Results 151 to 160 of about 523,565 (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

Comparative Efficacy of SGLT2 Inhibitors in MASLD: Bayesian Network Meta-Analysis of CAP-LSM Outcomes and Time Effects. [PDF]

JGH Open
Gomez DP, Hababag WF, Ong-Ramos CC.
europepmc   +1 more source

Late-Time Resolution of the Hubble Tension in CPL Cosmology with Massive Neutrinos via Bayesian Physics-Informed Neural Networks

Muhammad Yarahmadi, A. Salehi
openalex   +1 more source

Compromise-free Bayesian neural networks [PDF]

, 2020
Kamran Javid, Will Handley, M. P. Hobson, A. Lasenby   +3 more
openalex   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

Study on the driving mechanism of cultivated land change in the urban-rural fringe with Bayesian network modeling. [PDF]

Sci Rep
Wang J, Zhu Z, Chen M, Zhang Y.
europepmc   +1 more source

The Relation Between Acausality and Interference in Quantum-Like\n Bayesian Networks [PDF]

, 2015
Catarina Moreira, Andreas Wichert
openalex   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

The Bias-and-Expertise Model: A Bayesian Network Model of Political Source Characteristics. [PDF]

Cogn Sci
Young DJ, de-Wit LH.
europepmc   +1 more source

Preoperative endometrial cancer risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model: an ENITEC development and external validation study

, 2020
Casper Reijnen, Evangelia Gogou, Nicole C.M. Visser, Hilde Engerud, Jordache Ramjith, Louis J.M. van der Putten, K. van der Vijver, Marı́a Santacana, Peter Bronsert, Johan Bulten, Marc Hirschfeld, Eva Colás, Antonio Gil‐Moreno, Armando Reques, Gemma Mancebo, Camilla Krakstad, Jone Trovik, Ingfrid S. Haldorsen, Jutta Huvila, Martin Koskas, Vít Weinberger, Markéta Bednaříková, Jitka Hausnerová, A.A.M. van der Wurff, Xavier Matías‐Guiu, Frédéric Amant, Heidi V.N. Küsters‐Vandevelde, L.F.A.G. Massuger, Peter Lucas, Johanna M.A. Pijnenborg   +29 more
openalex   +1 more source