Results 201 to 210 of about 15,996,300 (248)

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

open access: yesAdvanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray   +3 more
wiley   +1 more source

Model-based cardiac field artefact correction for OP-MEG. [PDF]

open access: yesImaging Neurosci (Camb)
Woelk SP   +4 more
europepmc   +1 more source

Overcoming Artificial Structures in Resolution‐Enhanced Hi‐C Data by Signal Decomposition and Multi‐Scale Attention

open access: yesAdvanced Science, EarlyView.
Deep‐learning‐based signal enhancement is an effective way to recover high‐resolution details from a low‐resolution chromatin contact map. However, due to computational challenges, existing methods commonly divide up the contact map into small patches and create artificial discontinuities at patch boundaries.
Qinyao Li   +6 more
wiley   +1 more source

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

open access: yesAdvanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu   +8 more
wiley   +1 more source

Smart Nanotechnologies for Multimodal Neuromodulation and Brain Interfacing

open access: yesAdvanced Science, EarlyView.
Recent advances in smart nanotechnologies are expanding the toolbox for brain interfacing, from wireless neuromodulation and high‐resolution sensing to targeted delivery within the central nervous system. By combining responsive nanomaterials with bioinspired design, these platforms enable multimodal interactions with neurons and glia, while also ...
Tommaso Curiale   +6 more
wiley   +1 more source

Unifying Composition and Process Design: A Heterogeneous Graph Neural Network for Discovering High‐Performance Cu Alloys

open access: yesAdvanced Science, EarlyView.
By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin   +12 more
wiley   +1 more source

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