Results 211 to 220 of about 708,503 (346)

Online Health Management for Complex Nonlinear Systems Based on Hidden Semi‐Markov Model Using Sequential Monte Carlo Methods [PDF]

, 2012
Qinming Liu, Ming Dong
openalex   +1 more source

Interpretable Machine Learning for Solvent‐Dependent Carrier Mobility in Solution‐Processed Organic Thin Films

Advanced Intelligent Discovery, EarlyView.
This work establishes a correlation between solvent properties and the charge transport performance of solution‐processed organic thin films through interpretable machine learning. Strong dispersion interactions (δD), moderate hydrogen bonding (δH), closely matching and compatible with the solute (quadruple thiophene), and a small molar volume (MolVol)
Tianhao Tan, Lian Duan, Dong Wang
wiley   +1 more source

Monte Carlo simulations for free energies of hydration: Past to present. [PDF]

J Chem Phys
Jorgensen WL.
europepmc   +1 more source

Rejoinder: A comparison of Monte Carlo methods for computing marginal likelihoods of item response theory models. [PDF]

J Korean Stat Soc, 2019
Liu Y, Hu G, Cao L, Wang X, Chen MH.
europepmc   +1 more source

Integration within the Felsenstein equation for improved Markov chain Monte Carlo methods in population genetics

Proceedings of the National Academy of Sciences of the United States of America, 2007
J. Hey, R. Nielsen
semanticscholar   +1 more source

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

Diffusion of proteins in crowded solutions studied by docking-based modeling. [PDF]

J Chem Phys
Singh A, Kundrotas PJ, Vakser IA.
europepmc   +1 more source

Improved Monte Carlo methods for estimating confidence intervals for eleven commonly used health disparity measures. [PDF]

PLoS One, 2019
Ahn J, Harper S, Yu M, Feuer EJ, Liu B.
europepmc   +1 more source

Monte Carlo and Quasi-Monte Carlo Methods [PDF]

, 2016
openaire   +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