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Atomic-scale structure of the glassy Ge2Sb2Te5 phase change material: A quantitative assessment via first-principles molecular dynamics

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Publications associées (38)

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A recipe for cracking the quantum scaling limit with machine learned electron densities

Mario Geiger

A long-standing goal of science is to accurately simulate large molecular systems using quantum mechanics. The poor scaling of current quantum chemistry algorithms on classical computers, however, imposes an effective limit of about a few dozen atoms on tr ...

IOP Publishing Ltd2023

Spectral properties of extended systems from Koopmans-compliant functionals

Riccardo De Gennaro

Electronic-structure simulations have been impacting the study of materials properties thanks to the simplicity of density-functional theory, a method that gives access to the ground state of the system. Although very important, ground-state properties rep ...

EPFL2022

Femtosecond laser-induced modifications and self-organization in complex glass systems

Gözden Torun

Ultrashort laser pulses, i.e., pulses emitted shorter than a picosecond, can tailor material properties by introducing permanent modifications locally in three dimensions. Remarkably, under a certain exposure condition, these modifications are accompanied ...

EPFL2023

Electronic transport in graphene nanoribbon junctions

Kristians Cernevics

Graphene nanoribbons (GNRs) - one-dimensional strips of graphene - share many of the exciting properties of graphene, such as ballistic transport over micron dimensions, strength and flexibility, but more importantly, they exhibit a tunable band gap that d ...

EPFL2022

Neural Network Potentials for Age Hardening Aluminum Alloys

Daniel John Gilles Marchand

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 ...

EPFL2022

Unified theory of atom-centered representations and message-passing machine-learning schemes

Michele Ceriotti, Jigyasa Nigam, Guillaume André Jean Fraux, Sergey Pozdnyakov

Data-driven schemes that associate molecular and crystal structures with their microscopic properties share the need for a concise, effective description of the arrangement of their atomic constituents. Many types of models rely on descriptions of atom-cen ...

AIP Publishing2022

Influence of static correlation on the magnon dynamics of an itinerant ferromagnet with competing exchange interactions: First-principles study of MnBi

Henrik Moodysson Rønnow

We present first-principles calculations of the dynamic susceptibility in strained and doped ferromagnetic MnBi using time-dependent density functional theory. In spite of being a metal, MnBi exhibits signatures of strong correlation and a proper descripti ...

AMER PHYSICAL SOC2022

Spinor GW /Bethe-Salpeter calculations in BerkeleyGW: Implementation, symmetries, benchmarking, and performance

Oleg Yazyev

Computing the GW quasiparticle band structure and Bethe-Salpeter equation (BSE) absorption spectra for materials with spin-orbit coupling have commonly been done by treating GW corrections and spin-orbit coupling (SOC) as separate perturbations to density- ...

AMER PHYSICAL SOC2022

On the durability of surgical masks after simulated handling and wear

Jian Wang, Véronique Michaud, Baris Çaglar, Vincent Marc Varanges, Yann Albert Gérard Lebaupin

After the spread of COVID-19, surgical masks became highly recommended to the public. They tend to be handled and used multiple times, which may impact their performance. To evaluate this risk, surgical masks of Type IIR were submitted to four simulated tr ...

Nature Research2022

Assessing the persistence of chalcogen bonds in solution with neural network potentials

Anne-Clémence Corminboeuf, Rubén Laplaza Solanas, Veronika Juraskova, Frédéric Célerse

Non-covalent bonding patterns are commonly harvested as a design principle in the field of catalysis, supramolecular chemistry, and functional materials to name a few. Yet, their computational description generally neglects finite temperature and environme ...

2022

Effect of ZnO on the reactivity of cementitious systems

Andrea Eloisa Teixeira Pita

One of the biggest driving forces in cement research today is the mitigation of the CO2 emissions caused by its production. A potential solution to reduce the environmental impact is to replace cement with supplementary cementitious materials (SCMs). This ...

EPFL2022

Disclosing the response of the surface electronic structure in SrTiO3 (001) to strain

Jan Hugo Dil

Combining angle-resolved photoemission spectroscopy and density functional theory calculations, we addressed the surface electronic structure of bent SrTiO 3 (STO) (001) wafers. Using a custom-made device, we observe that the low-dimensional states that em ...

2022

Structure determination of an amorphous drug through large-scale NMR predictions

David Lyndon Emsley, Manuel Cordova, Albert Hofstetter, Pierrick Berruyer, Federico Maria Paruzzo, Bruno Simões de Almeida, Martins Balodis

Knowledge of the structure of amorphous solids can direct, for example, the optimization of pharmaceutical formulations, but atomic-level structure determination in amorphous molecular solids has so far not been possible. Solid-state nuclear magnetic reson ...

Nature Research2021

Microscopic picture of paraelectric perovskites from structural prototypes

Nicola Marzari, Giovanni Pizzi, Michele Kotiuga

We highlight with first-principles molecular dynamics the persistence of intrinsic < 111 > Ti off-centerings for BaTiO3 in its cubic paraelectric phase. Intriguingly, these are inconsistent with the Pm (3) over barm space group often used to atomistically ...

AMER PHYSICAL SOC2022

Physics-enhanced machine learning with symmetry-adapted and long-range representations

Andrea Grisafi

Theoretical and computational approaches to the study of materials and molecules have, over the last few decades, progressed at an exponential rate. Yet, the possibility of producing numerical predictions that are on par with experimental measurements is t ...

EPFL2021

Nuclei with Up to A=6 Nucleons with Artificial Neural Network Wave Functions

Giuseppe Carleo

The ground-breaking works of Weinberg have opened the way to calculations of atomic nuclei that are based on systematically improvable Hamiltonians. Solving the associated many-body Schrodinger equation involves non-trivial difficulties, due to the non-per ...

SPRINGER WIEN2022

Beyond potentials: Integrated machine learning models for materials

Michele Ceriotti

Over the past decade, interatomic potentials based on machine learning (ML) techniques have become an indispensable tool in the atomic-scale modeling of materials. Trained on energies and forces obtained from electronic-structure calculations, they inherit ...

SPRINGER HEIDELBERG2022

A general and efficient framework for atomistic machine learning

Félix Benedito Clément Musil

Over the last two decades, many technological and scientific discoveries, ranging from the development of materials for energy conversion and storage through the design of new drugs, have been accelerated by the use of preliminary in silico experiments, to ...

EPFL2021