Results 161 to 170 of about 2,731,437 (281)

Deep learning for stress oriented human activity recognition. [PDF]

Front Digit Health
Hamza M, Uddin N, Anjum G, Anas M, Gabol U, Saddaf Khan N.   +5 more
europepmc   +1 more source

Advancing Electronic Application of Coordination Solids: Enhancing Electron Transport and Device Integration via Surface‐Mounted MOFs (SURMOFs)

Advanced Functional Materials, Volume 36, Issue 43, 29 May 2026.
The layer‐by‐layer (LbL) assembly of coordination solids, enabled by the surface‐mounted metal‐organic framework (SURMOF) platform, is on the cusp of generating the organic counterpart of the epitaxy of inorganics. The programmable and sequential SURMOF protocol, optimized by machine learning (ML), is suited for accessing high‐quality thin films of ...
Zhengtao Xu, Christof Wöll, Stefan Bräse   +2 more
wiley   +1 more source

Diffusion-Shock PDEs for Deep Learning on Position-Orientation Space. [PDF]

J Math Imaging Vis
Sherry FM, Schaefer K, Duits R.
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

Electroactive Metal–Organic Frameworks for Electrocatalysis

Advanced Functional Materials, Volume 36, Issue 43, 29 May 2026.
Electrocatalysis is crucial in sustainable energy conversion as it enables efficient chemical transformations. The review discusses how metal–organic frameworks can revolutionize this field by offering tailorable structures and active site tunability, enabling efficient and selective electrocatalytic processes.
Irena Senkovska, Yutong Luo, Ruyan Zhao, Jianjun Zhang, Mingchao Wang, Xinliang Feng, Eike Brunner, Stefan Kaskel   +7 more
wiley   +1 more source

Monitoring horse behaviour with deep learning models. [PDF]

Vet Q
Giannone C, Maccario C, Dalla Costa E, Atallah E, Bovo M.   +4 more
europepmc   +1 more source

Slight Truncation Changes in Iron Oxide Nanocubes Strongly Affect Their Magnetic Properties

Advanced Functional Materials, EarlyView.
Subtle variations in nanoparticle morphology can lead to significant changes in functional properties. An automated shape‐fitting method captures minor differences in corner truncation between iron oxide nanocubes of similar sizes synthesized under identical conditions, revealing pronounced disparities in their magnetic and hyperthermia behavior ...
Kingsley Poon, Raúl López‐Martín, Verner Håkonsen, Nader Yaacoub, Francisco J. Terán, Alberto López Ortega, Jose A. De Toro, Gurvinder Singh   +7 more
wiley   +1 more source

Deep learning insights into β-lactamase dynamics and resistance evolution. [PDF]

Biochem J
Gu J, Gao L, Chen S, Xu M, Goyal J, Haider R, Attana F, Alzahrani ORA, Bonomo RA, Haider S.   +9 more
europepmc   +1 more source

Predicting Atomic Charges in MOFs by Topological Charge Equilibration

Advanced Functional Materials, Volume 36, Issue 43, 29 May 2026.
An atomic charge prediction method is presented that is able to accurately reproduce ab‐initio‐derived reference charges for a large number of metal–organic frameworks. Based on a topological charge equilibration scheme, static charges that fulfill overall neutrality are quickly generated.
Babak Farhadi Jahromi, Sumukh Shankar Sharadaprasad, Rochus Schmid   +2 more
wiley   +1 more source

Molecular deep learning at the edge of chemical space. [PDF]

Nat Mach Intell
van Tilborg D, Rossen L, Grisoni F.
europepmc   +1 more source