Results 111 to 120 of about 3,121 (282)

Two‐Dimensional Triferroics: From Fundamental Couplings to Multifunctional Applications

Advanced Science, EarlyView.
This graphic summarizes the three main types of currently reported 2D triferroic couplings. From the structural perspective, existing systems can be broadly classified into two categories, which exhibit distinct symmetry features and coupling behaviors. Beyond the lattice difference, a third type involves the interplay among ferroelectricity, magnetism,
Yang Li, Jialin Gong, Zhiqing Li
wiley   +1 more source

Artificial Intelligence Predictions in Huge Chemical Spaces: Chiroptical Properties of [6]‐helicene Family

Advanced Science, EarlyView.
This study shows that a local, data‐driven AI model can accurately predict diverse optical and chiroptical properties of [6]helicenes using information from close structural neighbours. Combined with genetic algorithms, it enables inverse design for tailored properties, establishing practical structure–property rules for efficient molecular discovery ...
Rafael G. Uceda, Sandra Míguez‐Lago, Carlos M. Cruz, Boris Pérez‐Cañedo, Alfonso Gijón, Luis Álvarez de Cienfuegos, Antonio J. Mota, Delia Miguel, Juan M. Cuerva   +8 more
wiley   +1 more source

Disorder‐Driven Fast Na+ Transport: From Crystalline to Amorphous Networks in the Mixed‐Anion NaTaOxCl6−2x Oxychlorides

Advanced Energy Materials, EarlyView.
Oxygen substitution in NaTaOxCl6‐2x drives structural evolution from isolated [TaCl6]– octahedra, through oxygen‐bridged [Ta2OCl10]2– dimers, toward extended trans‐[TaO2Cl4]3– chain‐like arrangements. At intermediate compositions, zero‐dimensional corner‐sharing motifs are proposed to create a flexible, disordered framework that peaks ionic ...
Justin Leifeld, Abigail C. Parsons, Alexandra Morscher, Xabier Martinez de Irujo‐Labalde, Marvin A. Kraft, Oskar G. Soulas, Bibek Samanta, Wiebke Zielasko, Neelam Yadav, Kyra Strotmann, Niina Jalarvo, Bruno V. M. Rodrigues, Adam Slabon, Philipp Adelhelm, Michael Ryan Hansen, David O. Scanlon, Wolfgang G. Zeier, Alexander G. Squires   +17 more
wiley   +1 more source

Bayesian Optimization Guiding the Experimental Mapping of the Pareto Front of Mechanical and Flame‐Retardant Properties in Polyamide Nanocomposites

Advanced Intelligent Discovery, EarlyView.
Bayesian optimization enabled the design of PA56 system with just 8 wt% additives, achieving limiting oxygen index 30.5%, tensile strength 80.9 MPa, and UL‐94 V‐0 rating. Without prior knowledge, the algorithm uncovered synergistic effects between aluminum diethyl‐phosphinate and nanoclay.
Burcu Ozdemir, Miguel Hernández‐del‐Valle, Christina Schenk, De‐Yi Wang, Maciej Haranczyk   +4 more
wiley   +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

A Bloom filter-based dynamic symmetric searchable encryption scheme over cloud data

Cybersecurity
In this paper, a searchable encryption scheme for cloud data is proposed to address the limitations of existing schemes, which suffer from inefficient index construction and search process, as well as a lack of support for dynamic updates or the ...
Xing Zhang, Haozhe Wang, Junqing Lu, Tianning Li, Changda Wang   +4 more
doaj   +1 more source

A Physics Constrained Machine Learning Pipeline for Young's Modulus Prediction in Multimaterial Hyperelastic Cylinders Guided by Contact Mechanics

Advanced Intelligent Discovery, EarlyView.
A physics‐guided machine learning framework estimates Young's modulus in multilayered multimaterial hyperelastic cylinders using contact mechanics. A semiempirical stiffness law is embedded into a custom neural network, ensuring physically consistent predictions. Validation against experimental and numerical data on C.
Christoforos Rekatsinas, Panagiotis Krokidas, Vasileios Vavourakis, Clara Essmann, George Giannakopoulos   +4 more
wiley   +1 more source

FIRE‐GNN: Force‐Informed, Relaxed Equivariance Graph Neural Network for Rapid and Accurate Prediction of Surface Properties

Advanced Intelligent Discovery, EarlyView.
This study introduces FIRE‐GNN, a force‐informed, relaxed equivariant graph neural network for predicting surface work functions and cleavage energies from slab structures. By incorporating surface‐normal symmetry breaking and machine learning interatomic potential‐derived force information, the approach achieves state‐of‐the‐art accuracy and enables ...
Circe Hsu, Claire Schlesinger, Karan Mudaliar, Jordan Leung, Robin Walters, Peter Schindler   +5 more
wiley   +1 more source

Exploiting Hidden Information Leakages in Backward Privacy for Dynamic Searchable Symmetric Encryption

Applied Sciences
Dynamic searchable symmetric encryption (DSSE) enables searches over encrypted data as well as data dynamics such as flexible data addition and deletion operations.
Hyundo Yoon, Muncheon Yu, Changhee Hahn, Dongyoung Koo, Junbeom Hur   +4 more
doaj   +1 more source

A Unifying Approach to Self‐Organizing Systems Interacting via Conservation Laws

Advanced Intelligent Discovery, EarlyView.
The article develops a unified way to model and analyze self‐organizing systems whose interactions are constrained by conservation laws. It represents physical/biological/engineered networks as graphs and builds projection operators (from incidence/cycle structure) that enforce those constraints and decompose network variables into constrained versus ...
F. Barrows, G. Zhang, S. Anand, Z. Chen, J. Lin, A. Desai, S. Martiniani, F. Caravelli   +7 more
wiley   +1 more source