Results 181 to 190 of about 987,581 (290)

Toward Knowledge‐Based Workflows: A Semantic Approach to Atomistic Simulations for Mechanical and Thermodynamic Properties

Advanced Engineering Materials, EarlyView.
Knowledge‐based atomistic workflows are presented for mechanical and thermodynamic properties. By coupling modular simulations with ontology‐aligned metadata and provenance, Fe case studies on elastic behavior, defects, thermal properties, and Hall–Petch strengthening reveal how FAIR, queryable, and reusable simulation data can be generated. Mechanical
Abril Azócar Guzmán, Hoang‐Thien Luu, Sarath Menon, Tilmann Hickel, Nina Merkert, Stefan Sandfeld   +5 more
wiley   +1 more source

Deep learning on protein language model embeddings unlocks accurate prediction of protein solubility. [PDF]

Front Microbiol
Cui J, Jiang Y, Wang J, Jiang H, Fang J, Jiang J, Chen Q, Zhong S, Wang X.   +8 more
europepmc   +1 more source

Molecular Dynamics Studies of Shape Memory Polymers: From Bead–Spring Models to Atomistic Simulations

Advanced Engineering Materials, EarlyView.
Coarse‐grained (left) and atomistic (right) models of the shape memory polymer ESTANE ETE 75DT3 are shown schematically. The two representations bridge molecular detail and mesoscopic description. Both models capture shape memory behavior, linking segmental mobility and conformational relaxation of anisotropic chains to macroscopic recovery, and ...
Fathollah Varnik
wiley   +1 more source

Physics-informed Hamiltonian learning for large-scale optoelectronic property prediction. [PDF]

Nat Commun
Schwade M, Zhang S, Vonhoff F, Delgado FP, Egger DA.   +4 more
europepmc   +1 more source

Nondestructive Testing of Welded Composite Metal Foams

Advanced Engineering Materials, EarlyView.
X‐ray computed tomography (CT) is used to evaluate welded steel–steel composite metal foam (CMF) joints of two density classes. It reports variation in postweld spatial void distribution and correlates it to weld‐induced changes, mechanical performance, and failure within welded CMF panels.
Chinmaya Prerana Inguva, Aman Kaushik, Afsaneh Rabiei   +2 more
wiley   +1 more source

An end-to-end framework for data lineage analysis covering link pattern recognition, fault diagnosis, and early warning. [PDF]

Sci Rep
Hou R, Zhang S, Wang H, Li S, Zhang Y.
europepmc   +1 more source

NanoMOF‐Based Multilevel Anti‐Counterfeiting by a Combination of Visible and Invisible Photoluminescence and Conductivity

Advanced Functional Materials, EarlyView.
This study presents novel anti‐counterfeiting tags with multilevel security features that utilize additional disguise features. They combine luminescent nanosized Ln‐MOFs with conductive polymers to multifunctional mixed‐matrix membranes and powder composites. The materials exhibit visible/NIR emission and matrix‐based conductivity even as black bodies.
Moritz Maxeiner, Ruben Maile, Murat Cuvalli, Andreas Wolf, Ayisha Komal, Robert Oestreich, Christoph Janiak, Karl Mandel, Alexander Knebel, Klaus Müller‐Buschbaum   +9 more
wiley   +1 more source

After-Hours Use of the Electronic Health Record Among Medical and Surgical Specialists After Implementation of a System-Wide Integrated Clinical Information System in Alberta, Canada: Longitudinal Descriptive Study. [PDF]

J Med Internet Res
Pauly RP, Stafinski T, Koosha H, Avdagovska M, Hadizadeh M, Bigam D, Graham T, Kuziemsky C, Kassam N, Menon D.   +9 more
europepmc   +1 more source

All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfOx ReRAM Devices

Advanced Functional Materials, EarlyView.
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone, Victoria Clerico, Wooseok Choi, Tommaso Stecconi, Folkert Horst, Laura Bégon‐Lours, Matteo Galetta, Antonio La Porta, Nikhil Garg, Fabien Alibart, Bert Jan Offrein, Valeria Bragaglia   +11 more
wiley   +1 more source

MuFGPS: enhancing liquid-liquid phase separation protein prediction through multi-level features and ensemble learning. [PDF]

Brief Bioinform
Xian L, Zou Q, Qi R, Niu M, Wang Y.
europepmc   +1 more source