Results 131 to 140 of about 418,083 (279)
This paper proposes a novel control framework to ensure safety of a robotic swarm. A feedback optimization controller is capable of driving the swarm toward a target density while keeping risk‐zone exposure below a safety threshold. Theory and experiments show how safety is more effectively achieved for sparsely connected swarms.
Longchen Niu, Gennaro Notomista
wiley +1 more source
This study refines the Crystal Hamiltonian Graph Network to predict energies, structures, and lithium‐ion dynamics in halide electrolytes. By generating ordered structural models and using an iterative fine‐tuning workflow, we achieve near‐ab initio accuracy for phase stability and ionic transport predictions.
Jonas Böhm, Aurélie Champagne
wiley +1 more source
Physics‐Informed Neural Networks (PINNs) provide a framework for integrating physical laws with data. However, their application to Prognostics and Health Management (PHM) remains constrained by the limited uncertainty quantification (UQ) capabilities.
Ibai Ramirez +4 more
wiley +1 more source
C1 non-integrability of a hydrogen atom in a circularly polarized microwave field
Guirao Juan, López Miguel, Vera Juan
doaj +1 more source
A Negatively Curved Pyrene‐Fused Azaacene
A pyrene‐fused azaacene derivative is reported in which steric overcrowding caused by eight phenyl substituents induces bending of the aromatic framework rather than twisting. ABSTRACT Non‐planar aromatic hydrocarbons display distorted π‐frameworks that give rise to unique optoelectronic properties.
Marco Carini +3 more
wiley +1 more source
The Progress of Orbitronics: The Enhancement of Orbital Torque Efficiency
Orbit‐torque (OT) devices attract significant attention for their low‐power consumption and high stability in applications. This review systematically outlines strategies for enhancing OT efficiency. We categorize approaches into boosting orbital currents/torques and improving the orbital‐to‐spin conversion coefficient.
Pengfei Liu +6 more
wiley +1 more source
A Hybrid Semi‐Inverse Variational and Machine Learning Approach for the Schrödinger Equation
A hybrid semi‐inverse variational and machine‐learning framework is presented for solving the Schrödinger equation with complex quantum potentials. Physics‐based variational solutions generate high‐quality training data, enabling Random Forest and Neural Network models to deliver near‐perfect energy predictions.
Khalid Reggab +5 more
wiley +1 more source
Data-driven approach to the deep learning of the dynamics of a non-integrable Hamiltonian system. [PDF]
Doria Rosales E, Carbone V, Lepreti F.
europepmc +1 more source
Interfacial Ru–O–W Orbital Coupling Enables Lattice Oxygen Stabilization for Enhanced Acidic OER
A RuO2/WO3 electrocatalyst with strong interfacial Ru–O–W bonds exhibits optimized Ru–O interactions, enhancing intrinsic activity and stability for acidic OER. The WO3 support modulates the electronic structure of RuO2 and promotes oxo‐intermediate deprotonation, delivering a low overpotential of 203 mV at 10 mA cm−2 and sustained operation beyond 200
Tongzhou Wang +9 more
wiley +1 more source
A Se‐mediated Co dual‐atom catalyst replaces sluggish oxygen evolution with efficient iodide oxidation in zinc‐air/iodide hybrid batteries. Se‐induced d‐p orbital hybridization optimizes adsorption, enabling a low 0.365 V voltage gap and superior durability, fundamentally overcoming conventional kinetic bottlenecks.
Huaipeng Pang +6 more
wiley +1 more source

