Results 201 to 210 of about 255,160 (310)

Dynamic Shrinkage Priors for Large Time-Varying Parameter Regressions Using Scalable Markov Chain Monte Carlo Methods. [PDF]

Stud Nonlinear Dyn Econom
Hauzenberger N, Huber F, Koop G.
europepmc   +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

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

Designing proteins: Mimicking natural protein sequence heterogeneity. [PDF]

J Chem Phys
Lequerica-Mateos M, Martin J, Onuchic JN, Morcos F, Coluzza I.   +4 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

Direct computations of viscoelastic moduli of biomolecular condensates. [PDF]

J Chem Phys
Cohen SR, Banerjee PR, Pappu RV.
europepmc   +1 more source

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

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

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