Results 201 to 210 of about 1,407,468 (324)

Monte Carlo and Quasi-Monte Carlo Sampling [PDF]

, 2009
openaire   +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

Performance of Diffusion Monte Carlo Calculations for Predicting the Relative Energies of Quinoidal and Nonquinoidal Species. [PDF]

J Phys Chem Lett
Mauger N, Benali A, Jordan KD.
europepmc   +1 more source

4He/H2binary clusters: A path-integral Monte Carlo study

, 1999
M. C. Gordillo
openalex   +2 more sources

Evaluation of Dose Distributions From Elongated and Curved Brachytherapy Sources Using Conventional Treatment Planning Software and Monte Carlo Methods [PDF]

, 2011
Elizabeth Bannon, Mark J. Rivard
openalex   +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

Feasibility of public CPR training kiosks to increase bystander resuscitation: a Monte Carlo simulation study. [PDF]

Resusc Plus
Ohle R, Savage DW, Roy D, Yadav K, Chhabra S, McIsaac S.   +5 more
europepmc   +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

Quantum annealing enhanced Markov-Chain Monte Carlo. [PDF]

Sci Rep
Arai S, Kadowaki T.
europepmc   +1 more source

MONTE CARLO MODEL OF THE ARGENTINE PROTON THERAPY CENTER FOR THE CALCULATION OF RADIATION PROTECTION QUANTITIES AND ACTIVATION PRODUCTS

Gustavo Santa Cruz
openalex   +1 more source