Results 231 to 240 of about 1,879,580 (356)

Excitation Energy Transfer between Porphyrin Dyes on a Clay Surface: A Study Employing Multifidelity Machine Learning

open access: yesAdvanced Theory and Simulations, EarlyView.
Inspired by natural light‐harvesting systems, this study computationally investigates a synthetic antenna by arranging cationic free‐base porphyrin molecules on an anionic clay surface. Using a multiscale quantum mechanics/molecular mechanics (QM/MM) approach combined with a multifidelity machine learning method, excitation energies are predicted ...
Dongyu Lyu   +7 more
wiley   +1 more source

The random noise modulations on the nonlinear Chiral Schrödinger structures. [PDF]

open access: yesPLoS One
Alhazmi H   +3 more
europepmc   +1 more source

Toward a Universal Czochralski Growth Model Leveraging Data‐Driven Techniques

open access: yesAdvanced Theory and Simulations, EarlyView.
This data‐driven study investigates Czochralski growth using Decision Trees, Symbolic Regression, ANN, and SHAP to assess the impact of 21 process and design parameters on crystal quality. Trained on 632 CFD simulation datasets across four crystalline materials, it demonstrates the feasibility of a universal Cz model applicable to various ...
Natasha Dropka   +4 more
wiley   +1 more source

A Comprehensive Lateral Flow Strip Assay for On‐Site mRNA Vaccine Quality Control in Decentralized Manufacturing

open access: yesAdvanced Science, EarlyView.
A rapid, portable lateral flow strip assay for on‐site mRNA quality control is developed, enabling sequence‐independent analysis of 5' capping, integrity, and LNPs encapsulation efficiency. This cost‐effective method offers real‐time stability monitoring, reduced sample requirements, and comparable accuracy to standard techniques, enhancing ...
Dengwang Luo   +9 more
wiley   +1 more source

Stochastic effects at ripple formation processes in anisotropic systems with multiplicative noise [PDF]

open access: green, 2010
Dmitrii O. Kharchenko   +3 more
openalex   +1 more source

CellPhenoX: An Explainable Machine Learning Method for Identifying Cell Phenotypes To Predict Clinical Outcomes from Single‐Cell Multi‐Omics

open access: yesAdvanced Science, EarlyView.
CellPhenoX is an explainable machine learning framework that identifies cell‐specific phenotypes and interaction effects from single‐cell omics data. By leveraging interpretable models, it enables robust discovery of cell‐level phenotypes that contribute to clinical outcomes.
Jade Young   +4 more
wiley   +1 more source

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