Results 61 to 70 of about 4,324 (212)

A Phase‐Resolved Geometric Deep Learning Framework Maps Structural Determinants of Disease‐Associated Protein Aggregation and Guides Suppressor Design

open access: yesAdvanced Science, EarlyView.
SKALE 2.0 maps disease‐associated protein aggregation as a phase‐resolved structural process, linking mutation‐induced geometric perturbations to nucleation, elongation, and suppressor design. Across neurodegenerative proteins, the framework reveals cryptic aggregation vulnerabilities, separates phase‐concordant and phase‐switching mutations, and ...
Jia Shen Sio   +6 more
wiley   +1 more source

Nonequilibrium solvent response force: What happens if you push a Brownian particle

open access: yesPhysical Review Research
In this Letter we discuss how to add forces to the Langevin equation. We derive an exact generalized Langevin equation for the dynamics of one particle subject to an external force embedded in a system of many interacting particles.
Fabian Koch, Jona Erle, Tanja Schilling
doaj   +1 more source

The Stochastic Resonance Behaviors of a Generalized Harmonic Oscillator Subject to Multiplicative and Periodically Modulated Noises

open access: yesAdvances in Mathematical Physics, 2016
The stochastic resonance (SR) characteristics of a generalized Langevin linear system driven by a multiplicative noise and a periodically modulated noise are studied (the two noises are correlated).
Suchuan Zhong   +4 more
doaj   +1 more source

Diffusion induced by bounded noise in a two-dimensional coupled memory system

open access: yesTheoretical and Applied Mechanics Letters, 2014
The diffusion behavior driven by bounded noise under the influence of a coupled harmonic potential is investigated in a two-dimensional coupled-damped model.
Pengfei Xu, Wenxian Xie, Li Cai
doaj   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

open access: yesAdvanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park   +19 more
wiley   +1 more source

1/f noise: A nonlinear-generalized-Langevin-equation approach

open access: yes, 1990
The spectrum of the autocorrelation function of the velocity fluctuations is calculated by using the nonlinear generalized Langevin equation which has a random force which is a function of the coordinates of the Brownian particle.
O\u27Connell, R. F., Hu, G. Y.
core   +1 more source

Mechanisms of Alkali Ionic Transport in Amorphous Oxyhalides Solid State Conductors

open access: yesAdvanced Energy Materials, EarlyView.
Large‐scale machine learning‐based molecular dynamics simulations are used to investigate isovalent amorphous oxyhalides, revealing a remarkable chemically independent ionic conductivity. A rigorous analysis of alkali residence times across different metal–anion environments identifies divalent anions as key diffusion bottlenecks.
Luca Binci   +3 more
wiley   +1 more source

( k , φ ) $(\mathtt{k},\varphi )$ -Hilfer fractional Langevin differential equation having multipoint boundary conditions

open access: yesBoundary Value Problems
The primary objective of this manuscript is to investigate the existence and uniqueness of solutions for the Langevin ( k , φ ) $(\mathtt{k},\varphi )$ -Hilfer fractional differential equation of different orders with multipoint nonlocal fractional ...
HuiYan Cheng   +4 more
doaj   +1 more source

Measure of Noncompactness for Hybrid Langevin Fractional Differential Equations

open access: yesAxioms, 2020
In this research article, we introduce a new class of hybrid Langevin equation involving two distinct fractional order derivatives in the Caputo sense and Riemann–Liouville fractional integral. Supported by three-point boundary conditions, we discuss the
Ahmed Salem, Mohammad Alnegga
doaj   +1 more source

ParamNet: A Physics‐Guided Deep Learning Framework for Intelligent Self‐Inversion of Vacuum Optical Levitation Systems

open access: yesAdvanced Intelligent Systems, EarlyView.
A physics‐guided deep learning framework, ParamNet, is introduced for the intelligent self‐inversion of vacuum optical tweezers. By fuzing dual‐branch time–frequency features with physical dynamical constraints, it achieves high‐accuracy calibration of trap parameters from short‐window, low‐frequency trajectories, outperforming traditional methods ...
Qi Zheng   +4 more
wiley   +1 more source

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