Results 221 to 230 of about 53,002 (284)

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

Critical shifts of fluids in shale nanopores under confinement effects using a modified Redlich Kwong equation of state. [PDF]

Sci Rep
Zhou B, Wu X, Li B, Li J.
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

From MAX to MXene: Unveiling Robust Magnetism and Half-Metallicity in Cr₂ZnC and Its Half-Metallic 2D Cr₂C Through Ab-Initio Investigation. [PDF]

Nanomaterials (Basel)
Lokbaichi A, Gueddouh A, Gueribiz D, Rougab M, Lagoun B, Elhamra F, Mahammedi A, Marfoua B.   +7 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

Targeting the TRIM28-EZH2 Protein-Protein Interface With Cysteine-Reactive Covalent Inhibitors: A Computational Blueprint for Cancer Therapy. [PDF]

Chem Biodivers
Kehinde IO, Mzozoyana V, Zamisa SJ, Faya M, Soliman MES.   +4 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

Raloxifene solubility in supercritical CO₂ and correlation of drug solubility via hybrid machine learning and gradient based optimization. [PDF]

Sci Rep
Alotaibi HF, Hsu CY, Sead FF, Yadav A, Jyothi SR, Mishra S, Purohit B, Ashirova A, Khudayberganov I, Chauhan AS.   +9 more
europepmc   +1 more source

Machine Learning‐Assisted Second‐Order Perturbation Theory for Chemical Potential Correction Toward Hubbard U Determination

Advanced Intelligent Discovery, EarlyView.
In this work, the Doubao large language model (LLM) is involved in the formula derivation processes for Hubbard U determination regarding the second‐order perturbations of the chemical potential. The core ML tool is optimized for physical domain knowledge, which is not limited to parameter prediction but rather serves as an interactive physical theory ...
Mingzi Sun, Tong Wu, Baian Chen, Zikang Li, Qiuyang Lu, Lu Lu, Hon Ho Wong, Cheuk Hei Chan, Bolong Huang   +8 more
wiley   +1 more source

Application of the Richards function to serum antibody titration. [PDF]

Front Immunol
Kovács Á, Papp K, Prechl J, Pfeil T.
europepmc   +1 more source