Results 201 to 210 of about 4,786,586 (290)

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

Pelagic sea cucumbers in the Galapagos: Midwater grazers that short-circuit deep-sea carbon flux. [PDF]

Ecology
Robison BH.
europepmc   +1 more source

Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics

Advanced Intelligent Discovery, EarlyView.
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong, Zijian Huang, Xuanwu Wang, Zhenghui Feng, Wei Liu, Bingding Huang   +5 more
wiley   +1 more source

Anthropogenic pollutants' threats to deep-sea ecosystems: Research challenges and technological frontiers. [PDF]

Eco Environ Health
Hou R, Feng JC, Zhang S.
europepmc   +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

Heavy metal contamination and remediation in deep drilling across Chinese energy basins. [PDF]

iScience
Liu Y, Guo W, Wu Y, Guo W.
europepmc   +1 more source

Factorization Machine with Iterative Quantum Reverse Annealing: A Python Package for Batch Black‐Box Optimization With Reverse Quantum Annealing

Advanced Intelligent Discovery, EarlyView.
Factorization machine with iterative quantum reverse annealing (FMIRA) leverages quantum reverse annealing to perform batch black‐box optimization. Factorization machine with quantum annealing (FMQA) is a widely used python package for solving black‐box optimization problems using D‐Wave quantum annealers.
Andrejs Tučs, Ryo Tamura, Koji Tsuda
wiley   +1 more source

Review and Integrated Framework for Deep-Sea Mineral Resources, Ecological Influence, and Future Development. [PDF]

Int J Microbiol
Liu J, Wang C, Etim IN, Zhang R, Sand W, Wang X, Yang L, Ge Y, Liu J.   +8 more
europepmc   +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, Tushar Singh Thakur, Manuel Dillenz, Johan M. Carlsson, Celso R. C. Rêgo, Eibar Flores, Hamidreza Hajiyani, Felix Hanke, Juan María García Lastra, Wolfgang Wenzel, Nicola Marzari, Tejs Vegge, Giovanni Pizzi, Ivano E. Castelli   +13 more
wiley   +1 more source

Growing nickel supply from the tropics threatens priority conservation areas. [PDF]

Nat Ecol Evol
Hyman J, Sonter LJ, McDonald-Madden E, Watson JEM, Mervine EM, Bull JW, Dawson C, Lloyd TJ, Luckeneder S, Maron M, Mendonca Severiano B, Raymond S, Schlacher TA, Sreekar R, Valenta RK, Visconti P, Werner TT, Northey SA.   +17 more
europepmc   +1 more source