Results 91 to 100 of about 1,506 (212)
Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez +4 more
wiley +1 more source
Autonomous AI‐Driven Design for Skin Product Formulations
This review presents a comprehensive closed‐loop framework for autonomous skin product formulation design. By integrating artificial intelligence‐driven experiment selection with automated multi‐tiered assays, the approach shifts development from trial‐and‐error to intelligent optimisation.
Yu Zhang +5 more
wiley +1 more source
Generalized Multiscale Finite Element Method for discrete network (graph) models
In this paper, we consider a time-dependent discrete network model with highly varying connectivity. The approximation by time is performed using an implicit scheme. We propose the coarse scale approximation construction of network models based on the Generalized Multiscale Finite Element Method.
openaire +3 more sources
Multimodal Learning with Rashomon Analysis for Battery Discharge Capacity Prediction
Multimodal fusion integrates composition, crystal‐structure, and radial‐distribution descriptors to predict battery discharge capacity. Rashomon analysis across near‐optimal models reveals that explanatory variation is structured rather than arbitrary, separating stable mechanistic signals from model‐contingent attributions and providing a more ...
Jue Gong +4 more
wiley +1 more source
Towards Advanced Intelligent and Perceptive Soft Grippers
Implementing soft yet strong and intelligent soft grippers request innovative and creative solutions in designing soft bodies and seamlessly integrating actuated systems with hierarchical sensing. This review systematically analyses soft grippers with a deep understanding of core components, from fundamental design principles to actuation and sensing ...
Haneul Kim +4 more
wiley +1 more source
This study introduces Cellular Material Network (CM‐Net), a pioneering machine learning architecture integrating physical information, to predict the mechanical properties of cellular materials. Comprehensive validation through simulations and experiments demonstrates its accuracy in predicting nonlinear behaviors, including initial peak compression ...
Sicong Zhou +5 more
wiley +1 more source
Tracheal chambers as a key innovation for high‐frequency emission in bat echolocation
Abstract Key innovations are pivotal for biodiversity and facilitating evolutionary success, enabling organisms' adaptation to various ecological niches through the diversification of phenotypic traits. In mammals, notable adaptations include evolving hypsodonty for grazing on grasses and, for bats, evolving echolocation and wing acquisition.
Nicolas L. M. Brualla +7 more
wiley +1 more source
Structure‐Function Tailoring of Plasmonic Nanomaterials for Thin‐Film Photovoltaics
This review discusses the mechanisms and recent advancements of plasmonics in achieving effective light management to enhance the performance of thin‐film solar cells. It highlights applications in high‐performance perovskite solar cells and future‐oriented tandem solar cells.
Sen Jiang +14 more
wiley +1 more source
Machine Learning Paradigm for Advanced Battery Electrolyte Development
Electrolyte materials determine ion transport kinetics within the bulk and interphases, ultimately influencing the performance of battery systems. As data‐driven paradigms increasingly reshape materials discovery, this review provides an application‐oriented exploration of the intersection between machine learning and electrolyte science. By evaluating
Chang Su +4 more
wiley +1 more source
This review elucidates the velocity–dispersion–attenuation coupling mechanisms of wave propagation in rock masses, compares six representative models, and reveals how pressure, temperature, mineral composition, and anisotropy jointly control dynamic responses in complex geological media.
Jiajun Shu +8 more
wiley +1 more source

