Results 41 to 50 of about 730,182 (230)

DyProL: Dynamic Ensemble Representation Learning for Protein–Nucleic Acid Binding Site Prediction

Advanced Science, EarlyView.
Protein function is represented as a dynamic conformational ensemble rather than a single static structure. A multi‐conformation geometric attention framework aligns, clusters, and learns representative states to capture residue‐ and ensemble‐level signals. Integrating structural dynamics improves interpretable protein‐NA binding prediction and reveals
Pengpai Li, Yiman Liu, Liya Liang, Rongming Liu   +3 more
wiley   +1 more source

Similarity‐Enhanced Representation Learning of Non‐Canonical Amino Acids for Therapeutic Peptide Modeling

Advanced Science, EarlyView.
Non‐canonical amino acids (ncAAs) enhance peptide therapeutics but remain difficult to model computationally. SinCAA, a similarity‐enhanced pretraining framework, jointly optimizes contrastive learning guided by a novel conformational similarity metric with masked node reconstruction, capturing both functional relationships and chemical identity of ...
Chencheng Xu, Lesong Wei, Jianmin Wang, Yuanpeng Xiong, Ruochi Zhang, Yu Wang, Chao Zha, Qiangcheng Zeng, Xin Gao   +8 more
wiley   +1 more source

A Cartesian grid technique based on one-dimensional integrated radial basis function networks for natural convection in concentric annuli [PDF]

, 2008
This paper reports a radial-basis-function (RBF)-based Cartesian grid technique for the simulation of two-dimensional buoyancy-driven flow in concentric annuli.
Mai-Duy, Nam, Le-Cao, Khoa, Tran-Cong, Thanh   +2 more
core   +1 more source

Assessing Mesoscale Heterogeneities in Hard Carbon Electrodes Through Deep Learning‐Assisted FIB‐SEM Characterization, Manufacturing and Electrochemical Modeling

Advanced Energy Materials, EarlyView.
A combination of discrete and finite element method models for the current collector deformation and electrochemical performance analysis, respectively. The models are calibrated and validated with electrochemical and imaging data of hard carbon electrodes. These electrodes were manufactured with different parameters (slurry solid contents of 35 and 40
Soorya Saravanan, Imelda Cardenas‐Sierra, Pei Sun, Hari Raj, Daniele Duranti, Mahnoor Nafees, Edoardo Rossi, Marco Sebastiani, Paul Vigneaux, Martin Petit, Maxime Moreaud, Francisco Fernandez, Alejandro A. Franco   +12 more
wiley   +1 more source

Computational Fluid Dynamics study of element type and turbulence model impact on a flow over a spacer grid using Simcenter STAR-CCM+

Brazilian Journal of Radiation Sciences
: This study presents a numerical investigation into the impact of various mesh element types on water flow results through a representative spacer grid, utilizing Computational Fluid Dynamics (CFD).
Tiago Augusto Santiago Vieira, Yasmim Martins Carvalho, Higor Fabiano Pereira de Castro, Keferson Almeida Carvalho, Rebeca Cabral Gonçalves, Pedro Henrique Araújo, Davi Cury, Vitor Vasconcelos Araújo Silva, Graiciany Paula Barros, Andre Augusto Campagnole dos Santos   +9 more
doaj   +1 more source

Lattice Boltzmann model in general curvilinear coordinates applied to exactly solvable 2D flow problems

Frontiers in Applied Mathematics and Statistics, 2023
Numerical simulation results of basic exactly solvable fluid flows using the previously proposed by H. Chen Lattice Boltzmann Method (LBM) formulated on a general curvilinear coordinate system are presented.
Alexei Chekhlov, Ilya Staroselsky, Raoyang Zhang, Hudong Chen   +3 more
doaj   +1 more source

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

Advanced 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, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

A Grid Service Infrastructure for Mobile Devices [PDF]

, 2005
One of the visions of Grid computing is to access computational resources automatically on demand to deliver the services required with appropriate quality. Because mobile devices are now increasingly common, an infrastructure is required to allow mobile
Guan, Tao, Ed Zaluska, Tao Guan, Roure, DD, David De Roure, Zaluska, Ed, Zaluska, E, Guan, T, Roure, David   +8 more
core   +2 more sources

Immersed Boundary Method Using Ghost Cells in a Three-Dimensional Case

Современные информационные технологии и IT-образование, 2020
When solving numerically gas dynamics problems, one often encounters difficulties in processing regions with complex geometry. Generating consistent computational grids for such areas may be a complex task.
Alexey Rybakov
doaj   +1 more source

Online dynamical downscaling of temperature and precipitation within the iLOVECLIM model (version 1.1) [PDF]

Geoscientific Model Development, 2018
This paper presents the inclusion of an online dynamical downscaling of temperature and precipitation within the model of intermediate complexity iLOVECLIM v1.1. We describe the following methodology to generate temperature and precipitation fields on
A. Quiquet, D. M. Roche, D. M. Roche, C. Dumas, D. Paillard   +4 more
doaj   +1 more source