Results 91 to 100 of about 12,481 (260)
Quasi-median hulls in Hamming space are Steiner hulls
Discrete Applied Mathematics, 2009 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hans-Jürgen Bandelt, Arne Röhl
openaire +2 more sources
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
The Interoperability Challenge in DFT Workflows Across Implementations
Advanced Intelligent Discovery, EarlyView.Interoperability and cross‐validation remain major challenges in the computational materials science. In this work, we introduce a common input/output standard that enables internal translation across multiple workflow managers—AiiDA, PerQueue, Pipeline Pilot, and SimStack—while producing results in a unified schema.
Simon K. Steensen +13 more
wiley +1 more source
MATEC Web of Conferences, 2018 The conceptional projecting of new type of marine technique presented by autonomous underwater vehicles based on new type of materials for hard hulls.
Karpachev Aleksandr
doaj +1 more source
Advanced Intelligent Discovery, EarlyView.This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath +4 more
wiley +1 more source
A Critical Assessment of Bonding Descriptors for Predicting Materials Properties
Advanced Intelligent Discovery, EarlyView.The impact of new bonding descriptors in machine learning models for predicting material properties is assessed. Improvements are validated using significance tests, and new, intuitive descriptors for screening lattice thermal conductivity and projected force constants are introduced.
Aakash Ashok Naik +6 more
wiley +1 more source
Animal Production: Indonesian Journal of Animal Production, 2000 An experiment was conducted to investigate the effect of soybean hulls levels substitution in to corn as ration in twenty seven rams. The animal had a mean of live weight of 12.86 ± 1.94 kg.
EA Rimbawanto, N Iriyanti
doaj
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.Quantitative phase maps of single cells recorded in flow cytometry modality feed a hierarchical architecture of machine learning models for the label‐free identification of subtypes of ovarian cancer. The employment of a priori clinical information improves the classification performance, thus emulating the clinical application of liquid biopsy during ...
Daniele Pirone +11 more
wiley +1 more source
Solution Synthesis of Actinide Chalcogenide and Oxychalcogenide Nanoparticles
Angewandte Chemie, EarlyView.We report the synthesis of UOS, UOSe, and US2 nanoparticles under colloidal conditions. The magnetic properties were measured of all three materials and compared to prior studies of UO2 nanoparticles. Nanoparticle phases were determined using powder diffraction, and size by transmission electron microscopy.
Cheyenne Orozco +7 more
wiley +2 more sources