Publications associées à Unified cluster expansion method applied to the configurational thermodynamics of cubic Ti1-xAlxN

Euclid: Validation of the MontePython forecasting tools

Slobodan Ilic, Georges Meylan, Frédéric Courbin, Julien Lesgourgues, Richard Massey, Maurizio Martinelli, Jean-Luc Starck, Yi Wang

Context. The Euclid mission of the European Space Agency will perform a survey of weak lensing cosmic shear and galaxy clustering in order to constrain cosmological models and fundamental physics. Aims. We expand and adjust the mock Euclid likelihoods of t ...

Edp Sciences S A2024

XXXIV. The effect of linear redshift-space distortions in photometric galaxy clustering and its cross-correlation with cosmic shear

Slobodan Ilic, Malte Tewes, Georges Meylan, Frédéric Courbin, Fabio Finelli, Richard Massey, Maurizio Martinelli, Alessandro Pezzotta, Gianluca Castignani, Marcello Farina, Yi Wang

Context. The cosmological surveys that are planned for the current decade will provide us with unparalleled observations of the distribution of galaxies on cosmic scales, by means of which we can probe the underlying large-scale structure (LSS) of the Univ ...

Edp Sciences S A2024

First-principles thermodynamics of precipitation in aluminum-containing refractory alloys

Anirudh Raju Natarajan

Materials for high -temperature environments are actively being investigated for deployment in aerospace and nuclear applications. This study uses computational approaches to unravel the crystallography and thermodynamics of a promising class of refractory ...

Pergamon-Elsevier Science Ltd2024

Pressure-freezing of dodecane: exploring the crystal structures, formation kinetics and phase diagrams for colossal barocaloric effects in n-alkanes

Tomasz Poreba

Barocaloric (BC) materials provide cheaper and more energy efficient alternatives to traditional refrigerants. Some liquid alkanes were recently shown to exhibit a colossal BC effect, matching the entropy changes in commercial vapour-liquid refrigerants. D ...

Royal Soc Chemistry2023

Cosmic void exclusion models and their impact on the distance scale measurements from large-scale structure

Jean-Paul Richard Kneib, Amélie Tamone, Andrei Variu, Daniel Felipe Forero Sanchez, Cheng Zhao

Baryonic Acoustic Oscillations (BAOs) studies based on the clustering of voids and matter tracers provide important constraints on cosmological parameters related to the expansion of the Universe. However, modelling the void exclusion effect is an importan ...

OXFORD UNIV PRESS2023

Influence of Sphericity on Surface Termination of Icosahedral Colloidal Clusters

Esther Amstad

Colloids self-organize into icosahedral clusters composed of a Mackay core and an anti-Mackay shell under spherical confinement to minimize the free energy. This study explores the variation of surface arrangements of colloids in icosahedral clusters, focu ...

Wiley-V C H Verlag Gmbh2023

Beyond - ?CDM constraints from the full shape clustering measurements from BOSS and eBOSS

Cheng Zhao

We analyse the full shape of anisotropic clustering measurements from the extended Baryon Oscillation Spectroscopic Survey quasar sample together with the combined galaxy sample from the Baryon Oscillation Spectroscopic Survey. We obtain constraints on the ...

OXFORD UNIV PRESS2023

A complete description of thermodynamic stabilities of molecular crystals

Venkat Kapil, Edgar Albert Engel

Predictions of relative stabilities of (competing) molecular crystals are of great technological relevance, most notably for the pharmaceutical industry. However, they present a long-standing challenge for modeling, as often minuscule free energy differenc ...

National Academy of Sciences2022

Tensor network investigation of frustrated Ising models

Jeanne Colbois

In spin systems, geometrical frustration describes the impossibility of minimizing simultaneously all the interactions in a Hamiltonian, often giving rise to macroscopic ground-state degeneracies and emergent low-temperature physics. In this thesis, combin ...

EPFL2022

Predicting hot-electron free energies from ground-state data

Michele Ceriotti, Chiheb Ben Mahmoud, Federico Grasselli

Machine-learning potentials are usually trained on the ground-state, Born-Oppenheimer energy surface, which depends exclusively on the atomic positions and not on the simulation temperature. This disregards the effect of thermally excited electrons, that i ...

