Results 221 to 230 of about 11,939,645 (246)

Generative and Experimental Validation of High Refractive Index Polymers via Domain Knowledge Approach with Small Data

Advanced Intelligent Discovery, EarlyView.
This research demonstrates that the combination of domain knowledge–based multiple regression, multi‐objective Bayesian optimization, and generative models is a suitable prediction tool for candidates of high refractive index polymers, even with the constraints in the model trained on limited data. The experimental validation can reproduce the proposed
Takuya Yokoo, Seigo Watanabe, Hana Matsunaga, Kenichi Oyaizu   +3 more
wiley   +1 more source

MolMiner: Toward Controllable, Three‐Dimensional‐Aware, Fragment‐Based Molecular Design

Advanced Intelligent Discovery, EarlyView.
MolMiner is a fragment‐based, geometry‐aware, and order‐agnostic generative model for molecular design with strong inductive biases. Using symmetry‐aware fragment assembly, dynamic three‐dimensional geometry, and multi‐property conditioning, MolMiner enables interpretable and controllable molecular generation.
Raul Ortega‐Ochoa, Tejs Vegge, Jes Frellsen   +2 more
wiley   +1 more source

A Multistrategy AI‐Assisted Consensus Screening Pipeline for Discovery of Candidate p53 Monomer Binders

Advanced Intelligent Discovery, EarlyView.
An ultra‐fast small‐sample AI consensus pipeline combines co‐folding and docking to target structure‐lacking p53 monomers. Its optimal mini‐protein binder shows robust interfacial affinity and promising druggability, supplying unprecedented antitumor p53 templates.
Ningyao Li, Xueyi Liu, Qiuyue Li, Chengfeng Yue, Xin Li, Yugang Huang, Xin Deng, Junfei Zhang, Xue Liang, Guodong Ye   +9 more
wiley   +1 more source

Predicting Performance of Hall Effect Ion Source Using Machine Learning

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park, Guentae Doh, Dongho Lee, Youngho Kim, Changmin Shin, Su‐Jin Shin, Young‐Chul Ghim, Sanghoo Park, Wonho Choe   +8 more
wiley   +1 more source

Compact Modeling of Volatile‐Switching Electrochemical Metallization Memory Cells by Means of the Electromotive Force

Advanced Intelligent Systems, EarlyView.
A volatile‐switching compact model of electrochemical metallization memory cells for neuromorphic architecture is developed and validated by reliable reproduction of device characterization measurements: I−V sweeps, SET kinetics, relaxation dynamics.
Rana Walied Ahmad, Solomon Amsalu Chekol, Julius Rasbach, Susanne Hoffmann‐Eifert, Stephan Menzel   +4 more
wiley   +1 more source

Enhancing quasi-Monte Carlo methods via delta dimension and directional control technique

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Exchange Monte Carlo Method and Application to Spin Glass Simulations

Journal of the Physical Society of Japan, 1996
Koji Hukushima
exaly  

Sustainable Queuing-Network Design for Airport Security Based on the Monte Carlo Method

Sustainability, 2018
Xiangqian Xu, Tianjun Liao, Yuejin Tan   +2 more
exaly  

Why the Monte Carlo method is so important today

Wiley Interdisciplinary Reviews: Computational Statistics, 2014
Zdravko Botev, Dirk Kroese, Thomas Taimre   +2 more
exaly  

Evaluating View Factors Using a Hybrid Monte-Carlo Method

Journal of Heat Transfer, 2022
exaly