Publications associées à Structure nucléaire

Higher-order moments of the elliptic flow distribution in PbPb collisions at √sNN=5.02 TeV

Matthias Finger, Konstantin Androsov, Qian Wang, Jan Steggemann, Anna Mascellani, Yiming Li, Varun Sharma, Xin Chen, Rakesh Chawla, Matteo Galli, Jian Wang, João Miguel das Neves Duarte, Tagir Aushev, Matthias Wolf, Yi Zhang, Tian Cheng, Yixing Chen, Werner Lustermann, Andromachi Tsirou, Alexis Kalogeropoulos, Andrea Rizzi, Ioannis Papadopoulos, Paolo Ronchese, Hua Zhang, Leonardo Cristella, Siyuan Wang, Tao Huang, David Vannerom, Michele Bianco, Sebastiana Gianì, Sun Hee Kim, Davide Di Croce, Kun Shi, Abhisek Datta, Jian Zhao, Federica Legger, Gabriele Grosso, Ji Hyun Kim, Donghyun Kim, Zheng Wang, Sanjeev Kumar, Wei Li, , , , , , , , , , , , , , , , , , , , , , , , , , ,

The role of Zn in C-S-H atomic structure, nucleation and growth

Anna Morales Melgares

The most promising solution towards cementitious materials with a lower carbon footprint is the partial substitution of the clinker by supplementary cementitious materials (SCMs) such as fly ash, blast furnace slag, limestone and calcined clays. The produc ...

EPFL2023

Advancing Computational Chemistry with Stochastic and Artificial Intelligence Approaches

Justin Villard

Computational chemistry aims to simulate reactions and molecular properties at the atomic scale, advancing the design of novel compounds and materials with economic, environmental, and societal implications. However, the field relies on approximate quantum ...

EPFL2023

Search for long-lasting electronic coherence using on-the-fly ab initio semiclassical dynamics

Jiri Vanicek, Alan Scheidegger, Nikolay Golubev

Using a combination of high-level ab initio electronic structure methods with efficient on-the-fly semiclassical evaluation of nuclear dynamics, we performed a massive scan of small polyatomic molecules searching for a long-lasting oscillatory dynamics of ...

AIP Publishing2022

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

Hidden-nucleons neural-network quantum states for the nuclear many-body problem

Giuseppe Carleo

We generalize the hidden-fermion family of neural network quantum states to encompass both continuous and discrete degrees of freedom and solve the nuclear many-body Schrodinger equation in a systematically improvable fashion. We demonstrate that adding hi ...

AMER PHYSICAL SOC2022

The Atomic-Level Structure of Cementitious Calcium Aluminate Silicate Hydrate Determined by NMR

David Lyndon Emsley, Pinelopi Moutzouri

We review our recent paper which resolves the long-standing dilemma of the location and nature of the six-fold coordinated aluminum in calcium aluminate silicate hydrate (C-A-S-H) samples. First principles calculations predict that at high Ca:Si and H2O ra ...

2021

Variational Monte Carlo Calculations of A

Giuseppe Carleo

The complexity of many-body quantum wave functions is a central aspect of several fields of physics and chemistry where nonperturbative interactions are prominent. Artificial neural networks (ANNs) have proven to be a flexible tool to approximate quantum m ...

AMER PHYSICAL SOC2021

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

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

David Lyndon Emsley, Pierrick Berruyer, Albert Hofstetter, Manuel Cordova, 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

Incompleteness of Atomic Structure Representations

Michele Ceriotti, Sergey Pozdnyakov, Michael John Willatt

Many-body descriptors are widely used to represent atomic environments in the construction of machine-learned interatomic potentials and more broadly for fitting, classification, and embedding tasks on atomic structures. There is a widespread belief in the ...

AMER PHYSICAL SOC2020

Magnetic exchange interactions in monolayer CrI3 from many-body wavefunction calculations

Oleg Yazyev, Ravi Yadav, Michele Pizzochero

The marked interplay between the crystalline, electronic, and magnetic structure of atomically thin magnets has been regarded as the key feature for designing next-generation magneto-optoelectronic devices. In this respect, a detailed understanding of the ...

IOP PUBLISHING LTD2020

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

Even-odd conductance effect in graphene nanoribbons induced by edge functionalization with aromatic molecules: basis for novel chemosensors

Oleg Yazyev, Michele Pizzochero, Kristians Cernevics

We theoretically investigate the electron transport in armchair and zigzag graphene nanoribbons (GNRs) chemically functionalized withp-polyphenyl and polyacene groups of increasing length. Our nearest-neighbor tight-binding calculations indicate that, depe ...

2020

Ab initio thermodynamics of liquid and solid water

Michele Ceriotti, Bingqing Cheng, Edgar Albert Engel

Thermodynamic properties of liquid water as well as hexagonal (Ih) and cubic (Ic) ice are predicted based on density functional theory at the hybrid-functional level, rigorously taking into account quantum nuclear motion, anharmonic fluctuations, and proto ...

National Academy of Sciences2019

Atom-density representations for machine learning

Michele Ceriotti, Félix Benedito Clément Musil, Michael John Willatt

This has led to a proliferation of alternative ways to convert an atomic structure into an input for a machine-learning model. We introduce an abstract definition of chemical environments that is based on a smoothed atomic density, using a bra-ket notation ...

2019

Identifying substitutional oxygen as a prolific point defect in monolayer transition metal dichalcogenides

Oleg Yazyev, Artem Pulkin

Chalcogen vacancies are generally considered to be the most common point defects in transition metal dichalcogenide (TMD) semiconductors because of their low formation energy in vacuum and their frequent observation in transmission electron microscopy stud ...

Springer2019

Picture of the wet electron: a localized transient state in liquid water

Alfredo Pasquarello, Francesco Ambrosio, Michele Pizzochero

A transient state of the excess electron in liquid water preceding the development of the solvation shell, the so-called wet electron, has been invoked to explain spectroscopic observations, but its binding energy and atomic structure have remained highly ...

2019

Gate-tunable Scanning Tunneling Microscope as a tool for performing single-molecule chemistry

Shai Mangel

The atomic level understanding and control of the electron transport has important advantages for many applications, such as molecular electronics, solar cells and sensors. The target of my Ph.D. work is to investigate electronic and chemical properties of ...

EPFL2019

Advanced Computational Methods for NMR Crystallography

Albert Hofstetter

Knowledge of the atomic-level structure is key to understanding and predicting properties of materials. X-ray diffraction (XRD) is the methods of choice for structures containing well-defined long-range order. However, many materials contain various degree ...

EPFL2019