Results 91 to 100 of about 14,571 (201)

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

Mechanistic study of <i>TFE3</i> breakage in <i>TFE3</i>-rearranged renal cell carcinoma: the perspective of non-canonical DNA structures and their stability. [PDF]

Front Genet
Zhang X, Dang Q, Pan X, Deng Z, Xu Y, Gan W, Guo H.   +6 more
europepmc   +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

Interfacial Stability and Design Strategies for Halide Solid Electrolytes in High-Voltage All-Solid-State Sodium-Ion Batteries. [PDF]

Small Methods
Jang M, Kwon E, Jeon C, Kim S, Yu S.
europepmc   +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

Autonomous thermodynamically informed database generation for machine-learned interatomic potentials and application to magnesium. [PDF]

NPJ Comput Mater
Fletcher VG, Bartók AP, Pártay LB.
europepmc   +1 more source

Advancements in Graphdiyne‐Based Multiscale Catalysts for Green Hydrogen Energy Conversion

Advanced Intelligent Discovery, EarlyView.
This review systematically explores the fundamental characteristics of graphdiyne (GDY), cutting‐edge field of GDY‐based multiscale catalysts within sustainable energy conversion systems.Special emphasis is placed on the structure‒property relationships in different reactions.
Qian Xiao, Lu Qi, Siao Chen, Yurui Xue
wiley   +1 more source

Comparative Assessment of Statistical and Thermodynamic Prediction Methods for Solvate Formation: A Case Study with Curcumin and Its Derivatives. [PDF]

Cryst Growth Des
Ticona-Chambi J, Choquesillo-Lazarte D, Cuffini SL, Infantes L.   +3 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

Thermodynamic dissipation constrains metabolic versatility of unicellular growth. [PDF]

Nat Commun
Cossetto T, Rodenfels J, Sartori P.
europepmc   +1 more source