Publications related to Universal machine learning for the response of atomistic systems to external fields

Physics of optically driven nanojunctions

Recent years have seen spectacular developments in the domain of nano-optics. Alongside the well-known techniques of super-resolution microscopy progress in nanofabrication has enabled important improvements in the fields of optical imaging and spectros ...

EPFL2020

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

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

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 ...

2020

Electric Field Analysis and Measurement for a Resistively Loaded Monocone

Tingyong Jiang

A novel resistively loaded monocone was proposed to extend the application for electric field calibration. Electric field analysis and measurement were carried out to verify the design of the proposed monocone. As revealed by the results, the S-11 of the m ...

IEEE2020

Instantaneous radiation from time-varying electric and magnetic dipoles

Romain Christophe Rémy Fleury, Mohammad Sajjad Mirmoosa

Radiation from magnetic and electric dipole moments is a key subject in the theory of electrodynamics. Although people treat the problem thoroughly in the context of the frequency domain, the problem is still not well understood in the context of the time ...

2020

Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems

Michele Ceriotti, Fabio Pietrucci

Machine learning encompasses tools and algorithms that are now becoming popular in almost all scientific and technological fields. This is true for molecular dynamics as well, where machine learning offers promises of extracting valuable information from t ...

AMER CHEMICAL SOC2020

Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems

Michele Ceriotti, Fabio Pietrucci

Machine learning encompasses tools and algorithms that are now becoming popular in almost all scientific and technological fields. This is true for molecular dynamics as well, where machine learning offers promises of extracting valuable information from t ...

2020

Direct currents stimulate carbonate mineralization for soil improvement under various chemical conditions

Lyesse Laloui, Dimitrios Terzis, Patrick Gabriel Yihsiu-Ming Hicher

The present study integrates direct electric currents into traditional calcium carbonate mineralization to investigate electrochemical interactions and the subsequent crystalline growth of CaCO3 bonds in sand. A specific line of focus refers to the effect ...

2020

Improved measurement of electric fields by nanobeam precession electron diffraction

Jean-Luc Rouvière

The electric field in a silicon p-n junction has been measured using pixelated scanning transmission electron microscopy. By using a convergence angle of 3.2mrad, a spatial resolution better than 1nm can be achieved leading to a rigid shift of the transmit ...

2020

Charge fluctuations from molecular simulations in the constant-potential ensemble

Sara Bonella, Alessandro Coretti

We revisit the statistical mechanics of charge fluctuations in capacitors. In constant-potential classical molecular simulations, the atomic charges of electrode atoms are treated as additional degrees of freedom which evolve in time so as to satisfy the c ...

2020

Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles

Michele Ceriotti, David Mark Wilkins, Max David Veit, Yang Yang

The molecular dipole moment (mu) is a central quantity in chemistry. It is essential in predicting infrared and sum-frequency generation spectra as well as induction and long-range electrostatic interactions. Furthermore, it can be extracted directly-via t ...

AMER INST PHYSICS2020

Universal machine learning for the response of atomistic systems to external fields

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Physics of optically driven nanojunctions

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

Electric Field Analysis and Measurement for a Resistively Loaded Monocone

Instantaneous radiation from time-varying electric and magnetic dipoles

Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems

Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems

Direct currents stimulate carbonate mineralization for soil improvement under various chemical conditions

Improved measurement of electric fields by nanobeam precession electron diffraction

Charge fluctuations from molecular simulations in the constant-potential ensemble

Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles

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

Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems

Instantaneous radiation from time-varying electric and magnetic dipoles

Physics of optically driven nanojunctions

Electric Field Analysis and Measurement for a Resistively Loaded Monocone

Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems

Direct currents stimulate carbonate mineralization for soil improvement under various chemical conditions

Improved measurement of electric fields by nanobeam precession electron diffraction

Charge fluctuations from molecular simulations in the constant-potential ensemble

Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles