Results 181 to 190 of about 213,445 (314)

Topology‐Aware Machine Learning for High‐Throughput Screening of MOFs in C8 Aromatic Separation

Advanced Intelligent Discovery, EarlyView.
We screened 15,335 Computation‐Ready, Experimental Metal–Organic Frameworks (CoRE‐MOFs) using a topology‐aware machine learning (ML) model that integrates structural, chemical, pore‐size, and topological descriptors. Top‐performing MOFs exhibit aromatic‐enriched cavities and open metal sites that enable π–π and C–H···π interactions, serving as ...
Yu Li, Honglin Li, Jialu Li, Wan‐Lu Li
wiley   +1 more source

Population pharmacokinetic modeling and Monte Carlo simulation to optimize meropenem dosing in patients with severe postoperative infections. [PDF]

Front Pharmacol
Zou Y, Ren J, Lei H, Chen G, Li C, He X, Hu Y, Liu X.   +7 more
europepmc   +1 more source

EMPIRICAL POWER COMPARISON OF NON-NESTED TESTS FOR THE EVM: SOME MONTE CARLO EVIDENCE [PDF]

Recently, Bodla and Bhatti (2007) revisited Davidson and MacKinnon’s (2002) well-known J test and noted that thought the test is simple to compute but lack small sample exact test computation properties. This paper is one of the attempts to compute a new
BHATTI, M.Ishaq, BODLA, Mahmud, A.
core  

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

Novel Allocation Strategies Can Boost Kidney Exchange Programs: A Monte Carlo Simulation. [PDF]

Transpl Int
Klaassen MF, de Klerk M, Baas MC, Bouwsma H, Bungener LB, Christiaans MHL, Dollevoet T, Glorie K, Heidt S, Hemke AC, de Jong MFC, Kal-van Gestel JA, Kho MML, Langereis JD, van der Pant KAMI, Ranzijn CM, Roelen DL, Spierings E, Voorter CEM, van de Wetering J, van Zuilen AD, Roodnat JI, de Weerd AE.   +22 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

Comment on "evaluation of high dose post-dialytic versus daily beta-lactam dosing in hemodialysis patients using Monte Carlo simulation". [PDF]

Int J Clin Pharm
Aljumaa SA, Al Musawa M, El Lababidi R, Abidi E, Ghazi I, El Nekidy WS.   +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

Threshold-Filtered Kinetic Monte Carlo Simulation for Real-Time Simulation and Control of Biomass Fractionation. [PDF]

Ind Eng Chem Res
Kim J, Ryu J, Yang Q, Yoo CG, Kwon JS.
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