Concept
Potentiel interatomique
Publications associées (164)
Gaussian Process Regression for Materials and Molecules
Michele Ceriotti, David Mark Wilkins
We provide an introduction to Gaussian process regression (GPR) machinelearning methods in computational materials science and chemistry. The focus of the present review is on the regression of atomistic properties: in particular, on the construction of in ...
AMER CHEMICAL SOC2021Rapid screening of nanopore candidates in nanoporous single-layer graphene for selective separations using molecular visualization and interatomic potentials
Luc Sébastien Bondaz, Rohit Karnik
Nanoporous single-layer graphene is promising as an ideal membrane because of its extreme thinness, chemical resistance, and mechanical strength, provided that selective nanopores are successfully incorporated. However, screening and understanding the tran ...
AIP Publishing2021Uncertainty estimation for molecular dynamics and sampling
Michele Ceriotti, Federico Grasselli, Yongbin Zhuang, Venkat Kapil, Kevin Rossi, Edgar Albert Engel, Giulio Imbalzano
Machine-learning models have emerged as a very effective strategy to sidestep time-consuming electronic-structure calculations, enabling accurate simulations of greater size, time scale, and complexity. Given the interpolative nature of these models, the r ...
2021Universal pair polaritons in a strongly interacting Fermi gas
Jean-Philippe Brantut, Victor Youri Helson, Kevin Etienne Robert Roux, Hideki Konishi
Cavity quantum electrodynamics (QED) manipulates the coupling of light with matter, and allows several emitters to couple coherently with one light mode1. However, even in a many-body system, the light–matter coupling mechanism has so far been restricted t ...
2021Understanding the mechanisms of adhesive wear for heterogeneous materials through MD simulations
Tribology properties emerge from a variety of mechanisms occurring along the sliding surfaces, at different length scales and time scales. Engineering friction and wear laws are mostly empirical, fitted against experimental results and thus only valid on a ...
2021Machine learning for metallurgy I. A neural-network potential for Al-Cu
William Curtin, Albert Johann Glensk, Daniel John Gilles Marchand, Abhinav Jain
High-strength metal alloys achieve their performance via careful control of precipitates and solutes. The nucleation, growth, and kinetics of precipitation, and the resulting mechanical properties, are inherently atomic scale phenomena, particularly during ...
2020