Results 121 to 130 of about 11,175 (238)

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

Advanced Intelligent Discovery, EarlyView.
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

A Robust Deep Temporal Causal Discovery Platform for Single‐Cell Gene Regulatory Network Reconstruction

Advanced Intelligent Discovery, EarlyView.
scTIGER2.0 is a deep‐learning framework that infers gene regulatory networks from single‐cell RNA sequencing data. By integrating correlation, pseudotime ordering, deep learning and bootstrap‐based significance testing, it reduces false positives and reveals directional gene interactions.
Nishi Gupta, Madison Dautle, Shaoqiang Zhang, Yong Chen   +3 more
wiley   +1 more source

Catalyst Hide‐and‐Seek Beneath Porous Support Surfaces: Pinpointing Active Site Distribution Through Resonance Energy Transfer

Angewandte Chemie, EarlyView.
Like a shark playing hide‐and‐seek in a coral reef, energy transfer (ET) finds hidden “fish” catalysts in the porous support “reef.” ET allows catalytic species to be precisely mapped, revealing whether they reside near the surface, deep in the pores, or are uniformly distributed. This approach ensures controlled catalyst distribution, highlighting how
Buddhima K. P. Maldeni Kankanamalage, William J. Thompson, Danielle N. Smith, Grace C. Thaggard, Namodhi Wijerathne, Isabella Incognito, Jeffery A. Byers, Jia Niu, Natalia B. Shustova   +8 more
wiley   +2 more sources

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

Advanced Intelligent Discovery, EarlyView.
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley   +1 more source

Evidence of introgression amid phylogenetic conflict in Brachyotum, a plant radiation from the Tropical Andes. [PDF]

Am J Bot
Paredes-Burneo D, Bedoya AM, Michelangeli FA, Majure LC, Ulloa Ulloa C, Lagomarsino LP.   +5 more
europepmc   +1 more source

Accelerating Discovery of Organic Molecular Crystals via Materials Informatics and Autonomous Experiments

Advanced Intelligent Discovery, EarlyView.
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi, Kazuki Ishizaki, Ryo Fukasawa   +2 more
wiley   +1 more source

Differentiable centerline-aware framework for aneurysm neck delineation in volumetric angiography. [PDF]

NPJ Digit Med
Liu X, Zhou J, Zhang H, Tao B, Xu S.
europepmc   +1 more source

Hypertopologies, functional differential equations and jointly continuous utility functions

, 2006
In the setting of Functional Differential Equations, the topology tau_B, a finer one than the Attouch - Wets, was introduced to obtain existence and continuous dependence results. The topology tau_B found many applications also in Mathematical Economics.
CEPPITELLI, Rita
core  

Investigation of Analog Memristor Characteristics for Hardware Synaptic Weight in Multilayer Neural Network

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
The systematic design of memristor‐based neural network is provided by analog conductance state parameters to accurately emulate the software‐based high‐resolution weight at discrete device level. The requirement of discrete analog conductance of memristor device is measured as ≈50 states with nonlinearity value of ≈0.142 within the deviation range of ...
Jingon Jang, Yoonseok Song, Sungjun Park
wiley   +1 more source