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Publications associées (30)

Passive Perching with Energy Storage for Winged Aerial Robots

Dario Floreano, Yegor Piskarev, William John Stewart, Luca Guarino

Perching in unmanned aerial vehicles (UAVs) offers the possibility of extending the range of aerial robots beyond the limits of their batteries. It has been a topic of intense study for multirotor UAVs. Perching in winged UAVs is harder because a kinetic e ...

2021

Impact of quantum-chemical metrics on the machine learning prediction of electron density

Anne-Clémence Corminboeuf, Ksenia Briling, Alberto Fabrizio

Machine learning (ML) algorithms have undergone an explosive development impacting every aspect of computational chemistry. To obtain reliable predictions, one needs to maintain a proper balance between the black-box nature of ML frameworks and the physics ...

AMER INST PHYSICS2021

Importance of Nuclear Quantum Effects for NMR Crystallography

Michele Ceriotti, Venkat Kapil, Edgar Albert Engel

The resolving power of solid-state nuclear magnetic resonance (NMR) crystallography depends heavily on the accuracy of computational predictions of NMR chemical shieldings of candidate structures, which are usually taken to be local minima in the potential ...

AMER CHEMICAL SOC2021

Trends in the Extremes of Environments Associated with Severe US Thunderstorms

Anthony Christopher Davison, Erwan Fabrice Koch, Jonathan Koh Boon Han

Severe thunderstorms can have devastating impacts. Concurrently high values of convective available potential energy (CAPE) and storm relative helicity (SRH) are known to be conducive to severe weather, so high values of PROD - (CAPE)(1/2) x SRH have been ...

AMER METEOROLOGICAL SOC2021

Negative capacitance semiconductor sensor

Mihai Adrian Ionescu, Ali Saeidi, Francesco Bellando

The present invention relates to a semiconductor sensor (1) comprising: a source element (17, 19); a drain element (21, 23); a semiconductor channel element (25) between the source element (17, 19) and the drain element (21, 23) for forming an electrically ...

2020

Experimental verification of X-point potential well formation in unfavorable magnetic field direction

Basil Duval, Holger Reimerdes, Christian Gabriel Theiler, Joaquim Loizu Cisquella, Marco Wischmeier, Mirko Wensing, Cedric Kar-Wai Tsui, Claudia Colandrea, Hugo De Oliveira, Sophie Danielle Angelica Gorno

Recent TCV experiments confirm the predicted formation of an electric potential well, below the magnetic X-point, in configurations with unfavorable

B_t

field direction (ion

\nabla B

drift away from the divertor), that substantially reshapes the typica ...

Elsevier2020

Spin-Dependent Thermoelectric Transport in Cobalt-Based Heusler Alloys

Haiming Yu, Simon Granville, Junfeng Hu

Sitting at the intersection of spintronics and thermoelectricity, research investigating the coupling of thermoelectric, magnetic, and electrical transport properties in materials has recently found that the ferromagnetic Heusler alloys are the ideal testb ...

WILEY-V C H VERLAG GMBH2020

X-point potential well formation in diverted tokamaks with unfavorable magnetic field direction

Basil Duval, Holger Reimerdes, Joaquim Loizu Cisquella, Marco Wischmeier, Mirko Wensing

Fluid simulations of the boundary of fusion plasmas predict the formation of an electric potential well in the vicinity of the X-point in detached divertor conditions with Bt in the unfavorable direction for H-mode access. This potential well arises when t ...

IOP Publishing Ltd2020

Highly spatially resolved mapping of the piezoelectric potentials in InGaN quantum well structures by off-axis electron holography

Victor Carle Adrien Boureau

The internal fields in 2.2nm thick InGaN quantum wells in a GaN LED structure have been investigated by using aberration-corrected off-axis electron holography with a spatial resolution of better than 1nm. To improve the spatial resolution, different types ...

2020

Machine-Learning of Atomic-Scale Properties Based on Physical Principles

Michele Ceriotti, Michael John Willatt

We briefly summarize the kernel regression approach, as used recently in materials modelling, to fitting functions, particularly potential energy surfaces, and highlight how the linear algebra framework can be used to both predict and train from linear fun ...

Springer International Publishing2020

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