Results 81 to 90 of about 77,535,860 (193)

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

open access: yesAdvanced 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   +19 more
wiley   +1 more source

Resistant Starch Dynamics in Whole‐Grain Cereals: How Processing and Macromolecular Interactions Shape Quality and Physiological Responses

open access: yesAgriFood: Journal of Agricultural Products for Food, EarlyView.
This review explores how processing and macromolecular interactions shape resistant starch dynamics in whole‐grain cereals. It highlights how these transformations influence nutritional quality, digestion kinetics, and product properties, offering insights for designing stable health‐oriented whole‐grain foods with optimized functional benefits ...
Yuewen Tan   +4 more
wiley   +1 more source

Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics

open access: yesAdvanced 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   +5 more
wiley   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

open access: yesAdvanced 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 Critical Assessment of Bonding Descriptors for Predicting Materials Properties

open access: yesAdvanced 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

Introduction to compact transformation groups

open access: yes, 1972
Introduction to compact transformation ...
Bredon, Glen E, Bredon, Glen E.
core  

Materials Representation Learning Based on a Material–Motif Network and Heterogeneous Graphs

open access: yesAdvanced Intelligent Discovery, EarlyView.
Structure motifs in materials are used to construct a bipartite material–motif network that links each material to its constituent motifs and establishes connectivity among materials sharing common motifs. Network analysis reveals material clusters associated with different functional applications and supports motif‐guided screening of materials.
Anoj Aryal   +3 more
wiley   +1 more source

A Language‐Guided Multimodal Foundation Model for Zero‐Shot and Multi‐Task Brain Signal Analysis

open access: yesAdvanced Intelligent Systems, EarlyView.
METIS aligns brain signals with natural‐language instructions to enable zero‐shot and multi‐task brain signal analysis. Pretrained on over 70 000 h of EEG and iEEG recordings, it generalizes across sleep stage classification, epilepsy detection, and neurological disorder diagnosis, providing a scalable foundation model for clinically meaningful brain ...
Mingzhi Chen   +3 more
wiley   +1 more source

Coarse‐Grained Simulations Rationalize Thermosensitivity and Hydrolytic Degradation of Vinyl Copolymers Obtained by Radical Ring‐Opening Polymerization

open access: yesAngewandte Chemie, EarlyView.
Coarse‐grained (CG) molecular dynamics simulations were used to predict the upper critical solution temperature of copolymers obtained by radical ring‐opening copolymerization of cyclic ketene acetals and vinyl monomers, and to account for their unique degradation kinetics under physiological conditions by evaluating the solvation of their ester groups.
Ping Gao   +3 more
wiley   +2 more sources

Artificial Intelligence for Advanced Functional Materials: Progress and Emerging Frontiers

open access: yesAdvanced Intelligent Systems, EarlyView.
Artificial intelligence is transforming the discovery of functional materials by linking synthesis, characterization, simulation, and design in unified workflows. Advances in machine learning, autonomous experimentation, and foundation models are accelerating innovation across energy, electronics, and biomedicine, while revealing new frontiers for ...
Cristiano Malica   +38 more
wiley   +1 more source

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