Results 151 to 160 of about 156,378 (258)

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

Simulation of the Long-Term Toxicity Towards Bobwhite Quail (Colinus virginianus) by the Monte Carlo Method. [PDF]

J Xenobiot
Iovine N, Toropova AP, Toropov AA, Roncaglioni A, Benfenati E.   +4 more
europepmc   +1 more source

Absorbed dose calculation for a realistic CT-derived mouse phantom irradiated with a standard Cs-137 cell irradiator using a Monte Carlo method. [PDF]

PLoS One, 2023
Entezam A, Fielding A, Bradley D, Fontanarosa D.   +3 more
europepmc   +1 more source

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

Two-metal-edge extended X-ray absorption fine structure analysis of oxygen octahedral rotation in SrTiO₃ using the reverse Monte Carlo method. [PDF]

J Synchrotron Radiat
Kamijo K, Nakajima N, Fan D, Anspoks A.
europepmc   +1 more source

Plate Waste Forecasting Using the Monte Carlo Method for Effective Decision Making in Latvian Schools. [PDF]

Nutrients, 2022
Kodors S, Zvaigzne A, Litavniece L, Lonska J, Silicka I, Kotane I, Deksne J.   +6 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

Thermal Energy Transfer between Helium Gas and Graphene Surface According to Molecular Dynamics Simulations and the Monte Carlo Method. [PDF]

Nanomaterials (Basel), 2022
Zhang L, Ban H.
europepmc   +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

Corrigendum to "Seemingly unrelated time series model for forecasting the peak and short-term electricity demand: Evidence from the Kalman filtered Monte Carlo method" [Heliyon Volume 9, Issue 8, August 2023, Article e18821]. [PDF]

Heliyon
Owusu FK, Amoako-Yirenkyi P, Frempong NK, Omari-Sasu AY, Mensah IA, Martin H, Sakyi A.   +6 more
europepmc   +1 more source