  
Keyword(s):  
Coarse Grained ◽  
Molecular Systems

scholarly journals Quantitative understanding of molecular competition as a hidden layer of gene regulatory network

2018 ◽  
Author(s):  
Ye Yuan ◽  
Lei Wei ◽  
Tao Hu ◽  
Shuailin Li ◽  
Tianrun Cheng ◽  
...  
Keyword(s):  
Biological Networks ◽  
Regulatory Mechanism ◽  
Response Speed ◽  
Coarse Grained ◽  
Motif Model ◽  
Molecular Systems ◽  
Trade Offs ◽  
Quantitative Understanding ◽  
Hidden Layer ◽  
Cellular Components

AbstractMolecular competition is ubiquitous, essential and multifunctional throughout diverse biological processes. Competition brings about trade-offs of shared limited resources among the cellular components, and it thus introduce a hidden layer of regulatory mechanism by connecting components even without direct physical interactions. By abstracting the analogous competition mechanism behind diverse molecular systems, we built a unified coarse-grained competition motif model to systematically compare experimental evidences in these processes and analyzed general properties shared behind them. We could predict in what molecular environments competition would reveal threshold behavior or display a negative linear dependence. We quantified how competition can shape regulator-target dose-response curve, modulate dynamic response speed, control target expression noise, and introduce correlated fluctuations between targets. This work uncovered the complexity and generality of molecular competition effect, which might act as a hidden regulatory mechanism with multiple functions throughout biological networks in both natural and synthetic systems.

Efficient parallelization of perturbative Monte Carlo QM/MM simulations in heterogeneous platforms

2016 ◽  
Vol 31 (6) ◽  
pp. 499-516 ◽  
Author(s):  
Sebastião Miranda ◽  
Jonas Feldt ◽  
Frederico Pratas ◽  
Ricardo A Mata ◽  
Nuno Roma ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Heterogeneous Systems ◽  
Coarse Grained ◽  
Molecular Systems ◽  
Fine Grained ◽  
Central Processing ◽  
Speed Up ◽  
Graphical Processing ◽  
Computational Bottleneck ◽  
The Cost

A novel perturbative Monte Carlo mixed quantum mechanics (QM)/molecular mechanics (MM) approach has been recently developed to simulate molecular systems in complex environments. However, the required accuracy to efficiently simulate such complex molecular systems is usually granted at the cost of long executing times. To alleviate this problem, a new parallelization strategy of multi-level Monte Carlo molecular simulations is herein proposed for heterogeneous systems. It simultaneously exploits fine-grained (at the data level), coarse-grained (at the Markov chain level) and task-grained (pure QM, pure MM and QM/MM procedures) parallelism to ensure an efficient execution in heterogeneous systems composed of central processing units and multiple and possibly different graphical processing units. This is achieved by making use of the OpenCL library, together with appropriate dynamic load balancing schemes. From the conducted evaluation with real benchmarking data, a speed-up of 56x in the computational bottleneck part was observed, which results in a global speed-up of 38x for the whole simulation, reducing the time of a typical simulation from 80 hours to only 2 hours.

scholarly journals Swarm-CG: Automatic Parametrization of Bonded Termsin Coarse-Grained Models of Simple to Complex Molecules via Fuzzy Self-Tuning Particle Swarm Optimization

2020 ◽  
Author(s):  
Charly Empereur-mot ◽  
Luca Pesce ◽  
Davide Bochicchio ◽  
Claudio Perego ◽  
Giovanni M. Pavan
Keyword(s):  
Particle Swarm Optimization ◽  
State Of The Art ◽  
Structural Complexity ◽  
Coarse Grained ◽  
Complex Molecules ◽  
Swarm Optimization ◽  
Molecular Systems ◽  
Self Tuning ◽  
User Friendly ◽  
Python Package

We present Swarm-CG, a versatile software for the automatic parametrization of bonded parameters in<br>coarse-grained (CG) models. By coupling state-of-the-art metaheuristics to Boltzmann inversion, Swarm-<br>CG performs accurate parametrization of bonded terms in CG models composed of up to 200 pseudoatoms<br>within 4h-24h on standard desktop machines, using an AA trajectory as reference and default<br>settings of the software. The software benefits from a user-friendly interface and two different usage<br>modes (default and advanced). We particularly expect Swarm-CG to support and facilitate the<br>development of new CG models for the study of molecular systems interesting for bio- and nanotechnology.<br>Excellent performances are demonstrated using a benchmark of 9 molecules of diverse<br>nature, structural complexity and size. Swarm-CG usage is ideal in combination with popular CG force<br>fields, such as e.g. MARTINI. However, we anticipate that in principle its versatility makes it well suited for<br>the optimization of models built based also on other CG schemes. Swarm-CG is available with all its<br>dependencies via the Python Package Index (PIP package: swarm-cg). Tutorials and demonstration data<br>are available at: www.github.com/GMPavanLab/SwarmCG.

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