Results 161 to 170 of about 56,006,406 (235)
Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen +4 more
wiley +1 more source
On Low-Rank Multiplicity-Free Complex Fusion Categories. [PDF]
Vercleyen G, Slingerland J.
europepmc +1 more source
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
An explanation of the number of peaks and symmetries of starbursts. [PDF]
Barbero S, Delgado AM, Fernández L.
europepmc +1 more source
Composite cathode degradation is not merely the sum of its particles. Kinetically mismatched populations develop state‐of‐charge differences that drive spontaneous internal Li‐ion transfer; the accompanying transient currents accelerate surface degradation.
Seheon Oh +5 more
wiley +1 more source
Reducing Complexity in Muscle-Tendon Kinematics Parameterization Improves Convergence Speed in Musculoskeletal Simulations. [PDF]
Harba M, Badia J, Serrancolí G.
europepmc +1 more source
The influence of different electrolytes on Lithium–sulfur batteries is investigated. Multimodal operando analysis, including Raman, UV–vis, and Impedance spectroscopy is used to study degradation mechanisms. The solubility of polysulfides in the electrolyte is crucial for battery capacity and lifetime.
Oliver Löhmann +3 more
wiley +1 more source
privateST: a feasible framework for privacy-preserving spatial transcriptomics prediction from histopathology images. [PDF]
Kim H, Kim M, Han B.
europepmc +1 more source
Farmers' Preferences for Gene Editing Crops and Influencing Factors
ABSTRACT Gene editing (GE) is gaining momentum worldwide, but limited data on UK farmers' preferences hinders our understanding of its potential impact amid deregulation debates. Based on a survey of 200 English arable farmers, we employ a Latent Class Analysis and Multinomial Logit regressions to investigate current preferences for GE crops.
Bertolozzi‐Caredio Daniele +1 more
wiley +1 more source

