Results 201 to 210 of about 1,983,959 (269)

Evolution of noisy learning in games. [PDF]

Proc Natl Acad Sci U S A
Couto MC, Santos FP, Hilbe C.
europepmc   +1 more source

Machine Learning‐Assisted Design and Performance Prediction of a Compact Dual‐Band Polarization‐Insensitive THz Metamaterial Absorber for Skin‐Cancer‐Related Refractive‐Index Sensing

Advanced Electronic Materials, EarlyView.
A compact QASRR‐based THz metamaterial absorber enables polarization‐insensitive dual‐band absorption and skin‐cancer‐related refractive‐index sensing through measurable resonance shifts. Field, surface‐current, and circuit analyses clarify the dual‐resonance mechanism, while StackNet‐assisted prediction accurately estimates the simulated absorption ...
Md. Murad Kabir Nipun, S. M. Rifatur Rana, Md. Golam Morshed, Imrose Jahan, F. A. Sabbir Ahamed, Md. Jahedul Islam   +5 more
wiley   +1 more source

Poly(ADP-ribose) (PAR) exhibits ion-dependent structural properties distinct from RNA. [PDF]

Nucleic Acids Res
Baidya L, Zhang H, Nguyen HT.
europepmc   +1 more source

AI‐Assisted Ultrasensitive Biosensing using Metal‐Electrolyte‐Metal‐Insulator‐Silicon Structure Based on Oxygen‐Tunable Iridium Oxide Nanonets for Lysyl‐Oxidase‐Like‐2 Breast Cancer Detection

Advanced Electronic Materials, EarlyView.
An oxygen‐controlled iridium‐oxide (IrOx) nano‐net structure is developed to enhance the sensitivity of metal‐electrolyte‐metal‐insulator‐silicon (MEMIS) biosensors. Integrated with a deep learning model, this platform achieves a high accuracy of 94.8% for detecting the breast cancer biomarker LOXL2.
Chiao‐Fan Chiu, Pankaj Kumar, Long Nguyen Minh Le, Ping‐Hsuan Wu, Punnatorn Boonyoung, Shen Tingyang, Abhijit Aich, Yi‐Pin Chen, Shih‐Yin Huang, Siddheswar Maikap   +9 more
wiley   +1 more source

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

PHLOWER leverages single-cell multimodal data to infer complex, multi-branching cell differentiation trajectories. [PDF]

Nat Methods
Cheng M, Jansen J, Reimer KC, Grande VP, Nagai JS, Li Z, Kießling P, Grasshoff M, Kuppe C, Schaub MT, Kramann R, Costa IG.   +11 more
europepmc   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

AI-Augmented Multi-Omics for Abiotic Stress Responses: A New Frontier in Plant Hormone Systems Biology. [PDF]

Plants (Basel)
Zhang Y, Cao X, Yang L, Chen J, Hwarari D.   +4 more
europepmc   +1 more source

Toward Knowledge‐Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human–AI Synergy

Advanced Intelligent Discovery, EarlyView.
This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee, Hyeonbin Moon, Junhyeong Lee, Seunghwa Ryu   +3 more
wiley   +1 more source

Integrating Dynamical Systems Modeling with Spatiotemporal scRNA-Seq Data Analysis. [PDF]

Entropy (Basel)
Zhang Z, Sun Y, Peng Q, Li T, Zhou P.
europepmc   +1 more source