Results 211 to 220 of about 11,554,034 (251)
Compositional neurosymbolic representations enable efficient active exploration. [PDF]
Furlong PM +4 more
europepmc +1 more source
Resistive memory devices are explored for operation at extremely low temperatures relevant to quantum computing. The study reveals how transistor behavior strongly influences memory performance under cryogenic conditions and introduces an optimized programming strategy.
Emilio Pérez‐Bosch Quesada +11 more
wiley +1 more source
Linear Residual Network Modeling for Anti-HIV-1 Activity Prediction and Docking-Validated Design of Biphenyl-DAPY-Based NNRTIs. [PDF]
Wang H, Zhang Y, Wang A, Zhang P.
europepmc +1 more source
In this work, low‐resolution infrared imaging is combined with a 28 nm FeFET IMC architecture to enable compact, energy‐efficient edge inference. MLC FeFET devices are experimentally characterized, and controlled multi‐level current accumulation is validated at crossbar array level.
Alptekin Vardar +9 more
wiley +1 more source
Forest Kernel Balancing Weights: Outcome-Guided Features for Causal Inference. [PDF]
Shen AA +4 more
europepmc +1 more source
A closed‐loop, data‐driven approach facilitates the exploration of high‐performance Si─Ge─Sn alloys as promising fast‐charging battery anodes. Autonomous electrochemical experimentation using a scanning droplet cell is combined with real‐time optimization to efficiently navigate composition space.
Alexey Sanin +7 more
wiley +1 more source
Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories
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 +4 more
wiley +1 more source
ReBaCCA-ss: Relevance-Balanced Continuum Correlation Analysis With Smoothing and Surrogating for Quantifying Similarity Between Population Spiking Activities. [PDF]
Zhang X, Xu C, Lu Z, Wang H, Song D.
europepmc +1 more source
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 +19 more
wiley +1 more source
Enhancing genomic prediction of wheat resistance to fusarium head blight through integration of passive resistance traits. [PDF]
Gorash A, Syed S, Brazauskas G.
europepmc +1 more source