AMER PHYSICAL SOC2022

Experimental Method to Distinguish between a Solution and a Suspension

Francesco Stellacci, Quy Ong Khac, Paulo Henrique Jacob Silva, Ting Mao, Xufeng Xu

Dispersion of objects in a fluid phase can be classified as solutions (Gibbs free energy of mixing, Delta G(mix) < 0) or suspensions (Delta G(mix) > 0) depending on their thermodynamic stability. Small objects tend to form solutions, larger ones suspension ...

WILEY2022

Tensor network investigation of the hard-square model

Frédéric Mila, Samuel Louis Nyckees

Using the corner-transfer matrix renormalization group to contract the tensor network that describes its partition function, we investigate the nature of the phase transitions of the hard-square model, one of the exactly solved models of statistical physic ...

AMER PHYSICAL SOC2022

Electric metal contacts to monolayer blue phosphorus: electronic and chemical properties

Pengfei Ou, Xiao Zhou, Cheng Chen

The contact nature when monolayer blue phosphorus (blueP) interfaces with three transition metal electrodes (i. e., Pd, Ir, and Pt) was unraveled by the ab initio density functional theory calculations. Specifically, n-type Schottky contact is observed for ...

ELSEVIER2022

BOSS Correlation Function analysis from the Effective Field Theory of Large-Scale Structure

Cheng Zhao

After calibrating the predictions of the Effective Field Theory of Large-Scale Structure against several sets of simulations, as well as implementing a new method to assert the scale cut of the theory without the use of any simulation, we analyze the Full ...

IOP Publishing Ltd2022

Transferable machine-learning models of complex materials: the case of GaAs

Giulio Imbalzano

Molecular simulations allow to investigate the behaviour of materials at the atomistic level, shedding light on phenomena that cannot be directly observed in experiments. Accurate results can be obtained with ab initio methods, while simulations of large-s ...

EPFL2021

Enhanced Room-Temperature Ionic Conductivity of NaCB11H12 via High-Energy Mechanical Milling

Radovan Cerny, Matteo Brighi, Claudia Esther Avalos, Laura Piveteau

The body-centered cubic (bcc) polymorph of NaCB11H12 has been stabilized at room temperature by highenergy mechanical milling. Temperature-dependent electrochemical impedance spectroscopy shows an optimum at 45-min milling time, leading to an rt conductivi ...

AMER CHEMICAL SOC2021

Repair or Degrade: the Thermodynamic Dilemma of Cellular Protein Quality-Control

Paolo De Los Rios, Bruno Claude Daniel Fauvet, Satyam Tiwari, Mathieu Rebeaud, Pierre Goloubinoff

Life is a non-equilibrium phenomenon. Owing to their high free energy content, the macromolecules of life tend to spontaneously react with ambient oxygen and water and turn into more stable inorganic molecules. A similar thermodynamic picture applies to th ...

FRONTIERS MEDIA SA2021

Atomic subgraphs and the statistical mechanics of networks

Sofia Charlotta Olhede

We develop random graph models where graphs are generated by connecting not only pairs of vertices by edges, but also larger subsets of vertices by copies of small atomic subgraphs of arbitrary topology. This allows for the generation of graphs with extens ...

2021

Prediction of aqueous free energies of solvation using coupled QM and MM explicit solvent simulations

Jeremy Samuel Arey, Daniel David Sadowsky

A method based on molecular dynamics simulations which employ two distinct levels of theory is proposed and tested for the prediction of Gibbs free energies of solvation for non-ionic solutes in water. The method consists of two additive contributions: (i) ...

ROYAL SOC CHEMISTRY2020

Hamiltonian-Reservoir Replica Exchange and Machine Learning Potentials for Computational Organic Chemistry

Anne-Clémence Corminboeuf, Alberto Fabrizio, Benjamin André René Meyer, Raimon Fabregat I De Aguilar-Amat, Daniel Hollas

This work combines a machine learning potential energy function with a modular enhanced sampling scheme to obtain statistically converged thermodynamical properties of flexible medium-size organic molecules at high ab initio level. We offer a modular envir ...

AMER CHEMICAL SOC2020