Results 161 to 170 of about 846,838 (328)

Classical criticality via quantum annealing. [PDF]

Nat Commun
Sathe P, King AD, Mniszewski SM, Coffrin C, Nisoli C, Caravelli F.   +5 more
europepmc   +1 more source

Validation of a Continuous-Energy Monte Carlo Burn-up Code MVP-BURN and Its Application to Analysis of Post Irradiation Experiment

, 2000
K. Okumura, Takamasa Mori, M. Nakagawa, K. Kaneko   +3 more
semanticscholar   +1 more source

A Generalized Framework for Data‐Efficient and Extrapolative Materials Discovery for Gas Separation

Advanced Intelligent Discovery, EarlyView.
This study introduces an iterative supervised machine learning framework for metal‐organic framework (MOF) discovery. The approach identifies over 97% of the best performing candidates while using less than 10% of available data. It generalizes across diverse MOF databases and gas separation scenarios.
Varad Daoo, Jayant K. Singh
wiley   +1 more source

Conventional versus Monte Carlo SPECT reconstruction of Lu-177: Toward reduced bias and variance in quantitative imaging. [PDF]

Med Phys
Polson LA, Esquinas P, Kurkowska S, Li C, Uribe C, Rahmim A.   +5 more
europepmc   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

Sampling Strategy: An Overlooked Factor Affecting Artificial Intelligence Prediction Accuracy of Peptides’ Physicochemical Properties

Advanced Intelligent Discovery, EarlyView.
This study reveals that sampling strategy (i.e., sampling size and approach) is a foundational prerequisite for building accurate and generalizable AI models in peptide discovery. Reaching a threshold of 7.5% of the total tetrapeptide sequence space was essential to ensure reliable predictions.
Meiru Yan, Ankeer Abuduhebaier, Haojin Zhou, Jiaqi Wang   +3 more
wiley   +1 more source

³¹P MRSI Coil Combination Using ²³Na Sensitivity Information. [PDF]

Magn Reson Med
Dai J, Gosselink M, Shams Z, Froeling M, Raaijmakers AJE, Klomp DWJ.   +5 more
europepmc   +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

Light‐Driven Competitive Selection in a Protein‐Catalyzed Dissipative Peptide Replication

Angewandte Chemie, EarlyView.
A structurally flexible protein catalyzes replication within a dissipative reaction network driven by UVA‐induced disulfide rearrangements. The protein accelerates templated autocatalytic cycles with replication emerging as the dominant pathway.
Éva Bartus, Edit Wéber, Attila Tököli, Ferenc Bogár, Momen R. F. Mohamed, Gábor Kecskeméti, Zoltán Szabó, Zoltán Kele, András Perczel, Márton Gadanecz, Zoltán Orgován, György M. Keserű, Tamás A. Martinek   +12 more
wiley   +2 more sources

Dual-function polyurethane-based CdO- B₄C radiation shields: experimental and monte carlo validation. [PDF]

Sci Rep
Mokhtari Dorostkar M, Abdi Saray A.
europepmc   +1 more source